Occurrence Probability Of Events Research Articles

Development of mortality prediction model for critically ill patients based on multidimensional and dynamic clinical characteristics

To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model. The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score. Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)]. The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.

Explainable Hyperbolic Temporal Point Process for User-Item Interaction Sequence Generation

Recommender systems which captures dynamic user interest based on time-ordered user-item interactions plays a critical role in the real-world. Although existing deep learning-based recommendation systems show good performances, these methods have two main drawbacks. Firstly, user interest is the consequence of the coaction of many factors. However, existing methods do not fully explore potential influence factors and ignore the user-item interaction formation process. The coarse-grained modeling patterns cannot accurately reflect complex user interest and leads to suboptimal recommendation results. Furthermore, these methods are implicit and largely operate in a black-box fashion. It is difficult to interpret their modeling processes and recommendation results. Secondly, recommendation datasets usually exhibit scale-free distributions and some existing recommender systems take advantage of hyperbolic space to match the data distribution. But they ignore that the operations in hyperbolic space are more complex than that in Euclidean space which further increases the difficulty of model interpretation. To tackle the above shortcomings, we propose an E xplainable H yperbolic T emporal P oint P rocess for User-Item Interaction Sequence Generation (EHTPP) . Specifically, EHTPP regards each user-item interaction as an event in hyperbolic space and employs a temporal point process framework to model the probability of event occurrence. Considering that the complexity of user interest and the interpretability of the model,EHTPP explores four potential influence factors related to user interest and uses them to explicitly guide the probability calculation in the temporal point process. In order to validate the effectiveness of EHTPP, we carry out a comprehensive evaluation of EHTPP on three datasets compared with a few competitive baselines. Experimental results demonstrate the state-of-the-art performances of EHTPP.

Risk Assessment of Offshore Wind Turbines Suction Bucket Foundation Subject to Multi-Hazard Events

For the offshore wind turbines (OWTs) located in a seismically active region, the occurrence of earthquakes combined with scour is a highly possible multi-hazard event. This study developed an alternative fragility analysis framework to assess the seismic performance of suction bucket-supported OWTs under the action of scour. First, the probabilistic approach was applied to calculate the occurrence probability of scour depth (SD) and earthquake events. Then, the possible combinations of these two events were considered in the analysis model to establish the fragility surface of the suction bucket foundation. Finally, by integrating the product of scour and earthquake hazard, as well as fragility curves, the suction bucket foundation failure probability was obtained. The developed framework provides a reliable approach to risk assessment for OWT-supporting structures in extreme event situations and can be applied to other complex natural hazards.

Quantitative Estimation of Pipeline Slope Disaster Risk in China

China’s economic development is closely related to oil and gas resources, and the country is investing heavily in pipeline construction. Slope geological hazards seriously affect the long-term safe operation of buried pipelines, usually causing pipeline leakage, property and environmental losses, and adverse social impacts. To ensure the safety of pipelines and reduce the probability of pipeline disasters, it is necessary to predict and quantitatively evaluate slope hazards. While there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards, existing methods only provide information on the occurrence probability of slope events, not whether a slope disaster will lead to pipeline damage. Taking the 2015 Xinzhan landslide in Guizhou Province, China, as an example, this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage, and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium- and high-risk areas. The results provide a reference for future pipeline disaster prevention and control.

A time-dependent subdistribution hazard model for major dental treatment events in cancer patients: a nationwide cohort study

BackgroundDental care in cancer patients tends to be less prioritized. However, limited research has focused on major dental treatment events in cancer patients after the diagnosis. This study aimed to examine dental treatment delays in cancer patients compared to the general population using a national claims database in South Korea.MethodThe Korea National Health Insurance Service-National Sample Cohort version 2.0, collected from 2002 to 2015, was analyzed. Treatment events were considered for stomatitis, tooth loss, dental caries/pulp disease, and gingivitis/periodontal disease. For each considered event, time-dependent hazard ratios and associated 95% confidence intervals were calculated by applying a subdistribution hazard model with time-varying covariates. Mortality was treated as a competing event. Subgroup analyses were conducted by type of cancer.ResultsThe time-dependent subdistribution hazard ratios (SHRs) of stomatitis treatment were greater than 1 in cancer patients in all time intervals, 2.04 within 30 days after cancer diagnosis, and gradually decreased to 1.15 after 5 years. The SHR for tooth loss was less than 0.70 within 3 months after cancer diagnosis and increased to 1 after 5 years. The trends in SHRs of treatment events for other dental diseases were similar to those observed for tooth loss. Subgroup analyses by cancer type suggested that probability of all dental treatment event occurrence was higher in head and neck cancer patients, particularly in the early phase after cancer diagnosis.ConclusionApart from treatments that are associated with cancer therapy, dental treatments in cancer patients are generally delayed and cancer patients tend to refrain from dental treatments. Consideration should be given to seeking more active and effective means for oral health promotion in cancer patients.

