Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.

Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

1 Introduction.With the continuous growth of the global population,the energy demand continues to increase.However,due to the dominance of fossil fuels in global energy and fossil fuels are non-renewable,it has led to the global energy crisis[1].Besides,the use of fossil fuels will generate a mass of air pollutants(e.g.,carbon dioxide,sulfur dioxide,etc.),which will cause serious environmental pollution,climate change[2],etc.To resolve the aforementioned issues,countries around the world have implemented a variety of measures hoping to fundamentally adjust the global energy structure and achieve sustainable development.Thereinto,“Paris Agreement”reached in 2015 under the framework of“United Nations Framework Convention on Climate Change”aims to control the increase in the average temperature of the globe to within 2°C below preindustrial levels,and thereafter to peak global greenhouse gas emissions as soon as possible,continuously decreasing thereafter[3].United Kingdom plans to reduce the average exhaust emissions of“new cars”to approximately 50–70 g/km by 20230,which is roughly half of what it is now[4].In addition,China proposed a plan at“United Nations General Assembly”in 2020 to peak carbon dioxide emissions by 2030 and strive to achieve carbon neutrality by 2060.It is a fact that the whole world is committed to changing the current energy structure,protecting the Earth’s ecology,and achieving global sustainable development[5].

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Research and Practice of the Blended Teaching Model of BOPPPS Teaching Method Under the Background of Digital Education: Taking Operations Research Course as an Example

The rapid development of digital education provides new opportunities and challenges for teaching model innovation.This study aims to explore the application of the BOPPPS(Bridge-in,Objective,Pre-assessment,Participatory learning,Post-assessment,Summary)teaching method in the development of a blended teaching model for the Operations Research course under the background of digital education.In response to the characteristics of the course and the needs of the student group,the teaching design is reconstructed with a student-centered approach,increasing practical teaching links,improving the assessment and evaluation system,and effectively implementing it in conjunction with digital educational technology.This teaching model has shown significant effectiveness in the context of digital education,providing valuable experience and insights for the innovation of the Operations Research course.

Energy model based sensorless estimation method for operational temperature of braking resistor onboard metro vehicles

Purpose–This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor(BR)onboard the vehicle,which overcomes the vulnerability of having conventional temperature sensor.Design/methodology/approach–In this study,the energy model based sensorless estimation method is developed.By analyzing the structure and the convection dissipation process of the BR onboard the vehicle,the energy-based operational temperature model of the BR and its cooling domain is established.By adopting Newton’s law of cooling and the law of conservation of energy,the energy and temperature dynamic of the BR can be stated.To minimize the use of all kinds of sensors(including both thermal and electrical),a novel regenerative braking power calculation method is proposed,which involves only the voltage of DC traction network and the duty cycle of the chopping circuit;both of them are available for the traction control unit(TCU)of the vehicle.By utilizing a real-time iterative calculation and updating the parameter of the energy model,the operational temperature of the BR can be obtained and monitored in a sensorless manner.Findings–In this study,a sensorless estimation/monitoring method of the operational temperature of BR is proposed.The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR,instead of adding dedicated thermal sensors.The results also validate the effectiveness of the proposal is acceptable for the engineering practical.Originality/value–The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks.The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

The Water-Bearing Numerical Model and Its Operational Forecasting Experiments PartII: The Operational Forecasting Experiments

おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Numerical Analysis on Temperature Distribution in a Single Cell of PEFC Operated at Higher Temperature by1D Heat Transfer Model and 3D Multi-Physics Simulation Model

This study is to understand the impact of operating conditions, especially initial operation temperature (Tini) which is set in a high temperature range, on the temperature profile of the interface between the polymer electrolyte membrane (PEM) and the catalyst layer at the cathode (i.e., the reaction surface) in a single cell of polymer electrolyte fuel cell (PEFC). A 1D multi-plate heat transfer model based on the temperature data of the separator measured using the thermograph in a power generation experiment was developed to evaluate the reaction surface temperature (Treact). In addition, to validate the proposed heat transfer model, Treact obtained from the model was compared with that from the 3D numerical simulation using CFD software COMSOL Multiphysics which solves the continuity equation, Brinkman equation, Maxwell-Stefan equation, Butler-Volmer equation as well as heat transfer equation. As a result, the temperature gap between the results obtained by 1D heat transfer model and those obtained by 3D numerical simulation is below approximately 0.5 K. The simulation results show the change in the molar concentration of O2 and H2O from the inlet to the outlet is more even with the increase in Tini due to the lower performance of O2 reduction reaction. The change in the current density from the inlet to the outlet is more even with the increase in Tini and the value of current density is smaller with the increase in Tini due to the increase in ohmic over-potential and concentration over-potential. It is revealed that the change in Treact from the inlet to the outlet is more even with the increase in Tini irrespective of heat transfer model. This is because the generated heat from the power generation is lower with the increase in Tini due to the lower performance of O2 reduction reaction.

