Agricultural Policy Simulation Based on Computable General Equilibrium Model

[Objective] This study aimed to examine the simulated effect of Computable General Equilibrium （CGE）-based agricultural policy simulation system. [Method] The policy simulation platform based on CGE model was constructed by integrating policy simulation, CGE model and Decision Supporting System （DSS）. The scenario analysis method was used to analyze the agricultural subsides policy simulation through empirical analysis. [Result] Farmers were the main beneficiaries of increasing agricultural production subsidies, which increased farmers＇ income and improved the export of agriculture products. The prototype system could solve the problems in actual policy simulation. [Conclusion] The results lay the foundation for the quantitative study on agricultural subsidy policy in China.

Intensity Allocation Criteria of Carbon Emissions Permits and Regional Economic Development in China——Based on a 30-Province/Autonomous Region Computable General Equilibrium Model

The intensity allocation criteria of carbon emissions permits and its influence on China＇s regional development are analyzed through the 30-province/autonomous region computable general equilibrium （CGE） model. Simulation results show that： industrial intensity criteria without taking regional economic development into account deepen the unbalance of regional economic development; regional intensity criteria without taking industrial properties into account exert little negative impact on regional harmonious development, but relatively high negative influence on high-carbon emission industries. The two-step allocation scheme that the central government allocates emissions permits to provincial governments based on regional economic development and then provincial governments allocate emissions permits to emission resources or entities based on industrial properties is a feasible and operable choice.

The Impact of Climate Change on Agriculture Production in Ethiopia: Application of a Dynamic Computable General Equilibrium Model

The challenge of meeting the ever-increasing food demand for the growing population will be further exacerbated by climate change in Ethiopia. This paper presents the simulated economy-wide impacts of climate change on the agriculture sector of Ethiopia using a dynamic computable general equilibrium (CGE) model. The study simulated the scenarios of agricultural productivity change induced by climate change up to the year 2050. At national level, the simulation results suggest that crop production will be adversely affected during the coming four decades and the severity will increase over the time period. Production of teff, maize and sorghum will decline by 25.4, 21.8 and 25.2 percent, respectively by 2050 compared to the base period. Climate change will also cause losses of 31.1 percent agricultural GDP at factor cost by 2050. Climate change affects more the income and consumption of poor rural households than urban rural non-farming households. The reduction in agricultural production will not be evenly distributed across agro ecological zones, and will not all be negative. Among rural residents, climate change impacts tend to hurt the income of the poor more in drought prone regions. Income from labor, land and livestock in moisture sufficient highland cereal-based will decline by 5.1, 8.8 and 15.2 percent in 2050. This study indicated that since climate change is an inevitable phenomenon, the country should start mainstreaming adaptation measures to sustain the overall performance of the economy.

Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

Development of multiple soft computing models for estimating organic and inorganic constituents in coal

The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.

Gastrointestinal tract modelling in health and disease

The gastrointestinal (GI) tract is the system of organs within multi-cellular animals that takes in food, digests it to extract energy and nutrients, and expels the remaining waste. The various patterns of GI tract function are generated by the integrated behaviour of multiple tissues and cell types. A thorough study of the GI tract requires understanding of the interactions between cells, tissues and gastrointestinal organs in health and disease. This depends on knowledge, not only of numerous cellular ionic current mechanisms and signal transduction pathways, but also of large scale GI tissue structures and the special distribution of the nervous network. A unique way of coping with this explosion in complexity is mathematical and computational modelling; providing a computational framework for the multilevel modelling and simulation of the human gastrointestinal anatomy and physiology. The aim of this review is to describe the current status of biomechanical modelling work of the GI tract in humans and animals, which can be further used to integrate the physiological, anatomical and medical knowledge of the GI system. Such modelling will aid research and ensure that medical professionals benefit, through the provision of relevant and precise information about the patient's condition and GI remodelling in animal disease models. It will also improve the accuracy and efficiency of medical procedures, which could result in reduced cost for diagnosis and treatment.

