Trends in the utilisation of emergency departments in California, 2005-2015: a retrospective analysis.

ObjectiveTo examine current trends in the characteristics of patients visiting California emergency departments (EDs) in order to better direct the allocation of acute care resources.DesignA retrospective study.SettingWe analysed ED utilisation trends between 2005 and 2015 in California using non-public patient data from California's Office of Statewide Health Planning and Development.ParticipantsWe included all ED visits in California from 2005 to 2015.Primary and secondary outcome measuresWe analysed ED visits and visit rates by age, sex, race/ethnicity, payer and urban/rural trends. We further examined age, sex, race/ethnicity and urban/rural trends within each payer group for a more granular picture of the patient population. Additionally, we looked at the proportion of patients admitted from the ED and distribution of diagnoses.ResultsBetween 2005 and 2015, the annual number of ED visits increased from 10.2 to 14.2 million in California. ED visit rates increased by 27.8% (p<0.001), with the greatest increases among patients aged 5-19 (37.4%, p<0.001) and 45-64 years (41.1%, p<0.001), non-Hispanic Black and Hispanic patients (56.8% and 48.8%, p<0.001), the uninsured and Medicaid-insured (36.1%, p=0.002; 28.6%, p<0.001) and urban residents (28.3%, p<0.001). The proportion of ED visits resulting in hospitalisation decreased by 18.3%, with decreases across all payer groups.ConclusionsOur findings reveal an increasing demand for emergency care and may reflect current limitations in accessing care in other parts of the healthcare system. Policymakers may need to recognise the increasingly vital role that EDs are playing in the provision of care and consider ways to incorporate this changing reality into the delivery of health services

Experimental Verification of an Inversion Algorithm for Flaw Characterization Using Eddy-Currents

In this paper we report the results of an experimental verification of our inversion algorithm in [1,2] for the reconstruction of flaws within tube walls. The intent of the algorithm is to improve the quantification of flaws by determining their shape as well as size. This is done by “imaging” the flaw on a spatial grid by computing the values of electrical conductivities that are to be assigned to each cell or pixel of the grid. This will give us the ability to obtain much more information about the nature of the flaw when compared with current methods

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects, , for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value of has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST)

A Novel Survey Tool to Quantify the Degree and Duration of STEMI Regionalization Across California.

IntroductionCalifornia has been a global leader in regionalization efforts for time-critical medical conditions. A total of 33 local emergency medical service agencies (LEMSAs) exist, providing an organized EMS framework across the state for almost 40 years. We sought to develop a survey tool to quantify the degree and duration of ST-elevation myocardial infarction (STEMI) regionalization over the last decade in California.MethodsThe project started with the development of an 8-question survey tool via a multi-disciplinary expert consensus process. Next, the survey tool was distributed at the annual meeting of administrators and medical directors of California LEMSAs to get responses valid through December, 2014. The first scoring approach was the Total Regionalization Score (TRS) and used answers from all 8 questions. The second approach was called the Core Score, and it focused on only 4 survey questions by assuming that the designation of STEMI Receiving Centers must have occurred at the beginning of any LEMSA's regionalization effort. Scores were ranked and grouped into tertiles.ResultsAll 33 LEMSAs in California participated in this survey. The TRS ranged from 15 to 162. The Core Score range was much narrower, from 2 to 30. In comparing TRS and Core Score rankings, the top-tertiles were quite similar. More rank variation occurred between mid- and low-tertiles.ConclusionThis study evaluated the degree and duration of STEMI network regionalization from 2004 to 2014 in California, and ranked 33 LEMSAs into tertiles based upon their TRS and their Core Score. Successful application of the 8-item survey and ranking strategies across California suggests that this approach can be used to assess regionalization in other states or countries around the world

Improving saddle stitching line using affordable embedded system

In most printing factories, the stitching machine is considered as a significant tool in accomplishing the printing process cycle, such as in the Printing House of the University of Kufa (PHUK), complete their jobs using a cheap manual machine, and thus this leads to an increase in the number of employees and work hours. That is because the automated stitching machine of production is very costly. A decent printing house design maximizes production with a minimum investment in new equipment parts. However, a decent PHUK layout alone cannot reach the intended aims unless firmly linked with a developed production line of an automated stitching machine for the purpose of reducing cost, time, and efforts. This article focused on designing and developing automatic saddle stitching machines for folded paper sheet products such as newspapers, magazines, catalogs, exam sheets, etc. using accommodate devices such as Arduino and infrared sensors. Furthermore, the proposed design is applied in PHUK successfully and it showed that the cost of the stitching machine and the manpower is reduced by 60 percent, also the time is reduced by 70 percent. Finally, one of the significant implications of this work is using IT in management of resources

