Mechatronic Modeling and Domain Transformation of Multi-physics Systems

The enhanced definition of Mechatronics involves the four underlying characteristics of integrated,unified,unique,and systematic approaches.In this realm,Mechatronics is not limited to electro-mechanical systems,in the multi-physics sense,but involves other physical domains such as fluid and thermal.This paper summarizes the mechatronic approach to modeling.Linear graphs facilitate the development of state-space models of mechatronic systems,through this approach.The use of linear graphs in mechatronic modeling is outlined and an illustrative example of sound system modeling is given.Both time-domain and frequency-domain approaches are presented for the use of linear graphs.A mechatronic model of a multi-physics system may be simplified by converting all the physical domains into an equivalent single-domain system that is entirely in the output domain of the system.This approach of converting(transforming)physical domains is presented.An illustrative example of a pressure-controlled hydraulic actuator system that operates a mechanical load is given.

Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching,realize the integration of online and offline teaching,and optimize the intelligent learning experience of university physics,this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms,and applies this system to teaching environment of university physics.It successfully integrates emerging technologies such as cloud computing,machine learning,and situational awareness,integrates learning context awareness,intelligent recording and broadcasting,resource sharing,learning performance prediction,and content planning and recommendation,and comprehensively improves the quality of university physics teaching.It can optimize the teaching process and deepen intelligent teaching reform,aiming at providing references for the teaching practice of university physics.

Association between physical activity and risk of gastroesophageal reflux disease:A systematic review and meta-analysis

Background:Lifestyle plays an important role in preventing and managing gastroesophageal reflux disease(GERD).In response to the conflicting results in previous studies,we performed a systematic review and meta-analysis to investigate this association.Methods:Relevant studies published until January 2023 were retrieved from 6 databases,and the prevalence of symptomatic gastroesophageal reflux(GER)or GERD was determined from the original studies.A random effects model was employed to meta-analyze the association by computing the pooled relative risk(RR)with 95%confidence intervals(95%CIs).Furthermore,subgroup and dose-response analyses were performed to explore subgroup differences and the association between cumulative physical activity(PA)time and GERD.Results:This meta-analysis included 33 studies comprising 242,850 participants.A significant negative association was observed between PA and the prevalence of symptomatic GER(RR=0.74,95%CI:0.66-0.83;p<0.01)or GERD(RR=0.80,95%CI:0.76-0.84;p<0.01),suggesting that engaging in PA might confer a protective benefit against GERD.Subgroup analyses consistently indicated the presence of this association across nearly all subgroups,particularly among the older individuals(RR_(<40 years):RR_(≥40 years)=0.85:0.69,p<0.01)and smokers(RR_(smoker):RR_(non-smoker)=0.67:0.82,p=0.03).Furthermore,a dose-response analysis revealed that individuals who engaged in 150 min of PA per week had a 72.09%lower risk of developing GERD.Conclusion:Maintaining high levels of PA decreased the risk of GERD,particularly among older adults and smokers.Meeting the recommended PA level of 150 min per week may significantly decrease the prevalence of GERD.

Jetting-based bioprinting:process,dispense physics,and applications

Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.

Alexa,let's train now!——A systematic review and classification approach to digital and home-based physical training interventions aiming to support healthy cognitive aging

Background:There is mounting evidence that regular physical activity is an important prerequisite for healthy cognitive aging.Consequently,the finding that almost one-third of the adult population does not reach the recommended level of regular physical activity calls for further public health actions.In this context,digital and home-based physical training interventions might be a promising alternative to center-based intervention programs.Thus,this systematic review aimed to summarize the current state of the literature on the effects of digital and home-based physical training interventions on adult cognitive performance.Methods:In this pre-registered systematic review(PROSPERO;ID:CRD42022320031),5 electronic databases(PubMed,Web of Science,Psyclnfo,SPORTDiscus,and Cochrane Library)were searched by 2 independent researchers(FH and PT)to identify eligible studies investigating the effects of digital and home-based physical training interventions on cognitive performance in adults.The systematic literature search yielded 8258 records(extra17 records from other sources),of which 27 controlled trials were considered relevant.Two reviewers(FH and PT)independently extracted data and assessed the risk of bias using a modified version of the Tool for the assEssment of Study qualiTy and reporting in EXercise(TESTEX scale).Results:Of the 27 reviewed studies,15 reported positive effects on cognitive and motor-cognitive outcomes(i.e.,performance improvements in measures of executive functions,working memory,and choice stepping reaction test),and a considerable heterogeneity concerning study-related,population-related,and intervention-related characteristics was noticed.A more detailed analysis suggests that,in particular,interventions using online classes and technology-based exercise devices(i.e.,step-based exergames)can improve cognitive performance in healthy older adults.Approximately one-half of the reviewed studies were rated as having a high risk of bias with respect to completion adherence(≤85%)and monitoring of the level of regular physical activity in the control group.Conclusion:The current state of evidence concerning the effectiveness of digital and home-based physical training interventions is mixed overall,though there is limited evidence that specific types of digital and home-based physical training interventions(e.g.,online classes and step-based exergames)can be an effective strategy for improving cognitive performance in older adults.However,due to the limited number of available studies,future high-quality studies are needed to buttress this assumption empirically and to allow for more solid and nuanced conclusions.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.