Cancer models in preclinical research:A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.

A review of computational modeling and deep brain stimulation:applications to Parkinson’s disease

Biophysical computational models are complementary to experiments and theories,providing powerful tools for the study of neurological diseases.The focus of this review is the dynamic modeling and control strategies of Parkinson’s disease(PD).In previous studies,the development of parkinsonian network dynamics modeling has made great progress.Modeling mainly focuses on the cortex-thalamus-basal ganglia(CTBG)circuit and its sub-circuits,which helps to explore the dynamic behavior of the parkinsonian network,such as synchronization.Deep brain stimulation(DBS)is an effective strategy for the treatment of PD.At present,many studies are based on the side effects of the DBS.However,the translation from modeling results to clinical disease mitigation therapy still faces huge challenges.Here,we introduce the progress of DBS improvement.Its specific purpose is to develop novel DBS treatment methods,optimize the treatment effect of DBS for each patient,and focus on the study in closed-loop DBS.Our goal is to review the inspiration and insights gained by combining the system theory with these computational models to analyze neurodynamics and optimize DBS treatment.

A Unified Framework of the Cloud Computing Service Model

After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.

Computational modelling of magnesium degradation in simulated body fluid under physiological conditions

Magnesium alloys are highly attractive for the use as temporary implant materials, due to their high biocompatibility and biodegradability.However, the prediction of the degradation rate of the implants is difficult, therefore, a large number of experiments are required. Computational modelling can aid in enabling the predictability, if sufficiently accurate models can be established. This work presents a generalized model of the degradation of pure magnesium in simulated body fluid over the course of 28 days considering uncertainty aspects. The model includes the computation of the metallic material thinning and is calibrated using the mean degradation depth of several experimental datasets simultaneously. Additionally, the formation and precipitation of relevant degradation products on the sample surface is modelled, based on the ionic composition of simulated body fluid. The computed mean degradation depth is in good agreement with the experimental data(NRMSE=0.07). However, the quality of the depth profile curves of the determined elemental weight percentage of the degradation products differs between elements(such as NRMSE=0.40 for phosphorus vs. NRMSE=1.03 for magnesium). This indicates that the implementation of precipitate formation may need further developments. The sensitivity analysis showed that the model parameters are correlated and which is related to the complexity and the high computational costs of the model. Overall, the model provides a correlating fit to the experimental data of pure Mg samples of different geometries degrading in simulated body fluid with reliable error estimation.

Numerical Simulation of Blood Flow in Aorta with Dilation:A Comparison between Laminar and LES Modeling Methods

Computational modeling methods have been increasingly employed to quantify aortic hemodynamic parameters that are challenging to in vivo measurements but important for the diagnosis/treatment of aortic disease.Although the presence of turbulence-like behaviors of blood flow in normal or diseased aorta has long been confirmed,the majority of existing computational model studies adopted the laminar flow assumption(LFA)in the treatment of sub-grid flow variables.So far,it remains unclear whether LFA would significantly compromise the reliability of hemodynamic simulation.In the present study,we addressed the issue in the context of a specific aortopathy,namely aortic dilation,which is usually accompanied by disturbed flow patterns.Three patient-specific aortas with treated/untreated dilation of the ascending segment were investigated,and their geometrical models were reconstructed from computed tomography angiographic images,with the boundary conditions being prescribed based on flow velocity information measured in vivo with the phase contrast magnetic resonance imaging technique.For the modeling of blood flow,apart from the traditional LFA-based method in which sub-grid flow dynamics is ignored,the large eddy simulation(LES)method capable of incorporating the dissipative energy loss induced by turbulent eddies at the sub-grid level,was adopted and taken as a reference for examining the performance of the LFA-based method.Obtained results showed that the simulated large-scale flow patterns with the two methods had high similarity,both agreeing well with in vivo measurements,although locally large between-method discrepancies in computed hemodynamic quantities existed in regions with high intensity of flow turbulence.Quantitatively,a switch from the LES to the LFAbased modeling method led to mild(<6%)changes in computed space-averaged wall shear stress metrics(i.e.,SA-TAWSS,SA-OSI)in the ascending aortic segment where intensive vortex evolution accompanied by high statistical Reynolds stress was observed.In addition,comparisons among the three aortas revealed that the treatment status of aortic dilation or the concomitant presence of aortic valve disease,despite its remarkable influence on flow patterns in the ascending aortic segment,did not significantly affect the degrees of discrepancies between the two modeling methods in predicting SA-TAWSS and SA-OSI.These findings suggest that aortic dilation per se does not induce strong flow turbulence that substantially negates the validity of LFA-based modeling,especially in simulating macro-scale hemodynamic features.

