Hasil untuk "Balance of trade"

H. Grubel

L. Lynd, J. Cushman, R. Nichols et al.

E. Milner‐Gulland, E. Bennett

Connie A. Van der Byl, Natalie Slawinski

Hao Chen, David A Bullock, J. Alonso et al.

Plants often live in adverse environmental conditions and are exposed to various stresses, such as heat, cold, heavy metals, salt, radiation, poor lighting, nutrient deficiency, drought, or flooding. To adapt to unfavorable environments, plants have evolved specialized molecular mechanisms that serve to balance the trade-off between abiotic stress responses and growth. These mechanisms enable plants to continue to develop and reproduce even under adverse conditions. Ethylene, as a key growth regulator, is leveraged by plants to mitigate the negative effects of some of these stresses on plant development and growth. By cooperating with other hormones, such as jasmonic acid (JA), abscisic acid (ABA), brassinosteroids (BR), auxin, gibberellic acid (GA), salicylic acid (SA), and cytokinin (CK), ethylene triggers defense and survival mechanisms thereby coordinating plant growth and development in response to abiotic stresses. This review describes the crosstalk between ethylene and other plant hormones in tipping the balance between plant growth and abiotic stress responses.

N. Arivazhagan, Colin Cherry, Wolfgang Macherey et al.

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simultaneous systems must carefully schedule their reading of the source sentence to balance quality against latency. We present the first simultaneous translation system to learn an adaptive schedule jointly with a neural machine translation (NMT) model that attends over all source tokens read thus far. We do so by introducing Monotonic Infinite Lookback (MILk) attention, which maintains both a hard, monotonic attention head to schedule the reading of the source sentence, and a soft attention head that extends from the monotonic head back to the beginning of the source. We show that MILk’s adaptive schedule allows it to arrive at latency-quality trade-offs that are favorable to those of a recently proposed wait-k strategy for many latency values.

V. Nair, A. Mohanathan, M. Herzog et al.

<p>In this work, SynRad, a new radar forward operator for the Active Tracer High-Resolution Atmospheric Model (ATHAM) volcanic plume model is introduced. The operator is designed to generate synthetic radar signals from ground-based scanning weather radars for volcanic ash clouds simulated by ATHAM. A key novelty of SynRad is a ray-tracing module that traces radar beams from the antenna to the ash cloud and calculates path attenuation due to hydrometeors and ash. The operator is designed to be compatible with the one-moment microphysics scheme in ATHAM, but it can easily be extended to other one- or two-moment schemes in ATHAM or any weather prediction model. The operator can be used to test candidate locations at which to operationally deploy portable high-frequency or multi-frequency (from long to short wavelength) scanning radar(s). An optimal frequency or frequencies (for a multi-frequency radar) can be identified that balance the trade-off between a stronger return signal and the increased path attenuation that comes at these higher frequencies. A case study of the eruption of the Raikoke volcano in 2019 is used to evaluate the performance of SynRad. The measurement process of a C-band radar is simulated using SynRad, and the operator was able to generate realistic fields of the equivalent radar reflectivities, echo tops, and vertical maximum intensities. Even though higher-frequency microwave weather radars (K-band and higher) have been used to observe volcanic activity, they may not operate in scanning mode. Ideally, higher-frequency microwave radars will be designed and constructed specifically for monitoring volcanic eruptions. This is certainly possible in the coming years, making feasibility studies on the capability of higher-frequency radars timely.</p>

Yohanes Hermanto Sirait, Arie Afriansyah, Tran Viet Dung

The intersection between trade and environmental issues has become an unavoidable reality in the contemporary global landscape. The WTO, as the foremost global trade body, integrates elements of sustainable development across several of its legal frameworks. This study seeks to explore the presence and evolution of environmental measures within the framework of the WTO. This research further examines the practical recognition of environmental measures within international trade contexts. Through normative research and comparative case studies of state disputes in WTO’ Dispute Settlement Body, this study identifies a growing trend in the use of environmental measures within WTO legal frameworks. The findings indicate that environmental measures possess a degree of legitimacy and are generally well-received by the WTO dispute settlement mechanism. Ambitious emission targets set by developed countries have contributed to the development of novel types of environmental measures. This indicates that the interconnection between trade and environmental concerns will continue to strengthen over time. Hence, ensuring a well-calibrated balance between trade obligations and environmental considerations requires the panel or Appellate Body to proceed with heightened prudence..

