Prediction Of Events Research Articles

COMPARISON OF COMPONENTS OF CENTRAL AORTIC AND PERIPHERAL BLOOD PRESSURE FOR PREDICTION OF CARDIOVASCULAR EVENTS IN A 2-YEAR AND 14-YEAR FOLLOW-UP OF A HYPERTENSIVE COHORT

Objective: The aim of this study was to assess components of central aortic pressure, brachial blood pressure and indices of arterial stiffness for prediction of cardiovascular events in a hypertensive cohort. Design and method: Noninvasive measurements of central aortic blood pressure (SphygmoCor), brachial pressure, central augmentation index (cAIx), augmented pressure (cAP) and carotid intima-media thickness (IMT) were obtained in 681 hypertensive patients (age 61 ± 9 years, 425 males). The primary endpoints were defined as cardiovascular disease (CVD) events or death from CVD. An initial analysis was conducted at mean 2-year follow-up and a second analysis is currently being conducted at mean 14-year follow-up. Results: Total events were 59 (8.7%) for the 2-year follow-up and 265 (38.9%) for the 14-year follow-up. Major cardiovascular events were 49 (83.1%) and 178 (67.2%) respectively. The analysis of the 2-year follow-up suggested that central systolic (cSBP) and pulse pressure (cPP) showed higher hazard ratios (HR/10 mmHg) for cardiovascular events compared to peripheral pressure indices (pSBP, pPP) at age >60 years (cSBP: HR = 1.18, pSBP: HR = 1.17, p = 0.034; cPP: HR = 1.28, pPP: HR = 1.2, p = 0.019). Each SD increase in IMT and in cAP was associated with 1.4 times higher risk of increased total events in elderly patients (age >60 years). Males showed that each SD increase in cAP was associated with significantly higher risk of increased total events than females. The complete data for the 14-year follow-up analysis has only recently become available and the analysis will be completed for the conference presentation. Conclusions: Preliminary analysis in a short-term (2-year) follow-up study of a large hypertensive cohort has shown that central aortic pressure and its components can improve prediction of CVD compared to peripheral pressure. The final analysis of the long-term (14-year) follow-up will determine which components are robust predictors and will provide important information on the clinical and epidemiological relevance of central aortic pressure.

Prediction of Major Adverse Cardiovascular Events in Patients with Chest Pain Using Coronary Artery Calcium Score.

Prediction of Major Adverse Cardiovascular Events in Patients with Chest Pain Using Coronary Artery Calcium Score.

Okadaic acid as a major problem for the seafood safety (Mytilus galloprovincialis) and the dynamics of toxic phytoplankton in the Slovenian coastal sea (Gulf of Trieste, Adriatic Sea)

This article presents the first results on shellfish toxicity in the Slovenian sea (Gulf of Trieste, Adriatic Sea) since the analytical methods for the detection of biotoxins (PSP, ASP, DSP and other lipophilic toxins) in bivalve molluscs were included in the national monitoring program in 2013. In addition to toxins, the composition and abundance of toxic phytoplankton and general environmental characteristics of the seawater (surface temperature and salinity) were also monitored.During the 2014–2019 study period, only lipophilic toxins were detected (78 positive tests out of 446 runs), of which okadaic acid (OA) predominated in 97 % of cases, while dinophysistoxin-2 and yessotoxins only gave a positive result in one sampling event each. The number of samples that did not comply with the EC Regulation for the OA group was 17 or 3.8 % of all tests performed, all of which took place from September to November, while a few positive OA tests were also recorded in December, April, and May. This toxicity pattern was consistent with the occurrence pattern of the five most common DSP-producing dinoflagellates, which was supported by the development of warm and thermohaline stratified waters: Dinophysis caudata, D. fortii, D. sacculus, D. tripos and Phalacroma rotundatum. The strong correlation (r = 0.611, p < 0.001) between D. fortii, reaching abundances of up to 950 cells L−1, and OA suggests that D. fortii is the main cause of OA production in Slovenian waters.Strong interannual variations in OA and phytoplankton dynamics, exacerbated by the effects of anthropogenic impacts in this coastal ecosystem, reduce the predictability of toxicity events and require continuous and efficient monitoring. Our results also show that the introduction of the LC-MS/MS method for lipophilic toxins has improved the management of aquaculture activities, which was not as accurate based on mouse bioassays.