Potential Maintenance Mechanism in Atrial Fibrillation Patients Subject to Different Treatment Procedures： A Preliminary Study Based on a Human Atrial Model

Two clinical ablation protocols, 2C3L and stepwise, have been routinely used in our group to treat atrial fibrillation （AF）, but with a less than 60% long-term arrhythmia-free outcome achieved in patients. The goal of this study was to examine the underlying mechanism of low success in clinical outcome. MRI images from one patient were used to reconstruct a human atrial anatomical model, and fibrotic tissue was manually added to represent the arrhythmia substrate. AF was induced with standard protocols used in clinical practice. 2C3L and stepwise were then used to test the efficacy of arrhythmia termination in our model. The results showed that re-entries induced in our model could not be terminated by using either 2C3L or the stepwise protocol. Although some of the induced re-entries were terminated, others emerged in new areas. Ablation using only the 2C3L or stepwise method was not sufficient to terminate all re-entries in our model, which may partially explain the poor long-term arrhythmiafree outcomes in clinical practice. Our findings also suggest that computational heart modelling is an important tool to assist in the establishment of optimal ablation strategies.

Comparision of China's volatile organic compound pollution management:a computable general equilibrium approach

The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the effectiveness of VOC reduction policies,namely pollution charges and environmental taxes at the national and industrial sector levels.It uses a computable general equilibrium model,which connects macroeconomic variables with VOC emissions inventory,to simulate the effects of policy scenarios(with 2007 as the reference year).This paper shows that VOC emissions are reduced by 2.2% when a pollution charge equal to the average cost of engineering reduction methods-the traditional approach to regulation in China-is applied.In order to achieve a similar reduction,an 8.9% indirect tax would have to be imposed.It concludes that an environmental tax should be the preferred method of VOC regulation due to its smaller footprint on the macroeconomy.Other policies,such as subsidies,should be used as supplements.

Numerical simulation of neuronal spike patterns in a retinal network model

This study utilized a neuronal compartment model and NEURON software to study the effects of external light stimulation on retinal photoreceptors and spike patterns of neurons in a retinal network Following light stimulation of different shapes and sizes, changes in the spike features of ganglion cells indicated that different shapes of light stimulation elicited different retinal responses. By manipulating the shape of light stimulation, we investigated the effects of the large number of electrical synapses existing between retinal neurons. Model simulation and analysis suggested that interplexiform cells play an important role in visual signal information processing in the retina, and the findings indicated that our constructed retinal network model was reliable and feasible. In addition, the simulation results demonstrated that ganglion cells exhibited a variety of spike patterns under different light stimulation sizes and different stimulation shapes, which reflect the functions of the retina in signal transmission and processing.

An In Vivo Experimental Validation of a Computational Model of Human Foot

Reliable computational foot models offer an alternative means to enhance knowledge on the biomechanics of human foot. Model validation is one of the most critical aspects of the entire foot modeling and analysis process.This paper presents an in vivo experiment combining motion capture system and plantar pressure measure platform to validate a three-dimensional finite element model of human foot.The Magnetic Resonance Imaging(MRI)slices for the foot modeling and the experimental data for validation were both collected from the same volunteer subject.The validated components included the comparison of static model predictions of plantar force,plantar pressure and foot surface deformation during six loading conditions,to equivalent measured data.During the whole experiment,foot surface deformation,plantar force and plantar pressure were recorded simultaneously during six different loaded standing conditions.The predictions of the current FE model were in good agreement with these experimental results.

