Based on theWorld Health Organization(WHO),Meningitis is a severe infection of the meninges,the membranes covering the brain and spinal cord.It is a devastating disease and remains a significant public health challeng...Based on theWorld Health Organization(WHO),Meningitis is a severe infection of the meninges,the membranes covering the brain and spinal cord.It is a devastating disease and remains a significant public health challenge.This study investigates a bacterial meningitis model through deterministic and stochastic versions.Four-compartment population dynamics explain the concept,particularly the susceptible population,carrier,infected,and recovered.The model predicts the nonnegative equilibrium points and reproduction number,i.e.,the Meningitis-Free Equilibrium(MFE),and Meningitis-Existing Equilibrium(MEE).For the stochastic version of the existing deterministicmodel,the twomethodologies studied are transition probabilities and non-parametric perturbations.Also,positivity,boundedness,extinction,and disease persistence are studiedrigorouslywiththe helpofwell-known theorems.Standard and nonstandard techniques such as EulerMaruyama,stochastic Euler,stochastic Runge Kutta,and stochastic nonstandard finite difference in the sense of delay have been presented for computational analysis of the stochastic model.Unfortunately,standard methods fail to restore the biological properties of the model,so the stochastic nonstandard finite difference approximation is offered as an efficient,low-cost,and independent of time step size.In addition,the convergence,local,and global stability around the equilibria of the nonstandard computational method is studied by assuming the perturbation effect is zero.The simulations and comparison of the methods are presented to support the theoretical results and for the best visualization of results.展开更多
In 2021,most of the developing countries are fighting polio,and parents are concerned with the disabling of their children.Poliovirus transmits from person to person,which can infect the spinal cord,and paralyzes the ...In 2021,most of the developing countries are fighting polio,and parents are concerned with the disabling of their children.Poliovirus transmits from person to person,which can infect the spinal cord,and paralyzes the parts of the body within a matter of hours.According to the World Health Organization(WHO),18 million currently healthy people could have been paralyzed by the virus during 1988–2020.Almost all countries but Pakistan,Afghanistan,and a fewmore have been declared polio-free.The mathematical modeling of poliovirus is studied in the population by categorizing it as susceptible individuals(S),exposed individuals(E),infected individuals(I),and recovered individuals(R).In this study,we study the fundamental properties such as positivity and boundedness of the model.We also rigorously study the model’s stability and equilibria with or without poliovirus.For numerical study,we design the Euler,Runge–Kutta,and nonstandard finite difference method.However,the standard techniques are time-dependent and fail to present the results for an extended period.The nonstandard finite difference method works well to study disease dynamics for a long time without any constraints.Finally,the results of different methods are compared to prove their effectiveness.展开更多
Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to el...Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods.This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning.The method obtains seed genes based on prior knowledge.Candidate genes are mined from biomedical literature.The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes.Furthermore,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of COVID-19.Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.展开更多
This paper proposes an innovative approach to social science research based on quantum theory,integrating quantum probability,quantum game theory,and quantum statistical methods into a comprehensive interdisciplinary ...This paper proposes an innovative approach to social science research based on quantum theory,integrating quantum probability,quantum game theory,and quantum statistical methods into a comprehensive interdisciplinary framework for both theoretical and empirical investigation.The study elaborates on how core quantum concepts such as superposition,interference,and measurement collapse can be applied to model social decision making,cognition,and interactions.Advanced quantum computational methods and algorithms are employed to transition from theoretical model development to simulation and experimental validation.Through case studies in international relations,economic games,and political decision making,the research demonstrates that quantum models possess significant advantages in explaining irrational and context-dependent behaviors that traditional methods often fail to capture.The paper also explores the potential applications of quantum social science in policy formulation and public decision making,addresses the ethical,privacy,and social equity challenges posed by quantum artificial intelligence,and outlines future research directions at the convergence of quantum AI,quantum machine learning,and big data analytics.The findings suggest that quantum social science not only offers a novel perspective for understanding complex social phenomena but also lays the foundation for more accurate and efficient systems in social forecasting and decision support.展开更多
The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great imp...The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.展开更多
Although three-dimensional protein structure determination using nuclear magnetic res- onance （NMR） spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniq...Although three-dimensional protein structure determination using nuclear magnetic res- onance （NMR） spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from special- ists in bioinformatics and computational biology. In this paper, we review recent advances in com- putational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also dis- cuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR.展开更多
Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultip...Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.展开更多
RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performa...RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.展开更多
This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartme...This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.展开更多