Xinyu ZHU, Haorui CUI, Yang SONG

As a crucial component of a high-speed rail traction power supply system, the catenary system is responsible for transmitting electrical energy to electric multiple units (EMUs). In practice, continuous impacts from pantograph-net interactions and external environmental factors can lead to defects in the catenary’s supporting parts, such as looseness, detachment, fracture, and cracking. These issues compromise the reliability of the catenary structure and pose risks to its stable operation. Therefore, timely and accurate positioning of the catenary support components (CSCs) is vital for ensuring the safe operation of high-speed rails and improving the catenary maintenance strategies. In 2012, the former Ministry of Railways of China (now the China Railway Corporation) officially promulgated the General Technical Specifications for High-speed Railway Power Supply Safety Detection and Monitoring System. This study marked a shift from traditional manual inspection methods to intelligent non-contact catenary detection and maintenance using computer vision technology. This study addresses challenges in detection systems by focusing on “catenary part positioning” in the whole detection process from the perspective of the functional integrity of the detection system. Detecting CSCs is challenging because of the variety of parts, scale differences, and small size of components. To overcome these challenges, this study proposes a catenary component detection algorithm that utilizes a multiscale fusion pyramid focus network. This approach integrates a balance module and a feature pyramid module to improve the detection performance of small targets. The separable residual pyramid aggregation module (SRPAM) was designed to optimize multi-scale feature extraction, expand the receptive field, and address multi-scale issues in CSC detection. Furthermore, a path aggregation network based on the equilibrium feature pyramid (PA-BFPN) was designed to improve cross-layer feature fusion efficiency and small object detection performance. Finally, the effectiveness of the proposed method is demonstrated through comparative experiments, visual analysis of the results, multi-scale feature fusion module experiments, feature pyramid network experiments, and ablation studies. The results demonstrate that the proposed multiscale feature pyramid FCOS (MFP-FCOS) algorithm offers excellent overall performance compared to many classical algorithms. Visualization experiments confirm its effectiveness in detecting targets across different scales and effectively solving small-scale and multi-scale sample detection challenges. The proposed SPRAM effectively mitigates information loss and improves feature extraction performance, whereas the proposed PA-BFPN obtains more comprehensive feature information. In summary, the proposed MFP-FCOS achieved a detection accuracy (mAP) of 48.6% on the CSC dataset with 30 floating point operations per second (FLOPs), indicating a balanced trade-off between detection accuracy and detection speed.

Xinyuan Zhang, Yang Zhang, Zihan Li et al.

Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research.

Yifan Xue, Yao Wang, Jiang Shi et al.

Understanding species-specific mechanisms governing symbiotic fungal responses to plant traits and soil factors is critical for optimizing urban tree “plant-soil-fungus” systems under pollution stress. To address this gap, we combined δ¹³C/δ¹⁵N isotope analysis and ITS sequencing for three common street trees in Beijing: <i>Sophora japonica</i>, <i>Ginkgo biloba</i>, and <i>Populus tomentosa</i>. In <i>S. japonica</i>, symbiotic fungal abundance was positively associated with leaf δ¹⁵N, indicating root exudate-mediated “plant-microbe” interactions during atmospheric NO_x assimilation. <i>G. biloba</i>, with weak NO_x assimilation, exhibited a negative correlation between fungal abundance and soil available N/P, suggesting mycorrhizal nutrient compensation under low fertility. <i>P. tomentosa</i> showed decreased fungal abundance with increasing soil N/P ratios and specific leaf area, reflecting carbon allocation trade-offs that limit mycorrhizal investment. These results demonstrate that symbiotic fungi respond to atmospheric and edaphic drivers in a tree species-dependent manner. Urban greening strategies should prioritize <i>S. japonica</i> for its NO_x mitigation potential and optimize fertilization for <i>G. biloba</i> (nutrient-sensitive fungi) and <i>P. tomentosa</i> (nutrient balance sensitivity). Strategic mixed planting of <i>P. tomentosa</i> with <i>S. japonica</i> could synergistically enhance ecosystem services through complementary resource acquisition patterns. This study provides mechanism-based strategies for optimizing urban tree management under atmospheric pollution stress.

