Efficiency Metrics Research Articles

Evaluation of Post-weaning Efficiency in Nellore-Angus Crossbred Steers through Model Predicted Residual Consumption

The objective of this work was to evaluate efficiency traits of Nellore-Angus crossbred steers (n = 349) on feed. Steers were fed a grain-based diet beginning at approximately 12 months of age for an average of 140 days. Contemporary groups were born in the fall or spring of 2003 through 2007 in full-sibling embryo transfer families or half-sibling families all sired by the same bulls. Individual intake was measured and weights were recorded to permit calculation of average daily gain. Residual feed intake (RFI) was estimated as the residual of models employing regressions on metabolic mid-test weight and ADG. An additional efficiency metric was also constructed and evaluated: model predicted residual consumption (MPRC). Mixed linear models were used to analyze daily dry matter intake, average daily gain (ADG), metabolic mid-test weight, RFI, and MPRC. Large positive associations of DMI with MPRC and RFI were identified along with low positive associations between metabolic mid-weight with ADG and MPRC. Genome wide association analysis revealed 5 regions associated with DMI, but none for the other traits analyzed. Residual feed intake values varied greatly between the contemporary group value and the overall value for the steers, showing the calculation’s dependency on the reference population. However, MPRC as based upon a standardized population, did not fluctuate. More selection phenotypes and strategies are needed for large-scale improvements in global beef cattle production sustainability. The stability of the MPRC metric could be beneficial for future feed efficiency research across multiple and diverse contemporary groups, and diverse production environments.

Fairness-aware scheduling of dynamic cross-job coflows in shared datacenters based on meta learning

In today’s shared datacenters, communications among tasks across jobs (applications) typically generate large amounts of coflows. System efficiency and job fairness oriented coflow scheduling is critical for improving both system performance and user satisfaction in the application level. Since the metrics of fairness and efficiency usually conflict to each other, how to schedule coflows at the desired tradeoff between the metrics is a challenging problem. Due to the great variety of jobs and tasks within each job, communications among them have different characteristics, resulting their coflows presented in different patterns. Existing coflow schedulers attempt to optimize either only one metric or a tradeoff of both for static patterns of coflows (within the same job) because of their incapability of capturing the inherent patterns of coflows that are dynamically changing specially for cross-job coflows. This paper proposes a novel fairness-aware coflow scheduling algorithm that combines a link-embedded multi-layer neural network with a meta-learning framework for unsupervised learning of dynamic coflows across different jobs and hence adaptive scheduling of the coflows to achieve the desired fairness–efficiency tradeoff. In our algorithm, while the neural network takes care of mining the relationships among coflow allocations, the meta-learning framework effectively captures the dynamic patterns of a large number of sample coflows and trains the neural network to achieve the desired scheduling performance. Extensive experimental results demonstrate that our algorithm outperforms the state-of-the-art coflow schedulers on scheduling performance for achieving the desired fairness–efficiency tradeoff.

Efficient coding in the economics of human brain connectomics.

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Cardiopulmonary, metabolic, and perceptual responses during exercise in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): A Multi-site Clinical Assessment of ME/CFS (MCAM) sub-study.