Modeling pulmonary alveolar microlithiasis by epithelial deletion of the Npt2b sodium phosphate cotransporter reveals putative biomarkers and strategies for treatment

Pulmonary alveolar microlithiasis (PAM) is a rare, autosomal recessive lung disorder associated with progressive accumulation of calcium phosphate microliths. Inactivating mutations in SLC34A2, which encodes the NPT2b sodiumdependent phosphate cotransporter, has been proposed as a cause of PAM.Weshow that epithelial deletion ofNpt2b in mice results in a progressive pulmonary process characterized by diffuse alveolar microlith accumulation, radiographic opacification, restrictive physiology, inflammation, fibrosis, and an unexpected alveolar phospholipidosis. Cytokine and surfactant protein elevations in the alveolar lavage and serum of PAM mice and confirmed in serum from PAM patients identify serum MCP-1 (monocyte chemotactic protein 1) and SP-D (surfactant protein D) as potential biomarkers.Microliths introduced by adoptive transfer into the lungs of wild-typemice produce markedmacrophagerich inflammation and elevation of serum MCP-1 that peaks at 1 week and resolves at 1 month, concomitant with clearance of stones. Microliths isolated by bronchoalveolar lavage readily dissolve in EDTA, and therapeutic wholelung EDTA lavage reduces the burden of stones in the lungs. A low-phosphate diet prevents microlith formation in young animals and reduces lung injury on the basis of reduction in serum SP-D. The burden of pulmonary calcium deposits in established PAM is also diminished within 4 weeks by a low-phosphate diet challenge. These data support a causative role for Npt2b in the pathogenesis of PAM and the use of the PAMmouse model as a preclinical platform for the development of biomarkers and therapeutic strategies

Birth prevalence of non-syndromic orofacial clefts in Saudi Arabia and the effects of parental consanguinity

Objectives: To describe the characteristics and prevalence of non-syndromic orofacial clefting (NSOFC) and assess the effects of parental consanguinity on NSOFC phenotypes in the 3 main cities of Saudi Arabia. Methods: All infants (114,035) born at 3 referral centers in Riyadh, and 6 hospitals in Jeddah and Madinah between January 2010 and December 2011 were screened. The NSOFC cases (n=133) were identified and data was collected through clinical examination and records, and information on consanguinity through parent interviews. The diagnosis was confirmed by reviewing medical records and contacting the infants’ pediatricians. Control infants (n=233) matched for gender and born in the same hospitals during the same period, were selected. Results: The prevalence of NSOFC was 1.07/1000 births in Riyadh, and 1.17/1000 births overall; cleft lip (CL) was 0.47/1000 births, cleft lip and palate (CLP) was 0.42/1000 births, and cleft palate (CP) was 0.28/1000 births. Cleft palate was significantly associated with consanguinity (p=0.047, odds ratio: 2.5, 95% confidence interval: 1 to 6.46), particularly for first cousin marriages. Conclusion: The birth prevalence of NSOFC in Riyadh alone, and in the 3 main cities of Saudi Arabia were marginally lower than the mean global prevalence. While birth prevalence for CLP was comparable to global figures, the CL:CLP ratio was high, and only CP was significantly associated with consanguinity

Exploration of the Equilibrium and Stability Properties of Spherical Tokamaks and Projection for MAST-U

In preparation for high fusion plasma performance operation of the newly operating spherical tokamak MAST-U, the equilibrium and stability properties of plasmas in the MAST database, as well as projections for MAST-U, are explored. The disruption event characterization and forecasting (DECAF) code is utilized to map disruptions in MAST, particularly with regard to vertical displacement events. Loss of vertical stability control was not found to be common in MAST, providing reassurance for MAST-U operation. MAST equilibria were reconstructed with magnetic diagnostics, adding kinetic diagnostics, or finally also adding magnetic pitch angle data. The reconstructions work well for MAST and the procedures are set up for MAST-U, including determination of the plasma current in the first MAST-U discharges. A 3D wall model of MAST-U has been constructed in the VALEN code, indicating that significant toroidal currents may be induced in the conducting structure. Rotation measurements may also be included in the reconstructions, and a test with the FLOW code of a rotating MAST plasma indicates a modest shift of the pressure contours off of the magnetic flux surfaces may be expected. Unstable resistive wall modes (RWMs) may constrain the performance of high pressure MAST-U plasmas. A machine learning (ML) assisted algorithm for stability calculation developed for the NSTX spherical tokamak has been applied to MAST plasmas. Improvements and expansion of the ML techniques continue, including semi-supervised learning techniques and a detection algorithm for unstable RWMs. Finally, projections of MAST-U plasma stability have been performed, indicating that a region of high pressure operational space exists in which the new passive stabilization plates act to stabilize ideal kink modes and RWMs may be stabilized by kinetic effects or active control