Muhammad Sukri Bin Ramli

We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.

Anahid Kiani, S. Mahdi Fazeli, G. Reza Jafari

Classical Heider balance theory models the evolution of social networks towards balanced states with stress minimization. Triad relationships are classically either balanced or imbalanced. However, real-world relationships often exhibit uncertainty, complexity, and interconnected dynamics that transcend this classical framework, and we will sometimes see the synchronicity of balance and imbalance states. When these triadics are simultaneously balanced and imbalanced, they form superposition and entanglement states that necessitate the introduction of quantum balance. This study introduces a framework that extends classical Heider balance theory from social network analysis by incorporating principles of quantum mechanics. In the framework, each triad is a quantum state of spin systems embedded in various network topologies. Using a quantum transformation operator, we investigate the emergence of balanced and imbalanced states and identify ground and steady states. In the quantum state, developments towards balance will occur by introducing the Hamiltonian within the creation and annihilation operators framework. In these developments, we witness the emergence of superposition and entanglement quantum states with no classical equivalent, which form an uncertainty in the states. When the quantum evolution is a functional temperature, we investigate the emergence of balanced and imbalanced states, analyze ground and steady states, and examine phase transitions as a function of temperature. Our results reveal novel dynamical behaviors unique to quantum social systems and offer a new perspective on collective decision-making, conflict resolution, and emergent order in complex networks.

Jędrzej Maskiewicz, Paweł Sakowski

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Yifu Gao, Zhijia Zhang, Xin Zhao et al.

The attainment of carbon neutrality requires the development of aqueous energy conversion and storage devices. However, these devices exhibit limited performance due to the permeability–selectivity trade-off of permselective membranes as core components. Herein, we report the application of a synergistic approach utilizing two-dimensional nanoribbons-entangled nanosheets to rationally balance the permeability and selectivity in permselective membranes. The nanoribbons and nanosheets can be self-assembled into a nanofluidic membrane with a distinctive “island-bridge” configuration, where the nanosheets serve as isolated islands offering adequate ionic selectivity owing to their high surface charge density, meanwhile bridge-like nanoribbons with low surface charge density but high aspect ratio remarkably enhance the membrane’s permeability and water stability, as verified by molecular simulations and experimental investigations. Using this approach, we developed a high-performance graphene oxide (GO) nanosheet/GO nanoribbon (GONR) nanofluidic membrane and achieved an ultrahigh power density of 18.1 W m–2 in a natural seawater|river water osmotic power generator, along with a high Coulombic efficiency and an extended lifespan in zinc metal batteries. The validity of our island-bridge structural design is also demonstrated for other nanosheet/nanoribbon composite membranes, providing a promising path for developing reliable aqueous energy conversion and storage devices.

Steve Goodacre, Laura Sutton, Kate Ennis et al.

