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Traditional three-dimensional numerical methods require a long time for transient computational fluid dynamics simulation on oil-filling process of hydrodynamic braking. The purpose of this paper is to investigate reconstruction and prediction methods for the pressure field on blade surfaces to explore an accurate and rapid numerical method to solve transient internal flow in a hydrodynamic retarder.

Dynamic braking performance for the oil-filling process was simulated and validated using experimental results. With the proper orthogonal decomposition (POD) method, the dominant modes of transient pressure distribution on blades were extracted using their spatio-temporal structural features from the knowledge of computed flow data. Pressure field on blades was reconstructed. Based on the approximate model (AM), transient pressure field on blades was predicted in combination with POD. The causes of reconstruction and prediction error were, respectively, analyzed.

Results show that reconstruction with only a few dominant POD modes could represent all flow samples with high accuracy. POD method demonstrates an efficient simplification for accurate prediction of the instantaneous variation of pressure field in a hydrodynamic retarder, especially at the stage of high oil-filling rate.

The paper presents a novel numerical method, which combines POD and AM approaches for rapid and accurate prediction of braking characteristics during the oil-filling period, based on the knowledge of computed flow data.

The purpose of this study is to propose a fast method for predicting flow fields with periodic behavior with verification in the context of a radial turbine to meet the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery. Aiming at meeting the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery, a fast method for predicting flow fields with periodic behavior is proposed here, with verification in the context of a radial turbine (RT).

Sparsity-promoting dynamic mode decomposition is used to determine the dominant coherent structures of the unsteady flow for mode selection, and for flow-field prediction, the characteristic parameters including amplitude and frequency are predicted using one-dimensional Gaussian fitting with flow rate and two-dimensional triangulation-based cubic interpolation with both flow rate and rotation speed. The flow field can be rebuilt using the predicted characteristic parameters and the chosen model.

Under single flow-rate variation conditions, the turbine flow field can be recovered using the first seven modes and fitted amplitude modulus and frequency with less than 5% error in the pressure field and less than 9.7% error in the velocity field. For the operating conditions with concurrent flow-rate and rotation-speed fluctuations, the relative error in the anticipated pressure field is likewise within an acceptable range. Compared to traditional numerical simulations, the method requires a lot less time while maintaining the accuracy of the prediction.

It would be challenging and interesting work to extend the current method to nonlinear problems.

The method presented herein provides an effective solution for the fast prediction of unsteady flow fields in the design of turbomachinery.

A flow prediction method based on sparsity-promoting dynamic mode decomposition was proposed and applied into a RT to predict the flow field under various operating conditions (both rotation speed and flow rate change) with reasonable prediction accuracy. Compared with numerical calculations or experiments, the proposed method can greatly reduce time and resource consumption for flow field visualization at design stage. Most of the physics information of the unsteady flow was maintained by reconstructing the flow modes in the prediction method, which may contribute to a deeper understanding of physical mechanisms.

The recently established SCOPE‐RADTEST (Scientific Committee on Problems of the Environment‐Radioactivity from Nuclear Test Explosions) programme is examining releases of radioactivity due to nuclear detonations which have occurred at various test sites around the world, for peaceful and military purposes, taking into consideration both ecological and human effects. Presents the background to this programme, together with a summary of the proceedings of RADTEST’s North Atlantic Treaty Organization Advanced Research Workshop meetings held during 1994 in Vienna, Austria, and Barnaul, Russia, and of the 1995 meeting in Brussels/Liège, Belgium.

Sampling taxpayers for audits has always been a major concern for policymakers of tax administration. The purpose of this study is to propose a systematic method to select a small number of taxpayers with a high probability of tax fraud.

An efficient sampling method for taxpayers for an audit is investigated in the context of a property acquisition tax. An autoencoder, a popular unsupervised learning algorithm, is applied to 2,228 tax returns, and reconstruction errors are calculated to determine the probability of tax deficiencies for each return. The reasonableness of the estimated reconstruction errors is verified using the Apriori algorithm, a well-known marketing tool for identifying patterns in purchased item sets.

The sorted reconstruction scores are reasonably consistent with actual fraudulent/non-fraudulent cases, indicating that the reconstruction errors can be utilized to select suspected taxpayers for an audit in a cost-effective manner.

The proposed deep learning-based approach is expected to be applied in a real-world tax administration, promoting voluntary compliance of taxpayers, and reinforcing the self-assessing acquisition tax system.

In answer to the urgent need to adapt conservation strategies and approaches to climate change, the purpose of this paper is to locate the climatically stable forests in West and Central Africa and to assess whether they overlap with the existing network of protected areas and if not, to prioritize them for protection.

With ongoing global warming, rain forest will survive where locally soil moisture content remains high compensating for the regional drought stress. As a proxy for a soil moisture‐driven model, rainfall >2,000 mm, altitude >500 m and strong relief (standard deviation in elevation data pixels) were overlapped in a GIS analysis to locate the climatically stable forest within the present continuous forest of Central Africa and within the degraded forest of West Africa. As a means of verification, the biodiversity was measured in and outside the identified areas in Gabon and Equatorial Guinea as high levels of biodiversity are related to the survival and stability of the forest in the past. Biodiversity was calculated (measured as Fisher‐α diversity) for all trees (dbh >5 cm) on 66 transects (200 × 5 m).