RETROSPECTIVE STUDY OF VERTICAL GROUND DEFORMATION IN COMO, NORTHERN ITALY: INTEGRATION OF LEVELLING AND PSI MEASUREMENTS

Abstract. Subsidence-related vertical ground deformation due to natural and anthropogenic factors may lead to considerable damages to structures and infrastructures, and may increase the occurrence probability of consequential events, such as floods, especially on sea and lake shores. Como city, placed in the north of Italy, adjacent to Como Lake, is subjected to significant subsidence phenomenon, which has been monitored by geodetic levelling networks. In this work the historical geodetic levelling measurements and satellite-based Synthetic Aperture Radar (SAR) data archive are integrated to assess the accuracy of Atmospheric Delay and Deformation Rate estimations obtained through Persistent Scatter Interferometry (PSI) techniques. Tree levelling measurement datasets acquired in 1990, 1997 and 2004 are used in order to obtain the precise deformation rate at the benchmarks for two periods of 1990–1997 and 1997–2004. For multi-temporal InSAR analysis, 106 SAR images (1992–2004) and 41 SAR images (1992–2004) in Ascending Track orbit from ERS-1/2 missions are used in this study. The assessment is performed through a statistical comparison between two sets of vertical land deformation rates obtained by integrated methods. The results of the validation represented a good consistency between deformation rates derived by both techniques. Also, this study has revealed the potential of SAR images acquired in gyro-less mode by ERS-2 mission (2001–2004) in terms of the estimation of ground deformation.

Использование метода экспертных оценок при оценке трудозатрат сотрудников государственного пожарного надзора

Purpose. The problem of determining labor costs and regulating the number of the supervision units has not been fully investigated, but taking into account the current situation of uncertainty and the accumulation of unfulfilled functions within the framework of the current legislation, it is relevant and in demand. The authors investigated various methods for increasing the efficiency of the working time fund distribution, including by increasing the number of employees. As part of the general study, the authors selected experts to develop a questionnaire containing regulated types of work and allowing systematizing, structuring and processing the data obtained to improve the planning of employees' activities and increase their work efficiency. Based on the study of the existing methods, an approach is proposed to form an expert group and develop a questionnaire to determine the labor costs of the engineering and inspecting staff. The survey was conducted with the participation of the personnel of the Supervisory Department of the Main Directorate of EMERCOM of Russia for the Nizhny Novgorod Region. Methods. Statistical data on the number of control (supervisory) activities carried out by Federal State Fire Supervision authorities in the Nizhny Novgorod region were used. When selecting candidates for the expert group, the Delphi method, the “snowball” method, the ranking method, etc. were used. The method of expert assessments was used to develop the questionnaire. Findings. The list of consolidated operations performed by a State Fire inspecting officer was formed, a decomposition of work was carried out, which made it possible to form a questionnaire on the labor costs distribution for an official. The method for determining the probability of events occurrence based on retrospective statistical data was chosen. Research application field. The outcomes of the work make it possible to form a mathematical apparatus for determining statistical indicators; determine the labor costs for performing each type of work; explore objective and subjective factors affecting labor costs; build predictive models for the supervisory activities management. Conclusions. The work carried out made it possible to develop an approach to the formation of expert groups for solving multi-criteria problems of streamlining and distributing the working time fund of the engineering and inspecting staff.

Record High Warm 2021 February Temperature over East Asia

Human-induced warming is estimated to have increased the occurrence probability of events like the record-breaking warm February in East Asia by a factor of 4–20.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling.