Utility of the ACD-GENE-CLI Score in Asian Patients with Critical Limb Ischemia Undergoing Endovascular Interventions.

Critical limb ischemia (CLI) is an emerging public health threat and lacks a reliable score for predicting the outcomes. The Age, Body Mass Index, Chronic Kidney Disease, Diabetes, and Genotyping (ABCD-GENE) risk score helps identify patients with coronary artery disease who have cytochrome P450 2C19 (CYP2C19) polymorphism-related drug resistance and are at risk for cardiovascular adverse events. However, its application to CLI remains unknown. In this study, we aim to validate a modified ACD-GENE-CLI score to improve the prediction of major adverse limb events (MALEs) in patients with CLI receiving clopidogrel. Patients with CLI receiving clopidogrel post-endovascular intervention were enrolled prospectively in two medical centers. Amputation and revascularization as MALEs were regarded as the outcomes. A total of 473 patients were recruited, with a mean follow-up duration of 25 months. Except for obesity, old age, diabetes, chronic kidney disease (CKD), and CYP2C19 polymorphisms were significantly associated with MALEs. Using bootstrap regression analysis, we established a modified risk score (ACD-GENE-CLI) that included old age (≥ 65 years), diabetes, CKD, and CYP2C19 polymorphisms. At a cutoff value of 8, the ACD-GENE-CLI score was superior to the CYP2C19 deficiency only, and the conventional ABCD-GENE score in predicting MALEs (area under the curve: 0.69 vs. 0.59 vs. 0.67, p=0.01). The diagnostic ability of the ACD-GENE-CLI score was consistent in the external validation. Also, Kaplan-Meier curves showed that in CYP2C19 deficiency, the ABCD-GENE and ACD-GENE-CLI scores could all differentiate patients with CLI who are free from MALEs. The modified ACD-GENE-CLI score could differentiate patients with CLI receiving clopidogrel who are at risk of MALEs. Further studies are required to generalize the utility of the score.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model

Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). A total of 4089 patient admissions were included, with a mean age±standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC=0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Short-term Classification of Strong Solar Energetic Particle Events Using Multivariate Time-series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors, including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle data set covering solar cycles 22, 23, and 24. We develop a framework using time-series-based machine-learning (ML) models with the aim of developing robust short-term forecasts by classifying SEP events. For this purpose, we introduce an ensemble learning approach that merges the results from univariate time series of three proton channels (E ≥10, 50, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions and analyze their performance. We consider three models, namely, time series forest, supervised time series forest (STSF), and Bag-of-Symbolic Fourier Approximation Symbols. Our study also focuses on understanding and developing confidence in the predictive capabilities of our models. Therefore, we utilize multiple evaluation techniques and metrics. Based on that, we find STSF to perform well in all scenarios. The summary of metrics for the STSF model is as follows: the area under the ROC curve = 0.981, F 1-score = 0.960, true skill statistics = 0.919, Heidke skill score = 0.920, Gilbert skill score = 0.852, and Matthew’s correlation coefficient = 0.920. The Brier score loss of the STSF model is 0.077. This work lays the foundation for building near-real-time short-term SEP event predictions using robust ML methods.

Decoding the Atmosphere: Optimising Probabilistic Forecasts with Information Gain

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

TSOANet: Time-Sensitive Orthogonal Attention Network for medical event prediction

Medical Event Prediction (MEP) based on Electronic Medical Records (EMR) is an essential and valuable task for healthcare. For a patient, information in the EMR can be organized into a structured sequence, consisting of multiple visits each with details about visit time and various types of medical events. As the time intervals between neighboring visits are irregular and the medical events at different visits can vary significantly, MEP based on EMR is still challenging. Many studies have been proposed to model the irregular time intervals, relations among different types of medical events within each visit and relations among medical events across visits, and reported exciting results. However, most of these studies focus on two out of the three aspects mentioned above, with only a few addressing all the three aspects simultaneously. In this study, we propose a novel network, the Time-Sensitive Orthogonal Attention Network (TSOANet), which can fully utilize the irregular time intervals, relations among different types of medical events within and across visits. In particular, we design two key components: (1) Time-Sensitive Block, used to model the time intervals at both local and global levels to determine the impact of each visit in EMR; (2) Orthogonal Attention Block, used to model relations among different types of medical events within each visit and across visits in two axes, that is, event axis and time axis. Extensive experiments on two public real-world EMR datasets demonstrate that TSOANet outperforms the state-of-the-art models for various prediction tasks, thereby verifying the effectiveness of our approach. The source code of TSOANet is released at https://github.com/chh13502/TSOANet.

DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs

Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture the high-order correlations among entities. To this end, we propose DHyper, a recurrent Dual Hypergraph neural network for event prediction in TKGs, which simultaneously models the influences of the high-order correlations among both entities and relations. Specifically, a dual hypergraph learning module is proposed to discover the high-order correlations among entities and among relations in a parameterized way. A dual hypergraph message passing network is introduced to perform the information aggregation and representation fusion on the entity hypergraph and the relation hypergraph. Extensive experiments on six real-world datasets demonstrate that DHyper achieves the state-of-the-art performances, outperforming the best baseline by an average of 13.09%, 4.26%, 17.60%, and 18.03% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

DDI‐Transform: A neural network for predicting drug‐drug interaction events

AbstractDrug‐drug interaction (DDI) event prediction is a challenging problem, and accurate prediction of DDI events is critical to patient health and new drug development. Recently, many machine learning‐based techniques have been proposed for predicting DDI events. However, most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information. To address these limitations, we propose a DDI‐Transform neural network framework for DDI event prediction. In DDI‐Transform, we design a drug structure information feature extraction module and a drug bind‐protein feature extraction module to obtain multidimensional feature information. A stack of DDI‐Transform layers in the DDI‐Transform network module are then used for adaptive learning, thus adaptively selecting the effective feature information for prediction. The results show that DDI‐Transform can accurately predict DDI events and outperform the state‐of‐the‐art models. Results on different scale datasets confirm the robustness of the method.

Analysis of bias correction of HRRR model outputs for offshore wind power ramp events

This study examines the impact of bias correction on the prediction of offshore wind power ramp events (WPREs) using NOAA's High-Resolution Rapid Refresh (HRRR) model. The analysis focuses on New England's offshore region, using Light Detection and Ranging (LiDAR) wind observations and the power curve derived from the nearby Block Island wind farm as a reference. The study characterizes bias in terms of amplitude and phase, finding a 30-min systematic lag error and a marginal underestimation of high wind speeds. Perfect bias correction treatments including both the amplitude and the phase are applied to HRRR wind speed, and their impact on WPREs is assessed using categorical performance measures. A large discrepancy in the number of cases with HRRR having much fewer cases than the reference is found, which is attributed to the model's temporal and spatial resolution. The results show that the amplitude bias correction has a marginal impact on reducing frequency bias, while the dynamic time warping (DTW) used in the phase bias correction shows promising results but limited performance. The study concludes that phase bias correction can be a feasible option to reduce bias in WPREs in offshore power systems.

Triglyceride Glucose Index in the Prediction of Adverse Cardiovascular Events in Patients with Coronary Artery Diseases

Background: The triglyceride-glucose index offers a novel perspective as an alternative indicator of insulin resistance and anticipates increased long-term cardiovascular risks. Elevated TyG index values have been correlated with an increased risk of cardiovascular events. Methods: PubMed, Cochrane, Web of sciences and Google scholar were searched for “Triglyceride glucose index AND cardiovascular outcome” studies. Patients with coronary artery disease (CAD) with cardiovascular outcome at in-hospital or on follow up reported through hazard ratio (HR) analysis were considered for systemic review. Twenty studies were chosen to examine the correlation between cardiovascular outcomes and TyG index in patients admitted for coronary disease. Results: Twenty observational studies involving 1,52,846 patients were included in the present systemic review. Included studies reported relation between TyG index and Major adverse cardiovascular events (MACE) through Hazard ratio analysis and found significant hazard ratio of TyG index (conditional/continuous) ranged from (HR= 1.06 to 12.92) for MACE after adjusting confounders. In hospital MACE analysis study involving 4839 participants, the HR = 1.83 (1.27, 2.64) for TyG ≥9.37 quartiles and HR = 1.23 (1.10, 1.38) for TyG index continuous value. Included studies showed significant association between higher value of TyG index and cardiovascular outcomes. Conclusion: Emerging evidence indicates a potential link between the TyG and cardiovascular events. Though further research is needed to solidify this associations, the TyG index holds promise as a simple and accessible tool for identifying individuals at risk of cardiovascular complications.