A novel encoding mechanism for particle physics

This study proposes a novel particle encoding mechanism that seamlessly incorporates the quantum properties of particles,with a specific emphasis on constituent quarks.The primary objective of this mechanism is to facilitate the digital registration and identification of a wide range of particle information.Its design ensures easy integration with different event generators and digital simulations commonly used in high-energy experiments.Moreover,this innovative framework can be easily expanded to encode complex multi-quark states comprising up to nine valence quarks and accommodating an angular momentum of up to 99/2.This versatility and scalability make it a valuable tool.

Transient Thermal Distribution in a Wavy Fin Using Finite Difference Approximation Based Physics Informed Neural Network

Heat transport has been significantly enhanced by the widespread usage of extended surfaces in various engi-neering domains.Gas turbine blade cooling,refrigeration,and electronic equipment cooling are a few prevalent applications.Thus,the thermal analysis of extended surfaces has been the subject of a significant assessment by researchers.Motivated by this,the present study describes the unsteady thermal dispersal phenomena in a wavy fin with the presence of convection heat transmission.This analysis also emphasizes a novel mathematical model in accordance with transient thermal change in a wavy profiled fin resulting from convection using the finite difference method(FDM)and physics informed neural network(PINN).The time and space-dependent governing partial differential equation(PDE)for the suggested heat problem has been translated into a dimensionless form using the relevant dimensionless terms.The graph depicts the effect of thermal parameters on the fin’s thermal profile.The temperature dispersion in the fin decreases as the dimensionless convection-conduction variable rises.The heat dispersion in the fin is decreased by increasing the aspect ratio,whereas the reverse behavior is seen with the time change.Furthermore,FDM-PINN results are validated against the outcomes of the FDM.

Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

Non-coding RNAs in exercise immunology:A systematic review

Regular physical exercise has been recognized as a potent modulator of immune function,with its effects including enhanced immune surveillance,reduced inflammation,and improved overall health.While strong evidence exists that physical exercise affects the specific expression and activity of non-coding RNAs(ncRNAs)also involved in immune system regulation,heterogeneity in individual study designs and analyzed exercise protocols exists,and a condensed list of functional,exercise-dependent ncRNAs with known targets in the immune system is missing from the literature.A systematic review and qualitative analysis was used to identify and categorize ncRNAs participating in immune modulation by physical exercise.Two combined approaches were used:(a)a systematic literature search for“ncRNA and exercise immunology”,(b)and a database search for microRNAs(miRNAs)(miRTarBase and DIANA-Tarbase v8)aligned with known target genes in the immune system based on the Reactome database,combined with a systematic literature search for“ncRNA and exercise”.Literature searches were based on PubMed,Web of Science,and SPORTDiscus;and miRNA databases were filtered for targets validated by in vitro experimental data.Studies were eligible if they reported on exercise-based interventions in healthy humans.After duplicate removal,95 studies were included reporting on 164 miRNAs,which were used for the qualitative synthesis.Six studies reporting on long-noncoding RNAs(lncRNAs)or circular RNAs were also identified.Results were analyzed using ordering tables that included exercise modality(endurance/resistance exercise),acute or chronic interventions,as well as the consistency in reported change between studies.Evaluation criteria were defined as“validated”with 100%of≥3 independent studies showing identical direction of regulation,“plausible”(≥80%),or“suggestive”(≥70%).For resistance exercise,upregulation of miR-206 was validated while downregulation of miR-133a appeared plausible.For endurance exercise,15 miRNAs were categorized as validated,with 12 miRNAs being consistently elevated and 3 miRNAs being downregulated,most of them after acute exercise training.In conclusion,our approach provides evidence that miRNAs play a major role in exercise-induced effects on the innate and adaptive immune system by targeting different pathways affecting immune cell distribution,function,and trafficking as well as production of(anti-)inflammatory cytokines.miRNAs miR-15,miR-29c,miR-30a,miR-142/3,miR-181a,and miR-338 emerged as key players in mediating the immunomodulatory effects of exercise predominantly after acute bouts of endurance exercise.