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Effects of anthropogenic forcing and atmospheric circulation on the record-breaking welt bulb heat event over southern China in September 2021

In September 2021, southern China witnessed an extreme high-temperature and high-humidity event. The average regional wet bulb globe temperature (WBGT) anomaly (relative to 1961–1990 mean) in 110.0°–120.0°E, 27.5°–32.5°N region was the highest on record at 3.28 °C and exceeded three times the observed standard deviation. To investigate the underlying causes, we examine the effects of anthropogenic forcings and anomalous circulation patterns on this event using the multi-model ensembles from the Coupled Model Intercomparison Project Phase 6. Results indicate that 2021-like events would happen extremely rarely without anthropogenic warming (would not occur in counterfactual world simulations) and have become a 1-in-16-year event in the factual world. For the threshold of the second most extreme year, the occurrence probability of extreme WBGT events increases approximately 50 times due to the impact of anthropogenic forcings. The effect of anthropogenic warming under similar atmosphere circulation increases the probability of extreme WBGT events by 13–60 times, and that of corresponding circulation patterns under the same anthropogenic warming increases the probability by 1.3–1.8 times.

Probabilistic Evaluation of Susceptibility to Fluid Injection-Induced Seismicity Based on Statistics of Fracture Criticality

AbstractFault reactivation and associated microseismicity pose a potential threat to industrial processes involving fluid injection into the subsurface. In this research, fracture criticality, defined as the gradient of critical fluid pressure change to trigger seismicity (Δpc/h), is proposed as a novel reservoir depth-independent metric of fault slip susceptibility. Based on statistics of the fracture criticality, a probabilistic evaluation framework for susceptibility to injection-induced seismicity was developed by integrating seismic observations and hydrogeological modelling of fluid injection operations for faulted reservoirs. The proposed seismic susceptibility evaluation method considers the injection-driven fluid pressure increase, the variability of fracture criticality, and regional fracture density. Utilising this methodology, the probabilistic distribution of fracture criticality was obtained to evaluate the potential for injection-induced seismicity in both fault and off-fault zones at the Hellisheiði geothermal site, Iceland. It has been found that the fracture criticality within both fault and off-fault zones shows natural variability (mostly ranging between 0.001 and 2.0 bar/km), and that fault zones tend to be characterised by larger fracture criticality values than the off-faut zones. Fracture criticality values estimated within each zone roughly follow a Gaussian distribution. Fault zones around five geothermal fluid re-injection wells at the site were estimated to have relatively high probability of seismic event occurrence, and these regions experienced high levels of induced seismicity over the microseismic monitoring period. The seismotectonic state estimated for each zone is generally consistent with the forecasted susceptibility to seismicity based on statistics of fracture criticality.

The Contribution of Climate Change to Increasing Extreme Ocean Warming Around Japan

AbstractGlobal warming is attributed to human activities that potentially cause record warm sea surface temperatures (SSTs) worldwide. However, to what extent climate change has altered discrete extreme ocean warming (EOW) around Japan remains unclear. Here, we show the impact of global warming on EOW events (high SSTs less frequent than once per 20 years at the preindustrial level) surrounding Japan in each calendar month for 1982–2022 using climate models. In contrast to the gradually increasing occurrences of EOW events, the most frequent events occurred in approximately 1998 and the mid‐2010s, especially in southern Japan. We found climate change was not yet dominant in the 1998 events but has multiplied the occurrence probability of most events by at least twice since 2000. Furthermore, we estimated that the possibility of the future normal climate exceeding record high SSTs can be sharply reduced by limiting global warming from 2°C to 1.5°C.

En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Compound drought constrains gross primary productivity in Chinese grasslands

Water constraints disturb and damage the growth and development of grassland vegetation mainly through both atmospheric and soil pathways. In the background of rapid climate change in the future, the impacts of water constraints on grasslands are expected to further deepen. However, current studies lack reports exploring the frequency, intensity, and area of land-atmospheric compound drought on carbon indicators in grassland ecosystems. In this study, we analyze the response of China grasslands to dual terrestrial-atmospheric water constraint events using ISIMIP gross primary productivity (GPP) data to reveal the carbon cycle-climate feedback relationships over the Chinese grassland. We found that the occurrence probability of compound drought events (i.e. land-atmospheric water constraint) was 3–4 times higher than that of random drought events, and the frequency, intensity, and affected area of compound droughts were significantly higher than that of single droughts. Compound droughts caused a decline of up to 20.27% in GPP of grassland ecosystems in China, while the decline of single atmospheric drought or soil drought was only 12.34% and 14.32%. Which is because vapor pressure deficit and soil moisture are a set of strongly coupled bivariate variables, and the continued strengthening of the land-atmospheric feedback causes a higher probability of occurrence of compound drought events and an increased impact on ecosystem GPP.