Cardiopulmonary exercise testing has demonstrated clinical utility in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). However, to what extent exercise responses are independent of, or confounded by, aerobic fitness remains unclear. To characterize and compare exercise responses in ME/CFS and controls with and without matching for aerobic fitness. As part of the Multi-site Clinical Assessment of ME/CFS (MCAM) study, 403 participants (n = 214 ME/CFS; n = 189 controls), across six ME/CFS clinics, completed ramped cycle ergometry to volitional exhaustion. Metabolic, heart rate (HR), and ratings of perceived exertion (RPE) were measured. Ventilatory equivalent ([Formula: see text], [Formula: see text]), metrics of ventilatory efficiency, and chronotropic incompetence (CI) were calculated. Exercise variables were compared using Hedges' g effect size with 95% confidence intervals. Differences in cardiopulmonary and perceptual features during exercise were analyzed using linear mixed effects models with repeated measures for relative exercise intensity (20-100% peak [Formula: see text]). Subgroup analyses were conducted for 198 participants (99 ME/CFS; 99 controls) matched for age (±5 years) and peak [Formula: see text] (~1 ml/kg/min-1). Ninety percent of tests (n = 194 ME/CFS, n = 169 controls) met standard criteria for peak effort. ME/CFS responses during exercise (20-100% peak [Formula: see text]) were significantly lower for ventilation, breathing frequency, HR, measures of efficiency, and CI and significantly higher for [Formula: see text], [Formula: see text] and RPE (p<0.05adjusted). For the fitness-matched subgroup, differences remained for breathing frequency, [Formula: see text], [Formula: see text], and RPE (p<0.05adjusted), and higher tidal volumes were identified for ME/CFS (p<0.05adjusted). Exercise responses at the gas exchange threshold, peak, and for measures of ventilatory efficiency (e.g., [Formula: see text]) were generally reflective of those seen throughout exercise (i.e., 20-100%). Compared to fitness-matched controls, cardiopulmonary responses to exercise in ME/CFS are characterized by inefficient exercise ventilation and augmented perception of effort. These data highlight the importance of distinguishing confounding fitness effects to identify responses that may be more specifically associated with ME/CFS.

APE: Metrics for understanding application performance efficiency under power caps

As supercomputers continue to grow in size and power consumption, the ability to understand application run time performance and energy/power usage is of growing importance. In the future it may be necessary for systems to operate under power caps imposed by facilities due to external influences such as renewable power generation (e.g. solar) impacting energy availability at an affordable cost during certain times of day and more frequent natural disasters due to climate change causing power transmission disruptions. However, current energy consumption and power characterization metrics like energy-delay products do not express all of the characteristics of an application that are relevant to understanding the impact of power capping on performance.In this study, we (1) characterize the useful features of a metric that effectively captures time-to-solution and power performance dynamics; and (2) design and evaluate a novel set of application power efficiency (APE) metrics that accounts for time-to-completion, power utilization and power variability. Power utilization quantifies a system’s trapped capacity (unused power budget) and power variability quantifies an application’s variation of power consumption over time. We demonstrate how APE can be used to quantify application characteristics which, in turn, enables reasoning about the impacts of power capping on an application.

Multidimensional performance measures and factors and their linkage with performance

PurposeThis study investigates the extent to which performance measures (PMs) are used, the relationship between multidimensional PMs and firm performance and the factors related to firms' PM use.Design/methodology/approachThe authors surveyed a sample of Malaysian manufacturing firms. Participants responded to a questionnaire indicating their use of PMs, firm performance, level of managerial commitment, degree of environmental uncertainty and firm's use of technology.FindingsThe results indicate a high degree of PM use related to financial indicators, internal efficiency and customer-related metrics. The results also demonstrate that firm performance has significant positive relationships with use of PMs related to quality and customers, efficiency, innovativeness and social responsibility, as well as comprehensive PM use. Industry variation, firm size, technology use and environmental uncertainty are also significantly related to PM use.Practical implicationsPerformance measurement systems (PMSs) are an important tool for improving organisational strategy in rapidly changing markets. These findings underscore the significant role of PMSs in manufacturing firms' performance, including emerging economies. The results suggest that individual PMS approaches should align with each firm's evolving needs and the characteristics of the sector and environment in which each firm operates.Originality/valueThis study advances understandings of the contingency approach to PMSs in manufacturing environments.

Design, Usability, and Acceptability of a Needs-Based, Automated Dashboard to Provide Individualized Patient-Care Data to Pediatric Residents.