The anti-inflammatory effects of exercise on autoimmune diseases:A 20-year systematic review

Background:The anti-inflammatory effect of exercise may be an underlying factor in improving several autoimmune diseases.The aim of this systematic review was to examine the evidence on the role of exercise training in mitigating inflammation in adolescents and adults with autoimmune disease.Methods:PubMed,Web of Science,and Embase databases were systematically reviewed for related studies published between January 1,2003,and August 31,2023.All randomized and non-randomized controlled trials of exercise interventions with autoimmune disease study participants that evaluated inflammation-related biomarkers were included.The quality of evidence was assessed using the Tool for the assEssment of Study qualiTy and reporting in EXercise scale and Cochrane bias risk tool.Results:A total of 14,565 records were identified.After screening the titles,abstracts,and full texts,87 were eligible for the systematic review.These studies were conducted in 25 different countries and included a total of 2779 participants(patients with autoimmune disease,in exercise or control groups).Overall,the evidence suggests that inflammation-related markers such as C-reactive protein,interleukin 6,and tumor necrosis factor a were reduced by regular exercise interventions.Regular exercise interventions combined with multiple exercise modes were associated with greater benefits.Conclusion:Regular exercise training by patients with autoimmune disease exerts an anti-inflammatory influence.This systematic review provides support for the promotion and development of clinical exercise intervention programs for patients with autoimmune disease.Most patients with autoimmune disease can safely adopt moderate exercise training protocols,but changes in inflammation biomarkers will be modest at best.Acute exercise interventions are ineffective or even modestly but transiently pro-inflammatory.

Ultrafast photoemission electron microscopy:A multidimensional probe of nonequilibrium physics

Exploring the realms of physics that extend beyond thermal equilibrium has emerged as a crucial branch of condensed matter physics research.It aims to unravel the intricate processes involving the excitations,interactions,and annihilations of quasi-and many-body particles,and ultimately to achieve the manipulation and engineering of exotic non-equilibrium quantum phases on the ultrasmall and ultrafast spatiotemporal scales.Given the inherent complexities arising from many-body dynamics,it therefore seeks a technique that has efficient and diverse detection degrees of freedom to study the underlying physics.By combining high-power femtosecond lasers with real-or momentum-space photoemission electron microscopy(PEEM),imaging excited state phenomena from multiple perspectives,including time,real space,energy,momentum,and spin,can be conveniently achieved,making it a unique technique in studying physics out of equilibrium.In this context,we overview the working principle and technical advances of the PEEM apparatus and the related laser systems,and survey key excited-state phenomena probed through this surface-sensitive methodology,including the ultrafast dynamics of electrons,excitons,plasmons,spins,etc.,in materials ranging from bulk and nano-structured metals and semiconductors to low-dimensional quantum materials.Through this review,one can further envision that time-resolved PEEM will open new avenues for investigating a variety of classical and quantum phenomena in a multidimensional parameter space,offering unprecedented and comprehensive insights into important questions in the field of condensed matter physics.

Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

The sampling of the training data is a bottleneck in the development of artificial intelligence(AI)models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices.Active learning(AL)approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples.Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence.To judge the relevance of a data instance,query rules are defined.In this paper,we propose an AL methodology based on a physics-based query rule.Given some industrial objectives from the physical process where the AI model is implied in,the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points.Therefore,the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are,while coarse everywhere else where the data instances are of no interest in the industrial context studied.

The effectiveness of physical activity interventions on blood pressure in children and adolescents:A systematic review and network meta-analysis

Background:High blood pressure(BP)is a major contributor to mortality and cardiovascular diseases.Despite the known benefits of exercise for reducing BP,it is crucial to identify the most effective physical activity(PA)intervention.This systematic review and network meta-analysis(NMA)aimed to evaluate the available evidence on the effectiveness of various PA interventions for reducing BP and to determine their hierarchy based on their impact on BP.Methods:A search of PubMed,SPORTDiscus,PsycINFO,Web of Science,CINAHL,Cochrane,and Eric databases was conducted up to December 2022 for this systematic review and NMA.Randomized controlled trials and quasi-experimental studies targeting healthy children and adolescents aged 6-12 years old were included in this study.Only studies that compared controlled and intervention groups using PA or exercise as the major influence were included.We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.Three independent investigators performed the literature screening,data extraction,and risk of bias assessment.We used Bayesian arm-based NMA to synthesize the data.The primary outcomes were systolic BP and diastolic BP.We calculated the mean differences(MDs)in systolic BP and diastolic BP before and after treatment.Mean treatment differences were estimated using NMA and random-effect models.Results:We synthesized 27 studies involving 15,220 children and adolescents.PA combined with nutrition and behavior change was the most effective intervention for reducing both systolic BP and diastolic BP(MD=-8.64,95%credible interval(95%CI):-11.44 to-5.84;MD=-6.75,95%CI:-10.44 to-3.11),followed by interventions with multiple components(MD=-1.39,95%CI:-1.94 to-0.84;MD=-2.54,95%CI:-4.89 to-0.29).Conclusion:Our findings suggest that PA interventions incorporating nutrition and behavior change,followed by interventions with multiple components,are most effective for reducing both systolic BP and diastolic BP in children and adolescents.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Physics-based seismic analysis of ancient wood structure:fault-to-structure simulation