Isolating pollen signals from laser diode aerosol Optical Particle Counter (OPC) data through positive matrix factorization (PMF) and Unmix receptor models

Pollen, a significant natural bioaerosol and allergen for sensitized individuals, is expected to increase in prevalence due to climate change. Mitigating allergy symptoms involves avoiding pollen exposure and pre-medication, emphasizing the importance of real-time knowledge of localized ambient air pollen concentrations. Laser diode Optical Particle Counters (OPCs) are commonly used for monitoring particle number concentrations in ambient air. This study explores the hypothesis that OPCs can monitor pollen but may struggle to distinguish them from other particles. We aimed to isolate the pollen signal from collective particle number concentrations using source apportionment models, specifically Positive Matrix Factorization (PMF) and Unmix, applied to multiple bin OPC data. The pollen signals isolated using PMF show slightly better correlation values than those isolated using Unmix. PMF-derived pollen signals exhibit strong correlations with Holoptelea (r = 0.64) and total pollen (r = 0.54) concentrations, while a moderate correlation is observed with Poaceae (r = 0.47). Exclusion of low pollen events strengthens correlations for Holoptelea and Poaceae to very strong (r = 0.87) and strong (r = 0.67), respectively. Although both model types effectively isolate the pollen signal, metrics suggest that Unmix has the potential for more accurate predictions of both moderate and extreme pollen events simultaneously. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RRMSE) metrics for Holoptelea are 46.2 grains m−3, 72.4 grains m−3, and 15.3; for Poaceae, 3.9 grains m−3, 4.9 grains m−3, and 13.0; and for total pollen, 43.5 grains m−3, 72.1 grains m−3, and 14.1. This study represents a significant development in the use of source apportionment models and ambient OPCs for real-time pollen monitoring, offering a cost-effective alternative to conventional automated pollen sensors. Despite challenges, the proposed methodology provides a practical and accessible solution for pollen monitoring, contributing to the advancement of bioaerosol monitoring technologies.

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Dynamic pathway linking Pakistan flooding to East Asian heatwaves.

In July to August 2022, Pakistan suffered historic flooding while record-breaking heatwaves swept southern China, causing severe socioeconomic impacts. Similar extreme events have frequently coincided between two regions during the past 44 years, but the underlying mechanisms remain unclear. Using observations and a suite of model experiments, here, we show that the upper-tropospheric divergent wind induced by convective heating over Pakistan excites a barotropic anomalous anticyclone over eastern China, which further leads to persistent heatwaves. Atmospheric model ensemble simulation indicates that this dynamic pathway linking Pakistan flooding and East Asian heatwaves is intrinsic to the climate system, largely independent of global sea surface temperature forcing. This dynamic connection is most active during July to August when convective variability is large over Pakistan and the associated divergent flow excites barotropic Rossby waves that propagate eastward along the upper troposphere westerly waveguide. This robust waveguide and the time delay offer hopes for improved subseasonal prediction of extreme events in East Asia.

XWaveNet: Enabling uncertainty quantification in short-term ocean wave height forecasts and extreme event prediction