Background Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department. Objectives To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment. Design Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness. Setting Two ambulance services and four acute hospitals in England. Participants Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded. Interventions Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation. Main outcome measures Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained. Results Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed. Limitations We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling. Conclusions No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis. Study registration This study is registered as Research Registry (reference: researchregistry5268). Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information. Plain language summary Sepsis is a life-threatening condition in which an abnormal response to infection causes heart, lung or kidney failure. People with sepsis need urgent treatment. They need to be prioritised at the emergency department rather than waiting in the queue. Paramedics attempt to identify people with possible sepsis using an early warning score (based on simple measurements, such as blood pressure and heart rate) alongside their impression of the patient’s diagnosis. They can then alert the hospital to assess the patient quickly. However, an inaccurate early warning score might miss cases of sepsis or unnecessarily prioritise people without sepsis. We aimed to measure how accurately early warning scores identified people with sepsis when used alongside paramedic diagnostic impression. We collected data from 71,204 people that two ambulance services transported to four different hospitals in 2019. We recorded paramedic diagnostic impressions and calculated early warning scores for each patient. At one hospital, we linked ambulance records to hospital records and identified who had sepsis. We then calculated the accuracy of using the scores alongside diagnostic impression to diagnose sepsis. Finally, we used modelling to predict how many patients (with and without sepsis) paramedics would prioritise using different strategies based on early warning scores and diagnostic impression. We found that none of the currently available early warning scores were ideal. When they were applied to all patients, they prioritised too many people. When they were only applied to patients whom the paramedics thought had infection, they missed many cases of sepsis. The NEWS2, score, which ambulance services already use, was as good as or better than all the other scores we studied. We found that using the NEWS2, score in people with a paramedic impression of infection could achieve a reasonable balance between prioritising too many patients and avoiding missing patients with sepsis. Scientific summary Background Sepsis is a life-threatening reaction to an infection in which the immune system overreacts to infection and causes organ damage. Early recognition and treatment of sepsis has the potential to reduce mortality. Guidelines for sepsis highlight the importance of early recognition and treatment, with treatment recommended within 1 hour of presentation for those at highest risk. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores to identify people with a high risk of sepsis and then pre-alert the emergency department (ED) or provide the patient with prehospital treatment. However, they need to determine which score to use, the threshold of positivity for the score, and whether to apply the early warning score to all medical cases or just those where the paramedic diagnostic impression suggests sepsis, infection or a non-specific presentation. This requires estimates of the diagnostic accuracy of early warning scores and consideration of the balance between sensitivity (avoiding missing sepsis) and specificity (prioritising too many patients who do not have sepsis). Objectives We aimed to determine the accuracy, impact and cost-effectiveness of prehospital early warning scores for adults with suspected sepsis. Our specific objectives were: to estimate the accuracy of prehospital early warning scores for identifying sepsis requiring time-critical treatment in adults with possible sepsis who are attended by emergency ambulance to estimate the impact of using prehospital early warning scores to guide key prehospital decisions, in terms of the operational consequences, and the cost-effectiveness of alternative strategies. Methods We undertook (1) a retrospective cohort study to estimate the accuracy of prehospital early warning scores alongside paramedic diagnostic impression and (2) decision-analytic modelling of the operational consequences and cost-effectiveness of using prioritisation strategies based on early warning score and diagnostic impression. Retrospective cohort study We used a literature review and expert opinion to identify 21 early warning scores for evaluation. We used routine ambulance service data to identify all episodes in 2019 in which two ambulance services (Yorkshire and West Midlands) transported patients with medical presentations to four acute hospitals (Sheffield Northern General Hospital, Doncaster Royal Infirmary, Rotherham General Hospital, University Hospitals Coventry and Warwickshire). We excluded episodes with injury, mental health problems, cardiac arrest or direct transfer to specialist services, and cases with no vital signs recorded. We calculated early warning scores from the first recorded vital signs on the ambulance service electronic patient-report form and categorised the paramedic diagnostic impression as sepsis, infection, non-specific presentation or other specific presentation. We then determined the number of cases that ambulance services would prioritise at each hospital using each early warning score alongside the categorised paramedic diagnostic impression. We planned to use the National Health Service (NHS) Digital Data Access