Damage model of fresh concrete in sulphate environment

A model of damage to fresh concrete in a corrosive sulphate environment was formulated to investigate how and why the strength of corroded concrete changes over time. First, a corroded concrete block was divided into three regions: an expanded and dense region; a crack-development region; and a noncorroded region. Second, based on the thickness of the surface corrosion layer and the rate of loss of compressive strength of the corroding region, a computational model of the concrete blocks' corrosion-resistance coefficient of compressive strength in a sulphate environment was generated. Third, experimental tests of the corrosion of concrete were conducted by immersing specimens in a corrosive medium for 270 d. A comparison of the experimental results with the computational formulae shows that the calculation results and test results are in good agreement. A parameter analysis reveals that the corrosion reaction plays a major role in the corrosion of fresh concrete containing ordinary Portland cement,but the diffusion of the corrosion medium plays a major role in the corrosion of concrete mixtures containing fly ash and sulphate-resistant cement. Fresh concrete with a high water-to-cement ratio shows high performance during the whole experiment process whereas fresh concrete with a low water-to-cement ratio shows poor performance during the late experiment period.

What can computational modeling offer for studying the Ca^(2+) dysregulation in Alzheimer's disease:current research and future directions

Ca^2＋ dysregulation is an early event observed in Alzheimer＇s disease（AD） patients preceding the presence of its clinical symptoms.Dysregulation of neuronalCa^2＋ will cause synaptic loss and neuronal death,eventually leading to memory impairments and cognitive decline.Treatments targetingCa^2＋ signaling pathways are potential therapeutic strategies against AD.The complicated interactions make it challenging and expensive to study the underlying mechanisms as to how Ca^2＋ signaling contributes to the pathogenesis of AD.Computational modeling offers new opportunities to study the signaling pathway and test proposed mechanisms.In this mini-review,we present some computational approaches that have been used to study Ca^2＋ dysregulation of AD by simulating Ca^2＋signaling at various levels.We also pointed out the future directions that computational modeling can be done in studying the Ca^2＋ dysregulation in AD.

Modeling the effects of mechanical parameters on the hydrodynamic behavior of vertical current classifiers

This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics （CFD） approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet＇s weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing.

Flow Field and Temperature Field in GaN-MOCVD Reactor Based on Computational Fluid Dynamics Modeling

Metal organic chenlical vapor deposition （AIOCVD） growth systems arc one of the. main types of equipment used for growing single crystal materials, such as GaN. To obtain fihn epitaxial materials with uniform performanee, the flow field and ternperature field in a GaN-MOCVD reactor are investigated by modeling and simulating. To make the simulation results more consistent with the actual situation, the gases in the reactor are considered to be compressible, making it possible to investigate the distributions of gas density and pressure in the reactor. The computational fluid dynamics method is used to stud,v the effects of inlet gas flow velocity, pressure in the reactor, rotational speed of graphite susceptor, and gases used in the growth, which has great guiding~ significance for the growth of GaN fihn materials.

The rough representation and measurement of quotient structure in algebraic quotient space model

Granular computing is a very hot research field in recent years. In our previous work an algebraic quotient space model was proposed,where the quotient structure could not be deduced if the granulation was based on an equivalence relation. In this paper,definitions were given and formulas of the lower quotient congruence and upper quotient congruence were calculated to roughly represent the quotient structure. Then the accuracy and roughness were defined to measure the quotient structure in quantification. Finally,a numerical example was given to demonstrate that the rough representation and measuring methods are efficient and applicable. The work has greatly enriched the algebraic quotient space model and granular computing theory.