To aid accurate short-term forecast of wave heights and extreme events prediction from buoy data, a deep learning approach is proposed. Wave heights are forecasted up to 72 h in advance from multivariate meteorological and oceanographic data, followed by quantification of uncertainty in the forecasted values, and estimation of exceedance probabilities for extreme event conditions. Using Monte Carlo dropout technique, network regularization and forecast uncertainty assessment were performed. To forecast wave heights, a stacked long short-term memory (LSTM) model was trained on historical buoy data augmented with positionally encoded features. Per comparative evaluation of different models, across the 72 h forecast horizon, positionally encoded temporal attributes resulted in a mean prediction error and forecast uncertainty of only 3 % and 13.5 %, respectively. Contrarily, without temporal feature augmentation mean error was 5 % and uncertainty, 15 %. Therefore, the former model was ensembled with a Random Forest model for exceedance probability estimation. The hybrid model yielded a 21.4 % better separability between the extreme and normal observations in the test data from a nearby buoy in the northern Gulf of Mexico than individual models. All the four extreme events in the test set were correctly predicted with 97 % mean accuracy. Further evaluation of the deep ensemble model on 5 other spatially diverse buoy datasets from the Gulf of Mexico, Atlantic and Pacific coasts, suggested excellent generalizability for short-term wave forecasts and extreme event prediction, with an average maximum error of just 5.2 % across all the 6 buoy test datasets and the entire forecast horizon.

Attitudes of Jordanian Anesthesiologists and Anesthesia Residents towards Artificial Intelligence: A Cross-Sectional Study.

Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI's role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human-human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.

Evaluation of COSMO-CLM Model Parameter Sensitivity in the Study of Extreme Events across the Eastern Region of India

The present study aims to identify the parameters from the Consortium for Small-scale Modelling in CLimate Mode (COSMO-CLM) regional climate model that strongly controls the prediction of extreme events over West Bengal and the adjoining areas observed between 2013 to 2018. Metrics, namely Performance Score (PS) screen out the most persuasive parameter on model output. Additionally, the Performance Index (PI) measure the reliability of the model and Skill Score (SS) establishes the model performance against the reference simulation leading to the optimization of the model for a given variable. In this study, parameter screening for four output variables such as 2m-temperature, surface latent heat flux, precipitation and cloud cover of COSMO-CLM is accomplished. For heat wave simulations, 2m-temperature and surface latent heat flux are explored whereas cloud cover and precipitation are examined for extreme rainfall events. A total of 25 adjustable parameters representing the following parameterization schemes: turbulence, land surface process, microphysics, convection, radiation and soil. Out of the six parameterization schemes, the scaling factor of the laminar boundary layer for heat (rlam_heat) and the ratio of laminar scaling factors for heat over sea and land (rat_sea) from the land surface process is sensitive to SLH, TP. The exponent to get the effective surface area (e_surf) from the land surface has a large impact on 2m-temperature. A few parameters from microphysics (cloud ice threshold for auto conversion), convection (mean entrainment rate for shallow convection) and radiation (parameter for computing the amount of cloud cover in saturated conditions) play a significant role in producing TP, and TCC fields. It is evident from the results that the parameter sensitivities on model performance depend on the choice of the meteorological field. Furthermore, in almost all input model parameters, the model performance reveals the opposite character in different domains for a given meteorological field.

Understanding winter windstorm predictability over Europe

Abstract. Winter windstorms belong to the most damaging meteorological events in the extra-tropics. Their impact on society makes it essential to understand and improve seasonal forecasts of these extreme events. Skilful predictions on a seasonal timescale have been shown in previous studies by investigating hindcasts from various forecast centres. This study aims to explain storm forecast skill based on relevant dynamical factors. Therefore, a number of factors which are known to influence either windstorms directly or their synoptic relevant systems, mid-latitude cyclones, are investigated. These factors are analysed for their relation to windstorm forecast performance based on a reanalysis (ERA5) and the seasonal hindcast of the UK Met Office (Global Seasonal forecasting system version 5, GloSea5). Within GloSea5, relevant dynamical factors are (1) validated with respect to their physical connections to windstorms, (2) investigated with respect to the seasonal forecast skill of the factors themselves, and (3) assessed on the relevance and influence of their forecast performance to and on windstorm forecast skill. Although not all investigated factors reveal a clear and consistent influence on windstorm forecast skill over Europe, core factors like mean sea level pressure gradient, sea surface temperature, equivalent potential temperature and Eady growth rate show consistent results within these three steps: their physical connection is well represented in the model; these factors are skilfully predicted in storm-relevant regions, and, consequently, this skill leads to increased forecast skill of winter windstorms over Europe. This study thus explains existing forecast skill in winter windstorms but also indicates potential for further model developments to improve seasonal winter windstorm predictions.