Pediatric residency programs are required by the Accreditation Council for Graduate Medical Education to provide residents with patient-care and quality metrics to facilitate self-identification of knowledge gaps to prioritize improvement efforts. Trainees are interested in receiving this data, but this is a largely unmet need. Our objectives were to (1) design and implement an automated dashboard providing individualized data to residents, and (2) examine the usability and acceptability of the dashboard among pediatric residents. We developed a dashboard containing individualized patient-care data for pediatric residents with emphasis on needs identified by residents and residency leadership. To build the dashboard, we created a connection from a clinical data warehouse to data visualization software. We allocated patients to residents based on note authorship and created individualized reports with masked identities that preserved anonymity. After development, we conducted usability and acceptability testing with 11 resident users utilizing a mixed-methods approach. We conducted interviews and anonymous surveys which evaluated technical features of the application, ease of use, as well as users' attitudes toward using the dashboard. Categories and subcategories from usability interviews were identified using a content analysis approach. Our dashboard provides individualized metrics including diagnosis exposure counts, procedure counts, efficiency metrics, and quality metrics. In content analysis of the usability testing interviews, the most frequently mentioned use of the dashboard was to aid a resident's self-directed learning. Residents had few concerns about the dashboard overall. Surveyed residents found the dashboard easy to use and expressed intention to use the dashboard in the future. Automated dashboards may be a solution to the current challenge of providing trainees with individualized patient-care data. Our usability testing revealed that residents found our dashboard to be useful and that they intended to use this tool to facilitate development of self-directed learning plans.

Improving Lagos State emergency medical services by analysing road traffic accident data, response time, and efficient allocation of ambulances

BackgroundEmergency medical services (EMS) play a critical role in the response to road traffic accidents (RTAs) by providing critical prehospital care to RTA victims, and thus helping to reduce the severity of injuries and casualties from RTAs. Analysing the efficiency of such systems and optimising the service (often with insufficient resources) would be highly beneficial to inform operating guidelines. In this study, we aimed to assess the ambulance utilisation rate for RTAs and provide insights about the system's efficiency and utilisation metrics in Lagos State, Nigeria. MethodsWe received hospital capacity information (type of hospital [eg, tertiary] and number of beds, nurses, operating rooms, and so on) from hospital survey data in Lagos. Additionally, we received RTA-specific intervention forms from the Lagos State Ambulance Service. With the limited amount of historical data, we highlighted methods to estimate the system parameters (eg, arrival rate, service rate, and so on) of RTA victims at various hospitals in Lagos. We also developed a parametric model and applied approximate dynamic programming approach for ambulance dispatch and location optimisation with a fixed number of base stations. Furthermore, our routing approach was accompanied by directions from OpenRouteService and queue analysis to determine the most appropriate hospital near an RTA for the EMS systems in Lagos. FindingsUsing patient-queue based wait-time analysis, we found that in over 80% of cases, one of the nearest three hospitals was the most appropriate recommendation for patient delivery. This exponentially decays with the next closest hospitals. Additionally, we found that optimising the ambulance relocation method, where the ambulance returns to a base selected by our model as opposed to its dedicated base or a random base, can reduce the average response time of the EMS system by 8%, or 3·5 min (from 41 min to 38 min). InterpretationAppropriate placement of mobile resources (ie, ambulances, health-care booths, and so on) can help to lower the response time of EMS systems in addressing RTAs. Similarly, optimal routing of patients to the most well prepared and nearby hospitals would significantly increase the system's efficacy and improve health outcomes. Our findings can provide valuable insight into this and further inform resource allocation and guidelines for health systems, by identifying those hospitals that have the highest burden of RTA victims and those that provide the most efficient care in a resource-limited setting. FundingNational Institutes of Health (1R21TW010991–01A1).

Driven by Density, Efficiency at Low Cost, Power Integration Reaches New Heights: PSiPs and PwrSoCs are growing faster than the traditional power supplies

For decades, miniaturization has been the trend in the power supply world. Consumers have been demanding more functions from smaller systems with better performance and reliability at lower cost. Such advances are evident in today’s laptops, tablets, smartphones, industrial drives and a host of other consumer and industrial applications. To meet these demands, the power supply industry has also kept pace with the needs of the markets. As a result, power integration, both in package and on-chip, has continued to evolve for more than a decade. Consequently, with this evolution, power density has risen from tens of watts to hundreds of watts from compact semiconductor packages with high performance in efficiency, density, reliability and other such metrics. On-chip, we are talking about tens of watts delivered from monolithic semiconductor substrates.