Request Service to link ambulance service to hospital data but NHS Digital were unable to provide this service. We therefore instituted a rescue plan to link ambulance service to hospital data at one participating hospital (Sheffield) to determine whether patients had a reference standard diagnosis of sepsis, adjudicated by two independent clinicians following hospital record review. The primary reference standard consisted of meeting the sepsis-3 definition [evidence of infection with a change of two or more points in the Sequential (sepsis-related) Organ Failure Assessment (SOFA) score] and receiving treatment for sepsis. The secondary reference standard consisted of meeting the sepsis-3 definition alone. We analysed the ambulance service data descriptively to report the mean daily number of cases that the ambulance service would pre-alert to each hospital for each combination of early warning score and diagnostic impression. For the accuracy analysis, we constructed receiver operating characteristic (ROC) curves to evaluate sensitivity and specificity over the range of each score. We calculated the area under the ROC curve, sensitivities, specificities, and positive and negative predictive values at key cut-points, each with a 95% confidence interval (CI). Reporting of the results highlights sensitivity and positive predictive value as these best indicate under-triage (sensitivity, the proportion of sepsis cases prioritised) and overtriage (positive predictive value, the proportion of prioritised cases with sepsis). To select strategies for the decision-analytic modelling, we calculated the proportion of mean daily ambulance arrivals that would be prioritised at each hospital and excluded strategies that would prioritise a potentially unmanageable proportion (> 10%). We then compared the accuracy of strategies and excluded those with sensitivity and specificity both inferior to another strategy. We also excluded strategies that were not clearly superior to a comparable strategy involving the National Early Warning Score, version 2 (NEWS2) on the basis that NEWS2 is already widely used by NHS ambulance services, whereas other strategies would require additional training and support to implement. Decision-analytic modelling We developed a decision-analytic model to evaluate the consequences to healthcare providers and the cost-effectives for the health services of using 23 different strategies to prioritise patients transported to hospital with possible sepsis. Due to the paucity of data associated with the benefit of early treatment for sepsis and conflicting results from studies where data existed, threshold analyses were independently undertaken to estimate the reduction in mortality, the reduction in general ward length of stay (LoS) and the reduction in intensive care unit LoS that would be required by each strategy in order to be cost-effective compared with a strategy of no prioritisation of patients. We additionally present the number of prehospital alerts associated with each strategy, the number of patients with sepsis who have been correctly prioritised and the number of patients with sepsis who are not prioritised. Results Retrospective cohort study We collected data from 95,022 ambulance episodes involving 71,204 patients with median age 66 years, and included 37,588 (53.0%) women, and 40,045 (94.9%) with white ethnicity. The mean (standard deviation) number of daily attendances meeting the study inclusion criteria was 93.5 (14.7) at Sheffield Northern General Hospital, 59.5 (10.8) at Doncaster Royal Infirmary, 51.3 (8.9) at Rotherham General Hospital and 74 (11) at University Hospitals Coventry and Warwickshire. Most early warning scores operating at their pre-specified thresholds would prioritise fewer than 10% of attendances when applied only to those with a diagnostic impression of sepsis or infection, but would prioritise more than 10% when applied to non-specific attendances or all attendances. The exceptions were qSOFA (threshold > 1), the Screening to Enhance PrehoSpital Identification of Sepsis (SEPSIS) score, the Critical Illness Score (CIS; threshold > 4), the Paramedic Initiated Treatment of Sepsis Targeting Out-of-hospital Patients clinical trial rule, the PRESS score and the sepsis alert criteria. Yorkshire Ambulance Service recorded only one diagnostic impression, whereas West Midlands Ambulance Service recorded multiple unranked impressions, so strategies prioritised a greater proportion of patients transported to University Hospitals Coventry and Warwickshire. Consequently, in the West Midlands most strategies prioritised more than 10% of cases when applied to those with a diagnostic impression of infection or sepsis. We linked 12,870 out of 24,955 (51.6%) cases to the Sheffield Northern General Hospital records at Sheffield and identified 348 (2.7%) with the primary reference standard. The sensitivity and positive predictive value of paramedic diagnostic impression were 0.328 (95% CI 0.28 to 0.379) and 0.285 (95% CI 0.243 to 0.331) for sepsis, 0.572 (95% CI 0.519 to 0.623) and 0.156 (95% CI 0.137 to 0.176) for infection or sepsis, and 0.897 (95% CI 0.86 to 0.924) and 0.053 (95% CI 0.048 to 0.059) for non-specific presentation, infection or sepsis. The early warning scores had a greater area under the ROC curve when applied to all cases rather than alongside diagnostic impression, but the low prevalence of the reference standard meant that thresholds with sensitivity above 0.7 generally had positive predictive value below 0.15, which would prioritise an unmanageable number of cases. When higher thresholds were used to provide acceptable positive predictive value and a manageable number of cases, strategies that applied the early warning score only to those with a diagnostic impression of sepsis or infection tended to have better overall accuracy. NEWS2 had the highest area under the ROC curve when applied only to those with a paramedic diagnostic impression of sepsis or infection (0.756, 95% CI 0.729 to 