Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

A novel small-signal equivalent circuit model for GaN HEMTs incorporating a dual-field-plate

An accurate and novel small-signal equivalent circuit model for GaN high-electron-mobility transistors(HEMTs)is proposed,which considers a dual-field-plate(FP)made up of a gate-FP and a source-FP.The equivalent circuit of the overall model is composed of parasitic elements,intrinsic transistors,gate-FP,and source-FP networks.The equivalent circuit of the gate-FP is identical to that of the intrinsic transistor.In order to simplify the complexity of the model,a series combination of a resistor and a capacitor is employed to represent the source-FP.The analytical extraction procedure of the model parameters is presented based on the proposed equivalent circuit.The verification is carried out on a 4×250μm GaN HEMT device with a gate-FP and a source-FP in a 0.45μm technology.Compared with the classic model,the proposed novel small-signal model shows closer agreement with measured S-parameters in the range of 1.0 to 18.0 GHz.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

Student Academic Performance Predictive Model Based on Dual-stream Deep Network

Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students’academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students’academic performance through learning behavior data,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic performance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evaluation model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE)indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness,which provides a powerful help for the practical application of learning effect evaluation and prediction.

Dynamics of Land Use/Land Cover Considering Ecosystem Services for a Dense-Population Watershed Based on a Hybrid Dual-Subject Agent and Cellular Automaton Modeling Approach

Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions,leading to potential conflicts among natural and human-related issues as well as among stakeholders.This study introduced economic standards for farmers.A hybrid approach(CA-ABM)of cellular automaton(CA)and an agent-based model(ABM)was developed to effectively deal with social and land-use synergic issues to examine human–environment interactions and projections of land-use conversions for a humid basin in south China.Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change.The major modules of the CA-ABM are initialization,migration,assets,land suitability,and land-use change decisions.Empirical estimates of the factors influencing the urban land-use conversion probability were captured using parameters based on a spatial logistic regression(SLR)model.Simultaneously,multicriteria evaluation(MCE)and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion.An agent-based CA-SLR-MCE-Markov(ABCSMM)land-use conversion model was proposed to explore the impacts of policies on land-use conversion.This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services.The results demonstrated land-use simulations under multi-policy scenarios,revealing the usefulness of the model for normative research on land-use management.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Research on the Dual Carbon Talent Training Model for the Construction Engineering Technology Major in Higher Vocational Education

Against the backdrop of global climate change and China’s“dual carbon”goals,the green transformation of the construction industry is imperative,and completing the transformation requires many dual carbon talents to support it.This article focuses on the construction engineering technology major in higher vocational education.It explores in depth the specific requirements for construction engineering technology talents in terms of professional knowledge,vocational skills,and literacy under the dual carbon mode.Based on this,corresponding dual carbon talent training courses are proposed,aiming to provide theoretical support and practical guidance for cultivating high-quality dual carbon talents that meet the needs of the new era.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Dynamical Modeling and Dynamic Characteristics Analysisof a Coaxial Dual-Rotor System

The dual-rotor structure serves as the primary source of vibration in aero-engines. Understanding itsdynamical model and analyzing dynamic characteristics, such as critical speed and unbalanced response, arecrucial for rotor system dynamics. Previous work introduced a coaxial dual-rotor-support scheme for aeroengines,and a physical model featuring a high-speed flexible inner rotor with a substantial length-to-diameter ratiowas designed. Then a finite element (FE) dynamic model based on the Timoshenko beam elements and rigid bodykinematics of the dual-rotor system is modeled, with the Newmark method and Newton–Raphson method used forthe numerical calculation to study the dynamic characteristics of the system. Three different simulation models,including beam-based FE (1D) model, solid-based FE (3D) model, and transfer matrix model, were designed tostudy the characteristics of mode and the critical speed characteristic of the dual-rotor system. The unbalancedresponse of the dual-rotor system was analyzed to study the influence of mass unbalance on the rotor system. Theeffect of different disk unbalance phases and different speed ratios on the dynamic characteristics of the dual-rotorsystem was investigated in detail. The experimental result shows that the beam-based FE model is effective andsuitable for studying the dual-rotor system.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Variable-coordinate forward modeling of irregular surface based on dual-variable grid