Wrap-Gate CNT-MOSFET Based SRAM Bit-Cell with Asymmetrical Ground Gating and Built-In Read-Assist Schemes for Application in Limited-Energy Environments

This paper proposes a novel design of ultra-low power radiation-hardened single-ended SRAM bit-cell using the gate-all-around CNT-MOSFET based-gate diffusion input method (GAA CNT-GDI) for application in radiation-prone terrestrial (low-orbit) environments, where resources of circuit’s power supply are limited. In the structure of bit-cell to improve read-/hold-stability and expand write-ability several schemes have been used such as asymmetric virtual ground gating, built-in read-assist and the multi-diameter/chirality for CNTs. Also, in order to investigate single/double upsets, injection circuit model using the structure of the T-connected pseudo resistors (TPRs) has been proposed. The results of extensive Monte-Carlo (MC) simulations to evaluate the proposed bit-cell indicate expand write/hold/read static noise margins about 12.5%, 3.8%, and 8.2%, other figure of merits (FoMs), such as performance, yield, variability (μ/σ) and critical charge about 6.4 %, 5.8 % and almost 1.19 times respectively compared to studied cell design in counterpart technologies. Moreover, the suggested bit-cell has more robustness against radiation-induced soft errors with high reliability of data storage in the presence of critical voltage conditions, and better results in terms of other comprehensive FoMs as compared to state-of-the-art bit-cells in the 16 nm technology. The proposed bit-cell in a real application is used to store data from two-layer quick-response (2LQR) code-based in safety-critical environments. The results show the better performance of bit-cell in terms of a comprehensive FoM, which provides more effective trade-off between the hardware efficiency and quality metrics to evaluate the appropriate accuracy in the pixel-by-pixel image as compared to other well-known counterpart designs. Finally, the simulation results have validated that the suggested memory architecture can be a suitable candidate for application in devices such as terrestrial satellites that require both high reliability and cost-effectiveness.

Solar-Driven Hydrogen Production: Recent Advances, Challenges, and Future Perspectives

Solar H2 production is considered as a potentially promising way to utilize solar energy and tackle climate change stemming from the combustion of fossil fuels. Photocatalytic, photoelectrochemical, photovoltaic–electrochemical, solar thermochemical, photothermal catalytic, and photobiological technologies are the most intensively studied routes for solar H2 production. In this Focus Review, we provide a comprehensive review of these technologies. After a brief introduction of the principles and mechanisms of these technologies, the recent achievements in solar H2 production are summarized, with a particular focus on the high solar-to-H2 (STH) conversion efficiency achieved by each route. We then comparatively analyze and evaluate these technologies based on the metrics of STH efficiency, durability, economic viability, and environmental sustainability, aiming to assess the commercial feasibility of these solar technologies compared with current industrial H2 production processes. Finally, the challenges and prospects of future research on solar H2 production technologies are presented.

A Comparative Analysis of White Matter Structural Networks on SCLC Patients After Chemotherapy.

Previous sMRI, DTI and rs-fMRI studies in small cell lung cancer (SCLC) patients have reported that patients after chemotherapy had gray and white matter structural alterations along with functional deficits. Nonetheless, few are known regarding the potential alterations in the topological organization of the WM structural network in SCLC patients after chemotherapy. In this context, the scope of the present study is to evaluate the WM structural network of 20 SCLC patients after chemotherapy and to 14 healthy controls, by applying a combination of DTI with graph theory. The results revealed that both SCLC and healthy controls groups demonstrated small world properties. The SCLC patients had decreased values in the clustering coefficient, local efficiency and degree metrics as well as increased shortest path length when compared to the healthy controls. Moreover, the two groups reported different topological reorganization of hub distribution. Lastly, the SCLC patients exhibited significantly decreased structural connectivity in comparison to the healthy group. These results underline that the topological organization of the WM structural network in SCLC patients was disrupted and hence constitute new vital information regarding the effects that chemotherapy and cancer may have in the patients' brain at network level.