Based on the domain reduction method,this study employs an SEM-FEM hybrid workflow which integrates the advantages of the spectral element method(SEM)for flexible and highly efficient simulation of seismic wave propagation in a three-dimensional(3D)regional-scale geophysics model and the finite element method(FEM)for fine simulation of structural response including soil-structure interaction,and performs a physics-based simulation from initial fault rupture on an ancient wood structure.After verification of the hybrid workflow,a large-scale model of an ancient wood structure in the Beijing area,The Tower of Buddhist Incense,is established and its responses under the 1665 Tongxian earthquake and the 1730 Yiheyuan earthquake are simulated.The results from the simulated ground motion and seismic response of the wood structure under the two earthquakes demonstrate that this hybrid workflow can be employed to efficiently provide insight into the relationships between geophysical parameters and the structural response,and is of great significance toward accurate input for seismic simulation of structures under specific site and fault conditions.

Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

Scientific exploration and hypotheses concerning the meridian system in traditional Chinese medicine

The meridian theory is an important component of traditional Chinese medicine,playing a crucial role in disease diagnosis,treatment,and health preservation.Serving as the media for the effects of acupuncture,moxibustion,herbal medicine,and acupressure massage,meridians exert undeniable impact on the human body.However,the essence of meridians remains a topic of debate.Recent research has primarily focused on their anatomical structures,leading to numerous hypotheses.Simultaneously,other researchers have approached this subject from an energetic perspective,discovering information interactions within the meridian system.These findings suggest that meridians possess both physical and information dimensions,indicating that a singular approach to their study is insufficient.To bridge this gap,a shift from purely structural research toward an exploration of the information aspects of meridians is necessary.By integrating this information approach with traditional meridian theory,it may be possible to develop a new,modernized meridian theory that is aligned with contemporary concepts,making it more accessible and applicable in clinical settings.

Tensile Shock Physics in Compressible Thermoviscoelastic Solid Medium

This paper addresses tensile shock physics in thermoviscoelastic (TVE) solids without memory. The mathematical model is derived using conservation and balance laws (CBL) of classical continuum mechanics (CCM), incorporating the contravariant second Piola-Kirchhoff stress tensor, the covariant Green’s strain tensor, and its rates up to order n. This mathematical model permits the study of finite deformation and finite strain compressible deformation physics with an ordered rate dissipation mechanism. Constitutive theories are derived using conjugate pairs in entropy inequality and the representation theorem. The resulting mathematical model is both thermodynamically and mathematically consistent and has closure. The solution of the initial value problems (IVPs) describing evolutions is obtained using a variationally consistent space-time coupled finite element method, derived using space-time residual functional in which the local approximations are in hpk higher-order scalar product spaces. This permits accurate description problem physics over the discretization and also permits precise a posteriori computation of the space-time residual functional, an accurate measure of the accuracy of the computed solution. Model problem studies are presented to demonstrate tensile shock formation, propagation, reflection, and interaction. A unique feature of this research is that tensile shocks can only exist in solid matter, as their existence requires a medium to be elastic (presence of strain), which is only possible in a solid medium. In tensile shock physics, a decrease in the density of the medium caused by tensile waves leads to shock formation ahead of the wave. In contrast, in compressive shocks, an increase in density and the corresponding compressive waves result in the formation of compression shocks behind of the wave. Although these are two similar phenomena, they are inherently different in nature. To our knowledge, this work has not been reported in the published literature.

基于Interactive Physics的物理习题教学--以2023年高考全国乙卷25题和湖北卷15题为例

在传统高中物理教学中,多过程碰撞的物理过程很难在板书上展现出来,成为教学的一大难点。基于Interactive Physics仿真功能,以2023年高考全国乙卷25题和湖北卷15题为例,将多过程碰撞的物理过程可视化,直观展现物理模型,帮助学生理解题目的复杂情境,有效缩短学习时间,提高教学质量和教学效率,实现最优化的教学目标。