The mapping method is a forward-modeling method that transforms the irregular surface to horizontal by mapping the rectangular grid as curved; moreover, the wave field calculations move from the physical domain to the calculation domain. The mapping method deals with the irregular surface and the low-velocity layer underneath it using a fine grid. For the deeper high-velocity layers, the use of a fine grid causes local oversampling. In addition, when the irregular surface is transformed to horizontal, the flattened interface below the surface is transformed to curved, which produces inaccurate modeling results because of the presence of ladder-like burrs in the simulated seismic wave. Thus, we propose the mapping method based on the dual-variable finite-difference staggered grid. The proposed method uses different size grid spacings in different regions and locally variable time steps to match the size variability of grid spacings. Numerical examples suggest that the proposed method requires less memory storage capacity and improves the computational efficiency compared with forward modeling methods based on the conventional grid.

Deformation behavior and constitutive model for dual-phase Mg-Li alloy at elevated temperatures

In order to study the deformation behavior and evaluate the workability of the dual-phase Mg-9Li-3Al-2Sr alloy, isothermal hot compression tests were conducted using the Gleeble-3500 thermal-mechanical simulator, in ranges of elevated temperatures （423-573 K） and strain rates （0.001-1 s^-1）. Plastic instability is evident during the deformation which is in the form of serrated flow; serrated yielding is attributed to the locking of mobile dislocations by the Mg and Li atoms which diffuse during the deformation. The relationships between flow stress, strain rate and deformation temperature were analyzed and the deformation activation energy and some basic material factors at different strains were calculated using the Arrhenius equation. The effects of temperature and strain rate on deformation behavior were represented using the Zener–Hollomon parameter in an exponent-type equation. To verify the validity of the constitutive model, the predicted values and experimental flow curves under different deformation conditions were compared, the correlation coefficient （0.9970） and average absolute relative error （AARE=4.41%） were calculated. The results indicate that the constitutive model can be used to accurately predict the flow behavior of dual-phase Mg-9Li-3Al-2Sr alloy during high temperature deformation.

Trust model based on individual experience

To improve the accuracy of node trust evaluation in a distributed network, a trust model based on the experience of individuals is proposed, which establishes a new trust assessment system by introducing the experience factor and the comparative experience factor. The new evaluation system considers the differences between individuals and interactive histories between nodes, which solves the problem that nodes have inaccurate assessments due to the asymmetry of nodes to a certain extent. The algorithm analysis indicates that the new model uses different deviating values of tolerance evaluation for different individuals and uses different updating values embodying node individuation when updating feedback credibility of individuals, which evaluates the trust value more reasonably and more accurately. In addition, the proposed algorithm can be used in various trust models and has a good scalability.

Numerical simulation of residual stress field in green power metallurgy compacts by modified Drucker-Prager Cap model

Compaction process simulation and residual stress prediction of green PM compact were carried out with elasto-plastic 3D FEA based on the modified Drueker-Prager Cap model in Abaqus. The model parameters of the investigated powder Distaloy AE were determined as functions of relative density through typical mechanical property tests of powder. The model was implemented as a user subroutine USDFLD. Single sided compaction of a d20 ram^5 mm disk green compact of Distaloy AE was simulated, and the residual stress of the disk after ejection was predicted with FEA. The FEA results of the compaction process and the residual stress of the disk show good agreement with compaction experiments and X-ray diffraction measurements, which validates the model and its parameters. The results indicate that the compressive residual stresses exist mainly in a thin layer on the side surface, but the residual stresses are very small on the top and bottom surfaces.