Water-energy tradeoffs in data centers: A case study in hot-arid climates

Although energy and water are intertwined, the analysis of energy consumption in data centers (DCs) has largely ignored water needs. In this study, we utilize established DC efficiency metrics, power usage effectiveness (PUE) and water usage effectiveness (WUE), to analyze the water and energy use in two colocation DCs (DC1 and DC2) located in the greater Phoenix area. These DCs use distinct cooling systems: DC1 uses air-cooled chillers and DC2 uses both water-cooled chillers and evaporative cooling. We also present a method for analyzing the water-energy tradeoff in DCs as a useful tool for DC designers and managers to evaluate different cooling systems. The results highlight that although DC1’s average PUE is nearly 13% higher compared to DC2's average PUE, its source WUE is 66% lower than DC2. In addition, DC1’s PUE is more affected by seasonal changes (variation of dry-bulb temperature) , while DC2's WUE has a larger seasonal variation. Our cooling system analysis indicates that hybrid evaporative cooling has the least power consumption, while air-cooled chillers have the most water use. Utilizing free cooling and evaporative cooling when outside air condition permits (∼40% per year), results in substantial power and water savings in Phoenix DCs.

A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries.

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.

How circular is a value chain? Proposing a Material Efficiency Metric to evaluate business models

The concept of Circular Economy is a principle aiming to improve sustainable development by reducing resource use and impact on ecological systems. An increasing number of companies are applying this theory on design strategies and business models in order to close, slow and narrow material loops. To highlight the importance, guide practitioners, and evaluate the progress of circular economy, a high number of circularity metrics (C-metrics) have been developed. However, little attention has been given to creating a connection between quantification of circularity and environmental performance. Existing metrics also do not highlight the interplay between micro (product), meso (industrial symbiosis), and macro (regional) level circularity. Moreover, existing metrics do not capture all material loops and do not adopt a value chain perspective on material flows.To improve the connection between C-metrics and environmental performance, a framework connecting circular economy strategies and material flows was developed. Based on this framework, a material flow-based C-metric was designed aimed at converting mechanisms of closing, narrowing and slowing material loops into a single-point value. To evaluate its feasibility, the metric was tested on three circular business models that represent all three mechanisms in a value chain perspective. The results showed that the metric is feasible in more situations than existing metrics and that the circularity value is highly dependent on assumptions. In future studies, the metric should be tested and compared to Life Cycle Assessments on multiple system levels to ensure that it generates valid results. Furthermore, user input assumptions should be standardized to ensure metric reliability.

Trajectory Privacy Preserving for Continuous LBSs in VANET

Location-based services (LBSs) support various applications in vehicular ad hoc network (VANET). However, location/trajectory privacy becomes a serious concern for LBSs. Existing location/trajectory privacy-preserving schemes rarely take the attack model of adversaries into consideration, and the cost for achieving privacy has not been carefully studied. To deal with these problems, this study proposes a collaborative trajectory obfuscation scheme based on analyzing the attack model and designs a privacy-preserving efficiency metric that balances the achieved privacy and the cost. Through simulation, the effects of the density of vehicles using the same LBSs on the performance of our design and an existing scheme are investigated. The performance comparison results validate the effectiveness and efficiency of our scheme.

Automated Planning for Prostate Stereotactic Body Radiation Therapy on the 1.5 T MR-Linac

PurposeAdaptive stereotactic body radiation therapy (SBRT) for prostate cancer (PC) by the 1.5 T MR-linac currently requires online planning by an expert user. A fully automated and user-independent solution to adaptive planning (mCycle) of PC-SBRT was compared with user's plans for the 1.5 T MR-linac. Methods and MaterialsFifty adapted plans on daily magnetic resonance imaging scans for 10 patients with PC treated by 35 Gy (prescription dose [Dp]) in 5 fractions were reoptimized offline from scratch, both by an expert planner (manual) and by mCycle. Manual plans consisted of multicriterial optimization (MCO) of the fluence map plus manual tweaking in segmentation, whereas in mCycle plans, the objectives were sequentially optimized by MCO according to an a-priori assigned priority list. The main criteria for planning approval were a dose ≥95% of the Dp to at least 95% of the planning target volume (PTV), V33.2 (PTV) ≥ 95%, a dose less than the Dp to the hottest cubic centimeter (V35 ≤ 1 cm3) of rectum, bladder, penile bulb, and urethral planning risk volume (ie, urethra plus 3 mm isotropically), and V32 ≤ 5%, V28 ≤ 10%, and V18 ≤ 35% to the rectum. Such dose-volume metrics, plus some efficiency and deliverability metrics, were used for the comparison of mCycle versus manual plans. ResultsmCycle plans improved target dose coverage, with V33.2 (PTV) passing on average (±1 SD) from 95.7% (±1.0%) for manual plans to 97.5% (±1.3%) for mCycle plans (P < .001), and rectal dose sparing, with significantly reduced V32, V28, and V18 (P ≤ .004). Although at an equivalent number of segments, mCycle plans consumed moderately more monitor units (+17%) and delivery time (+9%) (P < .001), whereas they were generally faster (–19%) in terms of optimization times (P < .019). No significant differences were found for the passing rates of locally normalized γ (3 mm, 3%) (P = .059) and γ (2 mm, 2%) (P = .432) deliverability metrics. ConclusionsIn the offline setting, mCycle proved to be a trustable solution for automated planning of PC-SBRT on the 1.5 T MR-linac. mCycle integration in the online workflow will free the user from the challenging online-optimization task.