0.783) or sepsis alone (0.655, 95% CI 0.63 to 0.68). Only the SEPSIS score had a higher area under the ROC curve than NEWS2 when applied to non-specific presentation, infection or sepsis (0.862 vs. 0.858) and all cases (0.882 vs. 0.877). None of the strategies provided high sensitivity (e.g. > 0.8) with acceptable positive predictive value (e.g. > 0.15). NEWS2, using varying thresholds and combinations with diagnostic impression, provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. We identified strategies reflecting published recommendations for prioritisation that could offer options with varying trade-offs between sensitivity and positive predictive value. Applying NEWS2 only to those with a paramedic diagnostic impression of sepsis or infection respectively provided sensitivities and positive predictive values of 0.522 (95% CI 0.469 to 0.574) and 0.216 (95% CI 0.189 to 0.245) with a threshold > 4, 0.447 (95% CI 0.395 to 0.499) and 0.274 (95% CI 0.239 to 0.313) with a threshold > 6, and 0.314 (95% CI 0.268 to 0.365) and 0.333 (95% CI 0.284 to 0.386) with a threshold > 8. Applying qSOFA > 1 only to those with a paramedic diagnostic impression of sepsis or infection provided sensitivity of 0.305 (95% CI 0.259 to 0.355) and positive predictive value of 0.356 (95% CI 0.304 to 0.412). Decision-analytic modelling The modelling provided estimates for a range of strategies with varying sensitivity and specificity of the number of cases (overall and with sepsis) that would be prioritised in a large and a small hospital. At a large hospital receiving 93.5 eligible cases per day, applying NEWS2 > 4 only to those with a diagnostic impression of infection or sepsis would prioritise 6.10 cases per day, including 1.32 with sepsis, while failing to prioritise 1.21 with sepsis. The corresponding numbers using NEWS2 > 6 were 4.11, 1.13 and 1.40, using NEWS2 > 8 were 2.38, 0.79 and 1.73, and using qSOFA > 1 were 2.17, 0.77 and 1.76. At a small hospital receiving 53.1 eligible cases per day, applying NEWS2 > 4 only to those with a diagnostic impression of infection or sepsis would prioritise 3.35 cases per day, including 0.72 with sepsis, while failing to prioritise 0.66 with sepsis. The corresponding numbers using NEWS2 > 6 were 2.26, 0.62 and 0.77, using NEWS2 > 8 were 1.31, 0.44 and 0.95, and using qSOFA > 1 were 1.19, 0.42 and 0.96. The threshold analysis showed that the relative risk of mortality associated with prioritisation at which each strategy would be cost-effective compared to no prioritisation [assuming willingness to pay £20,000 per quality-adjusted life-year (QALY) gained] ranged from 0.977 applying NEWS2 > 0 only to those with a diagnostic impression of infection or sepsis, to 0.996 applying NEWS2 > 11 only to those with a diagnostic impression of sepsis. The comparable ranges for other measures of effectiveness for these two strategies were: increase in QALYs 0.00056–0.00002; reduction in length of ward stay 3.8–0.7 days; reduction in intensive care LoS 1.2–0.2 days. Conclusions We were unable to identify a strategy that would prioritise a substantial majority of patients with sepsis without prioritising a potentially unmanageable number of patients for the ED. Most early warning scores, used at a recommended threshold, are likely to prioritise an unmanageable number of cases unless they are only used to prioritise cases with a paramedic diagnostic impression of infection or sepsis. However, paramedic diagnostic impression of infection or sepsis only identified 57% of cases with a reference standard diagnosis of sepsis requiring urgent treatment. The NEWS2 provides sensitivity and specificity for identifying sepsis that is generally similar or superior to other scores operating at a comparable threshold. We therefore found no evidence to justify the support and training required to implement an alternative strategy to NEWS2, which is already widely used in NHS ambulance services. National Early Warning Score, version 2, could be used at thresholds of > 4 or > 6 in presentations with a diagnostic impression of infection or sepsis, reflecting the Academy of Medical Royal Colleges clinical decision support framework, or > 8 to provide similar sensitivity and specificity to the use of qSOFA > 1 recommended in the sepsis-3 guidelines. These strategies provide a range of options that ambulance services and hospitals could use, depending upon capacity to manage prioritised cases and what prioritisation involves. Health economic modelling suggests that sensitive strategies for identifying patients with possible sepsis for prioritisation could be cost-effective, if we are convinced that reducing treatment delay reduces mortality and the emergency care system has the capacity to deliver meaningful prioritisation to substantial numbers of cases. Limitations Inability of NHS Digital to link ambulance service to hospital data meant that we were only able to estimate the accuracy of early warning scores at one hospital using data from 51.6% of the eligible population for whom the ambulance service had NHS numbers. The included patients were markedly older than the excluded patients. We were unable to identify reliable evidence to estimate the effectiveness of early treatment for sepsis, so were unable to identify the most cost-effective strategy. Future research Research into prehospital early warning scores for sepsis is limited by our current inability to clinically measure the dysregulated host response that characterises sepsis and uncertain estimates of the benefits of early treatment. We therefore need to prioritise research to develop better ways of defining and diagnosing sepsis, and to develop and evaluate effective early treatment for sepsis. Future research involving routine ambulance service and hospital data requires a system for NHS data management that supports health data science. Study registration This study is registered as Research Registry (reference: researchregistry5268). Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

