An idealized 1½-layer isentropic model with convection and precipitation for satellite data assimilation research. Part II: model derivation

In this part II paper we present a fully consistent analytical derivation of the ‘dry’ isentropic 1½-layer shallow water model described and used in part I of this study, with no convection and precipitation. The mathematical derivation presented here is based on a combined asymptotic and slaved Hamiltonian analysis which is used to resolve an apparent inconsistency arising from the application of a rigid-lid approximation to an isentropic two-layer shallow water model. Real observations based on radiosonde data are used to justify the scaling assumptions used throughout the paper, as well as in part I. Eventually, a fully consistent isentropic 1½-layer model emerges from imposing fluid at rest (v1 = 0) and zero Montgomery potential (M1 = 0) in the upper layer of an isentropic two-layer model.

Improve the Three-phase Unbalance Rate of Railway System Under 2% By Three Kinds of Special Transformer Wiring

Because imbalanced power will cause the loss of the propulsion motor of the railway vehicle, and the increase in temperature will shorten the service life of the electric vehicle. Not only this, but also increase the cost of electricity and maintenance. In the past, the industry only focused on methods to improve power quality such as load capacity, relay setting, and harmonic resolution. Now, the consider of three-phase unbalance rate (TPUR) must be applied. I propose special transformers wiring (STW) to improve the three unbalance rates and provide different transformer wiring methods. According to the IEEE Committee, in the future, power companies will need to install balanced relay stations to improve three-phase unbalance rate. the internal regulations of Taipower must be less than 4.5% (voltage unbalance rate (NPSUR)of 2.5% and motor temperature rise of 12.5%). the derivation of the transformer "three-phase unbalance rate" model is the focus of the railway system. This research is based on the model derivation of different wiring methods to improve the hot problem caused by the three-phase imbalance and improve the service life of the train. And pointed out that Scott, Le-Blanc, Modified-Woodbridge three wiring methods can be applied to future railway system routes to improve the three-phase unbalance rate, in line with the IEEE standard of less than 2%.

Improve the Three-Phase Unbalance Rate of Railway System Under 2% By Three Kinds of Special Transformer Wiring

Because imbalanced power will cause the loss of the propulsion motor of the railway vehicle, and the increase in temperature will shorten the service life of the electric vehicle. Not only this, but also increase the cost of electricity and maintenance. In the past, the industry only focused on methods to improve power quality such as load capacity, relay setting, and harmonic resolution. Now, the consider of three-phase unbalance rate (TPUR) must be applied. I propose special transformers wiring (STW) to improve the three unbalance rates and provide different transformer wiring methods. According to the IEEE Committee, in the future, power companies will need to install balanced relay stations to improve three-phase unbalance rate. the internal regulations of Taipower must be less than 4.5% (voltage unbalance rate (NPSUR)of 2.5% and motor temperature rise of 12.5%). the derivation of the transformer "three-phase unbalance rate" model is the focus of the railway system. This research is based on the model derivation of different wiring methods to improve the hot problem caused by the three-phase imbalance and improve the service life of the train. And pointed out that Scott, Le-Blanc, Modified-Woodbridge three wiring methods can be applied to future railway system routes to improve the three-phase unbalance rate, in line with the IEEE standard of less than 2%.

The Development of a Partially Averaged Navier-Stokes KSKL Model

A new partially averaged Navier-Stokes (PANS) closure is derived based on the KSKL model. The aim of this new model is to incorporate the desirable features of the KSKL model, compared to the SST model, into the PANS framework. These features include reduced eddy-viscosity levels, a lower dependency on the cell height at the wall, well-defined boundary conditions, and improved iterative convergence. As well as the new model derivation, the paper demonstrates that these desirable features are indeed maintained, for a range of modeled-to-total turbulence kinetic energy ratios (f_k), and even for multiphase flow.

Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

There are few laws and regulations related to privacy protection in the existing artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects my country’s current weak protection of big data privacy. In order to solve the problem of sharing artificial intelligence data and algorithms, it becomes very important to study the legal protection of artificial intelligence data and algorithms from the perspective of Internet of Things resource sharing. This article is aimed at studying the use of laws to protect artificial intelligence data and algorithms. Aiming at reducing the bullwhip effect, a most effective bullwhip effect model derivation algorithm is proposed. This method can not only share customer demand information with members at all levels in the supply chain but also achieve information sharing among members at all levels. Calculate the proportion of the overall time of the program through multiple statistical data ( m = 30 , k = 12 ; and m = 60 , k = 15 ), and extract two special values representing the overall situation ( m = 30 , k = 12 ; m = 60 , k = 15 ). Most of the time consumption of this program is concentrated in the secret distribution stage, accounting for about 80% on average.

CORONET; COVID-19 in Oncology evaluatiON Tool: Use of machine learning to inform management of COVID-19 in patients with cancer.

1505 Background: Patients (pts) with cancer are at increased risk of severe COVID-19 infection and death. Due to COVID-19 outcome heterogeneity, accurate assessment of pts is crucial. Early identification of pts who are likely to deteriorate allows timely discussions regarding escalation of care. Likewise, safe home management will reduce risk of nosocomial infection. To aid clinical decision-making, we developed a model to help determine which pts should be admitted vs. managed as an outpatient and which pts are likely to have severe COVID-19. Methods: Pts with active solid or haematological cancer presenting with symptoms/asymptomatic and testing positive for SARS-CoV-2 in Europe and USA were identified following institutional board approval. Clinical and laboratory data were extracted from pt records. Clinical outcome measures were discharge within 24 hours, requirement for oxygen at any stage during admission and death. Random Forest (RF) algorithm was used for model derivation as it compared favourably vs. lasso regression. Relevant clinical features were identified using recursive feature elimination based on SHAP. Internal validation (bootstrapping) with multiple imputations for missing data (maximum ≤2) were used for performance evaluation. Cost function determined cut-offs were defined for admission/death. The final CORONET model was trained on the entire cohort. Results: Model derivation set comprised 672 pts (393 male, 279 female, median age 71). 83% had solid cancers, 17% haematological. Predictive features were selected based on clinical relevance and data availability, supported by recursive feature elimination based on SHAP. RF model using haematological cancer, solid cancer stage, no of comorbidities, National Early Warning Score 2 (NEWS2), neutrophil:lymphocyte ratio, platelets, CRP and albumin achieved AUROC for admission 0.79 (+/-0.03) and death 0.75 (+/-0.02). RF explanation using SHAP revealed NEWS2 and C-reactive protein as the most important features predicting COVID-19 severity. In the entire cohort, CORONET recommended admission of 96% of patients requiring oxygen and 99% of patients who died. We then built a decision support tool using the model, which aids clinical decisions by presenting model predictions and explaining key contributing features. Conclusions: We have developed a model and tool available at https://coronet.manchester.ac.uk/ to predict which pts with cancer and COVID-19 require hospital admission and are likely to have a severe disease course. CORONET is being continuously refined and validated over time.

Dynamic Modeling and CAE Cosimulation Method for Heavy-Duty Concrete Spreader

This paper presents the dynamic model of heavy-duty concrete spreader with liquid-solid rigid-flexible coupling by means of mathematical modeling and CAE cosimulation. The mathematical method of liquid-solid dynamic model of heavy-duty concrete spreader is described. Based on the liquid-solid coupling system, two degrees of freedom are added to change the model into a liquid-solid rigid-flexible coupling model, and the calculation process of the model is given in detail. The results show that, considering two flexible body factors, the solution scale is relatively large and the complexity of mathematical model derivation is increased. It is very difficult to establish a general dynamic equation which can be easily solved by computer. Therefore, this paper presents a new method of CAE cosimulation of liquid-solid rigid-flexible coupling. This method is divided into two parts: the computer simulation process of liquid-solid coupling and the computer simulation process of rigid-flexible coupling. First, the fluid-solid coupling is carried out by COMSOL software, and then the rigid-flexible coupling is carried out by HyperMesh software, Ansys software, and Adams software. This method can easily establish the dynamic model of the liquid-solid rigid-flexible coupling system, which provides a new idea for the simulation of heavy-duty concrete spreader. The simulation results can provide valuable insights into product design and structural optimization.