Average theoretical peak time as a metric to analytical speed in one dimensional and multidimensional gas chromatographic separations

The definition of a chromatographic analysis speed based simply on analysis time is an outdated concept to define conventional chromatography, fast chromatography, and emerging high-resolution techniques such as comprehensive two-dimensional and comprehensive three-dimensional gas chromatography. Here, the metric average theoretical peak time (ATPT) is proposed for separation speed, considering conventional and multidimensional separations. ATPT can be defined as the time (in ms per peak) needed to elute a theoretical peak in a chromatographic system. Using this metric, it is possible to define ranges, proposed for a normal speed (ATPT higher than 4000ms/peak), high speed (ATPT range from 600 to 4000ms/peak), very high speed (ATPT range from 200 to 600ms/peak), hyper speed (ATPT range from 3.3 to 200ms/peak) and ultra high speed chromatography (ATPT lower than 3.3ms/peak), that combines time and efficiency metrics. This metric was applied in several contexts to demonstrate its robustness to evaluate chromatographic separations for different techniques and analytical conditions. Applications also demonstrate the advantages of the use of ATPT as a method development metric tool.

Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins

Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling.

Abstract WMP67: Evaluation Of The Implementation Of An Ai Tool For Large Vessel Occlusion: Impact On Radiologists’ Workflow And Patient Outcomes

Timely and accurate diagnosis of a large vessel occlusion (LVO) is critical for prompt initiation of thrombectomy treatment to reduce morbidity and mortality in acute ischemic stroke patients. Recently, artificial intelligence (AI) tools to aid stroke triage have shown promise for their accuracy and speed. However, little is known about the impact of the clinical integration of AI on efficiency metrics and patient outcomes. This study aims to evaluate the impact of an AI-based LVO detection tool on three main areas: (1) performance metrics, (2) radiologist workflow, and (3) ischemic stroke metrics and outcomes. This single center retrospective analysis includes patients with suspected acute stroke who underwent CT angiograms (CTA) in a three month period before and after the implementation of an AI-based LVO detection tool. CTA of 224 patients pre-implementation and 144 patients post-implementation of the tool were evaluated. The tool correctly identified 93/107 cases, with an accuracy of 87%, sensitivity of 91%, and specificity of 86%. The PPV was 0.64 and the NPV was 0.97. The mean radiology report turnaround time (TAT) using the tool was significantly reduced at 19.9 minutes (SD 32.4) compared to 33.7 minutes (SD 34.7) without the tool (p=0.001). There was a significant improvement in the resident trainee TAT with the LVO tool, with a mean TAT of 17.9 minutes (SD 32.6) compared to 38.4 minutes (SD 33.6) without (p=0.0002). Baseline stroke risk factors were similar between the pre and post-implementation groups. There were no significant differences in stroke metric medians for door-to-image, door-to-needle, door-to-puncture, and door-to-revascularization times and there were no statistically significant differences in NIHSS on arrival, post treatment NIHSS, discharge mRS, or mortality. The findings in clinical outcomes and stroke metrics may be attributable to small sample size. This study validates an AI-based tool in the detection of LVO and demonstrates a significant associated reduction in report TAT, allowing for faster communication of critical findings to ordering clinicians. Further research is needed to evaluate the impact of these tools on stroke benchmarks and clinical outcomes and to establish their role in stroke triage for clinicians.