Caustics in turbulent aerosols: an excitable system approach

Aerosol particles are inertial particles. They cannot follow the surrounding fluid in a region of high vorticity. These encounters render the particle velocity field ${{\boldsymbol {v}}}$ locally compressible. Caustics occur if the trace of the gradient matrix $\textsf{$\boldsymbol{\sigma}$}$ of the field ${{\boldsymbol {v}}}$ locally diverges. For three-dimensional isotropic and homogeneous turbulence, the dynamics of the gradient matrix can be expressed in terms of three geometric invariants. In the present paper we establish a parametrisation of this problem where the dynamics takes the form of an excitable stochastic dynamical system with a three-dimensional phase space. The deterministic part of the dynamics is solved analytically. We show that the deterministic system has a globally attractive stable fixed point. Small noise induces excursions from the fixed point that typically relax straight back towards the fixed point. Caustics emerge as non-trivial return to a global fixed point when noise excites a trajectory across a stability threshold. The relaxation to the global fixed point will then involve at least one, and it may involve two or even three divergences of $\boldsymbol {Tr}\,\textsf{$\boldsymbol{\sigma}$}$ . Based on a combination of analytical insights and numerical analysis, we determine the rate of occurrence, duration and relative observation probability of caustic events in turbulent aerosols. Our analysis reveals that each approach towards a divergence proceeds along a straight line in the phase space of the dynamical system, which can help identify caustics. Moreover, there are infinite ways in which caustics can arise, namely whenever $\boldsymbol {Tr}\,\textsf{$\boldsymbol{\sigma}$}$ tends to $-\infty$ , so that no two caustics look the same.

Use of fuzzy fault tree analysis and Bayesian network for occurrence likelihood estimation of navigational accidents in the Qinzhou Port

This paper utilizes a fuzzy fault tree analysis and Noisy-OR gate Bayesian network to estimate the occurrence likelihood of navigational accidents. The kernel of this proposed method is first to construct the fault tree from investigation reports of navigational accidents, to calculate the occurrence probability of basic events using fuzzy set, to transform the fault tree to Bayesian network using the Noisy-OR gate. The merit of the developed model can overcome the problem of absolute description of the relationships between basic events and intermediate events. Finally, the model is applied to Qinzhou Port, the results are reasonable by comparing the results in other waterways. Moreover, the key influencing factors are identified from minimum cut set analysis and sensitive influencing factors are quantified sensitivity analysis. Consequently, the findings are beneficial for the maritime authorities to take countermeasures for navigational accidents prevention.

Quantitative risk assessment of railway intrusions with text mining and fuzzy Rule-Based Bow-Tie model

With the increasing traveling speed of railway transportation, rail right-of-way intrusions can cause high-consequence accidents and pose severe challenges to railway safety. Although intrusion detection technologies have been widely studied and applied, they can only support in-event inspection and post-event control. In the current complex environment, there is a critical need to analyze the causal chain of railway intrusions and mitigate safety risks before or during the risk evolution process. This paper developed a novel methodological framework on the cause-consequence model based on the text mining techniques and fuzzy bow-tie modeling to systematically investigate the railway intrusion risks. In order to mine both critical factors and their interrelationships, a lexical co-occurrence analysis was carried out on a customized corpus of intrusion accident recordings. Then structured bow-tie diagrams were developed based on the networks generated by unstructured data. To overcome the data uncertainty issue, this paper utilized cause-consequence-based probabilistic analysis and fuzzy theory to quantify the risks involving the occurrence probability of top events and outcomes in terms of expert judgements. The application of the proposed bow-tie model was demonstrated based on the case of the Hualien Derailment accident. The findings based on the bow-tie model and historical accidents in this research have systematically summarized basic events and causal chains. Ultimately, they can be utilized by researchers and practitioners both to identify the critical risk factors and enhance railway safety via proactive and reactive measures.

Extreme temperature trend and return period mapping in a changing climate in Upper Tekeze river basin, Northern Ethiopia

There is a consensus that global warming leads to the frequent occurrence of extreme temperature events that significantly impact human life, water resources, agricultural production, and ecosystems. However, the magnitudes of projected temperatures and the probabilities of extreme event occurrences are still deficient for robust planning and early warning efforts at major river basins. This study aimed to analyze extreme temperature trends and mapping return periods in a changing climate for the case of the upper Tekeze river basin, Northern Ethiopia. The future temperature is projected based on global climate models (GCMs) under RCP 4.5 and RCP 8.5 scenarios. The annual extreme temperatures were obtained from the daily temperature data series for the 12 stations by applying the block maxima series method. The non-parametric Mann-Kendall (M-K) statistical test was used for trend detection. The inverse distance weighted (IDW) method has been used to interpolate the point data and reveal the spatial patterns of the extreme temperature of the 50-year return period. The result shows the future extreme temperature will rise by 0.6–2.2 °C. Moreover, the future extreme temperature revealed a significant increasing trend in most of the stations (around 66.3%). The spatial extent of the 50-year return period of the extreme temperature map showed that in the future (2022–2051), the extreme temperature will be increased by 0.2–3.4 °C compared to the baseline period (1980–2009). The extreme temperature trend and return period mapping under climate change will help better local plans and fine-tune mitigation and adaptation measures.