Development of Operational Space Weather Prediction Models

In this report, we summarize the needs of space weather models, and recommend that developing operational prediction models, rather than transitioning from research to operation, is a more feasible and critical way for space weather services in the near future. Operational models for solar wind speed, geomagnetic indices, magnetopause, plasma sheet energetic electrons, inner boundary of ion plasma sheet, energetic electrons in outer radiation belt, and thermospheric density at low Earth orbit, have been developed and will be introduced briefly here. Their applications made a big progress in space weather services during the past two years in China.

Operational Model for Evaluating the Permeation of Mixed Gas Through Poly(dimethylsiloxane) Membrane

An operational model is developed to evaluate and predict the permeation performance of mixed gas through poly(dimethylsiloxane) (PDMS) membranes by combining the ideal gas permeation model with the ex-perimental analysis of the mixed gas transport character. This model is tested using the binary and ternary mixed gas with various compositions through the PDMS membranes, and the predicted data of the permeation flux and the compositions of the permeated gas are in good agreement with the experimental ones, which indicates that the op-erational model is applicable for the evaluation of the permeation performance of mixed gas through PDMS mem-branes.

Effects of spot size on the operation mode of Ga As photoconductive semiconductor switch employing extrinsic photoconductivity

To guide the illuminating design to improve the on-state performances of gallium arsenide(GaAs)photoconductive semiconductor switch(PCSS),the effect of spot size on the operation mode of GaAsPCSS based on a semi-insulating wafer with a thickness of 1 mm,triggered by a 1064-nm extrinsic laser beam with the rectangular spot,has been investigated experimentally.It is found that the variation of the spot size in length and width can act on the different parts of the output waveform integrating the characteristics of the linear and nonlinear modes,and then significantly boosts the PCSS toward different operation modes.On this basis,a two-channel model containing the active and passive parts is introduced to interpret the relevant influencing mechanisms.Results indicate that the increased spot length can peak the amplitude of static domains in the active part to enhance the development of the nonlinear switching,while the extended spot width can change the distribution of photogenerated carriers on both parts to facilitate the linear switching and weaken the nonlinear switching,which have been proved by comparing the domain evolutions under different spot sizes.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Impact Damage Testing Study of Shanxi-Beijing Natural Gas Pipeline Based on Decision Tree Rotary Tiller Operation

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Triptych Knowledge—Skills—Payroll and Business Performance: Application of an Operational Model for an Accounting Rating of the Human Capital in the Area of Distribution

The human capital’s accounting could allow companies to better manage the acquired knowledge and the skills developed by the employees when they join the firm.An operational model,suitable for a business enterprise’s HR manager and validated by its headboard,needs to include the employees’expertise and skills relative to their“cost”for the employer—the wage bill—and an assessment of the commercial performance.The human capital’s valuation includes methodological issues.Besides,the notion of human capital seems hardly understandable as a whole:The human capital gathers nevertheless components,such as knowledge—skills for which a first valuation can be proposed to test an accounting evaluation model for the operational human capital.In a perspective of a responsible management and a good HR policy,the method used must be able to better manage the knowledge and the competences employees acquired by accompanying them with the appropriate human resource management practices.This paper aims to show that the accounting valuation of human capital can become a tool in order to manage the knowledge and skills acquired and able to support a company’s human resources policy while being useful to its commercial performance—here in the distribution sector.In a research-intervention frame led in a responsible group,we use a model based on a triptych—wage bill,knowledge,and skills—to evaluate the human capital’s accounting,with an analytical highlight on the components measurement of the used“knowledge”and“skills”indexes in particular.A reflection on the operational model’s enrichment is proposed.

The Operation Mechanism of Agricultural Products Supply Chain and Profit Allocation Model in the Context of Asymmetric Information

The research performed analysis on causes of asymmetric information of agricultural product supply chain and made conclusion on operation mechanism and characteristics of supply chain based on asymmetric information. Finally, the research detailed profit sharing of agricultural product supply chain in the context of asymmetric information and proposed suggestions, providing references of pricing and profit sharing of supply chains of agricultural products.