The forest areas identified as climatically stable in the GIS analysis showed a higher biodiversity than the forest outside these areas (student T‐test: P<0.000035, stable = 54.7 and unstable = 33.7), supporting the validity of the model. Mapping the results of the GIS query showed that most of the climatically stable forests in West and Central Africa are located outside the park systems, and that it is already too late to protect the climatically stable forest in West Africa as almost nothing is left of it.

Wedged in between large‐scale drought tolerant ecosystems the African rain forest is most vulnerable to global climate change. Knowing which parts are climatically stable and resilient helps to set and focus conservation priorities and efforts. This approach is a powerful tool which has helped to identify areas with a high‐conservation priority in Africa.

The convenience of online shopping enables the manufacturer to develop direct channels. To counter manufacturer encroachment, the retailer tends to provide presale service to attract more customers. Meanwhile, the service provided by the retailer also has a positive impact on the manufacturer's sale volume, which is usually called the showrooming effect or free-ride. The purpose of this paper is to explore the dynamic game of pricing and service strategy in a dual-channel supply chain with risk attitudes and free-ride.

This paper considers the risk attitude, characterized by mean-variance theory. First, the optimal pricing and service strategy of two static models under two scenarios are derived. Second, dynamic games are then considered to explore the evolution of the decisions. The classical optimization method is used to solve the problem, and numerical experiments are done to analyze the complex characteristic of the system.

The result shows that the retailer is willing to provide a higher level of service if his risk preference is higher. The offline retail price and online retail price are positively related to the retailer's risk preference. Besides, the free-ride behavior can reduce the offline retail price and the level of service provided by the retailer. Furthermore, the study indicates that the system is more likely to enter chaos if the retailer's risk preference is higher. Additionally, consumers' service sensitivity and cost coefficient affect the stability of the system.

The study provides a different perspective on supply chain management considering risk attitudes and free-ride The findings of the study can offer theoretical and practical guidance for enterprises to choose adjustment measures according to their risk preference.

The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.

To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.

The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.

The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.

The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.

This paper aims to improve performance prediction and to acquire more detailed flow structures so as to analyze the turbulence in complex rotor-stator flow.

Hydraulic retarder as typical fluid machinery was numerically investigated by using hybrid Reynolds-averaged Navier–Stokes (RANS)/large eddy simulation (LES) models CIDDES Algebraic Wall-Modeled Large Eddy Simulation (LES) (WMLES) S-Ω and dynamic hybrid RANS/LES (DHRL). The prediction results were compared and analyzed with a RANS model shear stress transport (SST) k-omega which was a recommended choice in engineering.

The numerical results were verified by experiment and indicated that the predicted values for three hybrid turbulence models were more accurate. Then, the transient flow field was further analyzed visually in terms of turbulence statistics, Reynolds number, pressure-streamline, vortex structure and eddy viscosity ratio. The results indicated that HRL approaches could capture unsteady flow phenomena.

This study achieves both in performance prediction improvement and better flow mechanism understanding. The computational fluid dynamics (CFD) could be used instead of flow visualization to a certain extent. The improved CFD method, the fine computational grid and the reasonable simulation settings jointly enhance the application of CFD in the rotor-stator flow.

The improvement was quite encouraging compared with the reported literatures, contributing to the CFD playing a more important role in the flow machinery. DHRL provided the detailed explanation of flow transport between rotor and stator, which was not reported before. Through it, the flow mechanism can be better understood.

The aim of this paper is to present and evaluate a methodology for automatically constructing and applying the physiologically‐realistic boundary/loading conditions for use in the structural finite element analysis of the femur during various exertion tasks (e.g. gait/walking).

To obtain physiologically‐realistic boundary/loading conditions needed in the femur structural finite element analysis, a whole‐body musculoskeletal inverse dynamics analysis is carried out and the resulting muscle forces and joint reaction forces/moments extracted.

The finite element results obtained are compared with their counterparts available in literature and it is found that the overall agreement is acceptable while the highly automated procedure for the finite element model generation developed in the present work made the analysis fairly easy and computationally highly efficient. Potential sources of errors in the current procedure have been identified and the measures for their mitigation recommended.

The present approach enables a more accurate determination of the physiological loads experienced by the orthopedic implants which can be of great value to implant designers and orthopedic surgeons.

This study aims at investigating the evolution of disaster management by identifying the different phases it has gone through over time, and laying a ground for the next generation of disaster studies that focus on value-creating and value-adding activities.

An extensive review of the existing literature was made to develop an understanding of the evolution of disaster management. This study does not aim at assessing the tools or techniques used; rather it aims at identifying the major developments that took place over time.

Disaster management is a dynamic process. It has encountered/experienced different evolutionary phases that indicate that it has been developing over time. It continues to evolve until today as long as disasters occur. The nature and complexity of disasters are also changing. Most importantly, what seemed to be a practical approach for managing disasters yesterday might not fit for the use of today or tomorrow.

Understanding the evolution of disaster management mirrors the evolution of mankind and the ways people survived major incidents. As life itself evolves, disasters will continue to evolve which subsequently triggers the need for broader management insight to cope with.

This study traces the evolution of disaster management and the development of research and practice in this field over time. The existing literature rarely addresses the uniqueness of individual disasters and the need to treat them differently even the recurrent ones. To the best of the author’s knowledge, there is no single study that attempted to capture the evolution of disaster management during the 20th century until today. This study aims to achieve this goal.