Transient high latitude geomagnetic response to rapid increases in solar wind dynamic pressure

Rapid changes in solar wind dynamic pressure can produce a transient geomagnetic response in the high latitude ionosphere. In this study we carry out a superposed epoch analysis of the geomagnetic response based on 2,058 events. The events are divided into 12 groups based on interplanetary magnetic field clock angle and dipole tilt and the magnetic perturbation field is modeled using spherical harmonics. We find that the high latitude transient current vortices associated with a sudden commencement are most clearly observed when the interplanetary magnetic field is northward during equinox and winter in the northern hemisphere. The high latitude geomagnetic response during northward interplanetary magnetic field is decomposed into a preliminary and main impulse. The preliminary impulse onset is 1–2 min prior to the onset of the low/mid latitude geomagnetic response and its rise time is 4–6 min. The main impulse onset is around 2 min after the low/mid latitude geomagnetic response and has a rise time of 6–11 min. When examining the change relative to pre-onset conditions a coherent transient geomagnetic response emerges for all IMF clock and dipole tilt angles. The current vortex associated with the main impulse on the dawnside appears at (9.3 ± 0.5 mlt, 64.8° ± 1.5° mlat) and moves westward with a velocity of 5 ± 1.4 km/s. The vortex on the duskside appears at (15.3 ± 0.9 mlt, 65.8° ± 2.5° mlat) and does not move significantly. In addition, the models were used to recreate the SMR index showing a significant mlt dependence on the magnetic perturbation above 40° mlat and below 10° mlat. The former is thought to be caused by high latitude ionospheric currents. The latter is potentially a combination of the event occurrence probability being skewed toward certain UT ranges for large dipole tilt angles and a UT dependence of the equatorial electrojet magnitude caused by the south atlantic magnetic anomaly.

Multivariate Bayesian Arm-Based Network Meta-Analysis of Pharmacological Interventions for the Treatment of Acute Bipolar Mania in Adults.

In a network meta-analysis (NMA), multiple treatments can be compared simultaneously by aggregating pieces of evidence from direct as well as indirect treatment comparisons in different randomized controlled trials (RCTs). Conventional NMA are performed using a normal approximation approach and can be applied for arm-level binary outcome data as well. This study aimed to estimate the treatment effects within a Bayesian framework using a binomial likelihood for a multivariate NMA model. The dataset consists of 57 RCTs comparing the effect of ten pharmacological drugs and a placebo for acute bipolar mania in adults. The binary outcomes of interest were treatment response and all-cause dropouts measured three weeks from the baseline. Binomial distribution was adopted for the number of events and the probability of event occurrence modeled on the logit scale. Jeffrey's Beta prior was considered for the heterogeneity and inconsistency of standard deviation (SD) parameters. Cholesky and spherical decomposition strategies were adopted for the between-study variance-covariance matrix. Deviance information criterion (DIC) indices were computed to determine the model fit. All results pertaining to Markov chain Monte Carlo simulations and all analyses were carried out in WinBUGS software. The estimated common heterogeneity SDs were similar, and the DIC values did not provide any evidence for superiority between the two decomposition strategies. The correlation (95% credible interval) between the outcomes was estimated as -0.31 (-0.71, -0.02) and -0.37 (-0.73, -0.03) for the Cholesky and spherical decompositions, respectively. Gelman-Rubin convergence statistics were stable, and Monte Carlo errors for all the parameters were around 0.005. Overall, olanzapine, paliperidone, and quetiapine were both significantly more effective and acceptable than a placebo when both the study outcomes were considered simultaneously. The findings favoring olanzapine, paliperidone, and quetiapine possess an excellent concordance with the one adopted in clinical practice, and the Canadian Network for Mood and Anxiety Treatments and Royal Australian and New Zealand College of Psychiatrists guidelines recommend these as first-line drugs for treating bipolar disorder.