Rocco Duvenhage, Kyle Oerder, Keagan van den Heuvel

Quantum detailed balance is formulated in terms of elementary transitions, in close analogy to detailed balance in a classical Markov chain on a finite set of points. An elementary transition is taken to be a pure state of two copies of the quantum system, as a quantum analogue of an ordered pair of classical points representing a classical transition from the first to the second point. This form of quantum detailed balance is shown to be equivalent to standard quantum detailed balance with respect to a reversing operation, thus providing a new conceptual foundation for the latter. Aspects of parity in quantum detailed balance are clarified in the process. The connection with the Accardi-Cecchini dual and the KMS dual (or Petz recovery map) is also elucidated.

A. Gruslys, R. Munos, Ivo Danihelka et al.

We propose a novel approach to reduce memory consumption of the backpropagation through time (BPTT) algorithm when training recurrent neural networks (RNNs). Our approach uses dynamic programming to balance a trade-off between caching of intermediate results and recomputation. The algorithm is capable of tightly fitting within almost any user-set memory budget while finding an optimal execution policy minimizing the computational cost. Computational devices have limited memory capacity and maximizing a computational performance given a fixed memory budget is a practical use-case. We provide asymptotic computational upper bounds for various regimes. The algorithm is particularly effective for long sequences. For sequences of length 1000, our algorithm saves 95\% of memory usage while using only one third more time per iteration than the standard BPTT.

Mohsen Bahmani‐Oskooee, Hadiseh Fariditavana

Emily Crawford Das, Jingzhi Tie, Qing Zhang

This paper is concerned with an optimal strategy for simultaneously trading a pair of stocks. The idea of pairs trading is to monitor their price movements and compare their relative strength over time. A pairs trade is triggered by the divergence of their prices and consists of a pair of positions to short the strong stock and to long the weak one. Such a strategy bets on the reversal of their price strengths. A round-trip trading strategy refers to opening and closing such a pair of security positions. Typical pairs-trading models usually assume a difference of the stock prices satisfies a mean-reversion equation. However, we consider the optimal pairs-trading problem by allowing the stock prices to follow general geometric Brownian motions. The objective is to trade the pairs over time to maximize an overall return with a fixed commission cost for each transaction. Initially, we allow the initial pairs position to be either long or flat. We then consider the problem when the initial pairs position may be long, flat, or short. In each case, the optimal policy is characterized by threshold curves obtained by solving the associated HJB equations.