To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un...To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.展开更多
Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes...Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.展开更多
Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks....Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.展开更多
Numerical modeling has played a pivotal role in advancing inertial microfluidics,tracing its development from inception and offering deeper insights into the microscale phenomena governing inertial focusing.These comp...Numerical modeling has played a pivotal role in advancing inertial microfluidics,tracing its development from inception and offering deeper insights into the microscale phenomena governing inertial focusing.These computational approaches have simultaneously supported the proliferation of on-chip technologies.Initially adopted across diverse industries for passive and high-throughput operations such as trapping,separation,and sorting of particles,the greatest potential of inertial microfluidics lies in biomedical applications,where it serves as a cornerstone for processing cells in clinical and research settings.As the range of applications continues to expand,microfluidic devices are evolving into increasingly complex systems capable of handling diverse cell types and particles within miniature chip architectures.This growing complexity necessitates the enhancement of conventional numerical techniques and the integration of innovative computational approaches to address these emerging challenges.This review aims to provide an overview of the available numerical techniques,highlighting their advantages and limitations.We explore recent strides in computational inertial microfluidics,emphasizing advancements within the last four years and the emergence of innovative methodologies such as smoothed particle hydrodynamics.Furthermore,we describe the nascent role of machine learning in inertial microfluidics,noting its limited adoption compared to conventional microfluidics and highlighting the potential to transform the field,as well as challenges that need to be overcome.展开更多
Based on reasonable assumptions that simplified the calculational model,a simple and practical method was proposed to calculate the post-construction settlement of high-speed railway bridge pile foundation by using th...Based on reasonable assumptions that simplified the calculational model,a simple and practical method was proposed to calculate the post-construction settlement of high-speed railway bridge pile foundation by using the Mesri creep model to describe the soil characteristics and the Mindlin-Geddes method considering pile diameter to calculate the vertical additional stress of pile bottom.A program named CPPS was designed for this method to calculate the post-construction settlement of a high-speed railway bridge pile foundation.The result indicates that the post-construction settlement in 100 years meets the requirements of the engineering specifications,and in the first two decades,the post-construction settlement is about 80% of its total settlement,while the settlement in the rest eighty years tends to be stable.Compared with the measured settlement after laying railway tracks,the calculational result is closed to that of the measured,and the results are conservative with a high computational accuracy.It is noted that the method can be used to calculate the post-construction settlement for the preliminary design of high-speed railway bridge pile foundation.展开更多
Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer tra...Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.展开更多
The roll motions are influenced by significant viscous effects such as the flow separation.The 3D simulations of free decay roll motions for the ship model DTMB 5512 are carried out by Reynold averaged NavierStokes(RA...The roll motions are influenced by significant viscous effects such as the flow separation.The 3D simulations of free decay roll motions for the ship model DTMB 5512 are carried out by Reynold averaged NavierStokes(RANS) method based on the dynamic mesh technique.A new moving mesh technique is adopted and discussed in details for the present simulations.The purpose of the research is to obtain accurate numerical prediction for roll motions with their respective numerical/modeling errors and uncertainties.Errors and uncertainties are estimated by performing the modern verification and validation(V&V) procedures.Simulation results for the free-floating surface combatant are used to calculate the linear,nonlinear damping coefficients and resonant frequencies including a wide range of forward speed.The present work can provide a useful reference to calculate roll damping by computational fluid dynamics(CFD) method and simulate a general ship motions in waves.展开更多
The present paper reviews the recent developments of a high⁃order⁃spectral method(HOS)and the combination with computational fluid dynamics(CFD)method for wave⁃structure interactions.As the numerical simulations of wa...The present paper reviews the recent developments of a high⁃order⁃spectral method(HOS)and the combination with computational fluid dynamics(CFD)method for wave⁃structure interactions.As the numerical simulations of wave⁃structure interaction require efficiency and accuracy,as well as the ability in calculating in open sea states,the HOS method has its strength in both generating extreme waves in open seas and fast convergence in simulations,while computational fluid dynamics(CFD)method has its advantages in simulating violent wave⁃structure interactions.This paper provides the new thoughts for fast and accurate simulations,as well as the future work on innovations in fine fluid field of numerical simulations.展开更多
Three-dimensional(3 D)reconstruction of icosahedral viruses has played a crucial role in the development of cryoelectron microscopy single-particle reconstruction,with many cryo-electron microscopy techniques first es...Three-dimensional(3 D)reconstruction of icosahedral viruses has played a crucial role in the development of cryoelectron microscopy single-particle reconstruction,with many cryo-electron microscopy techniques first established for structural studies of icosahedral viruses,owing to their high symmetry and large mass.This review summarizes the computational methods for icosahedral and symmetry-mismatch reconstruction of viruses,as well as the likely challenges and bottlenecks in virus reconstruction,such as symmetry mismatch reconstruction,contrast transformation function(CTF)correction,and particle distortion.展开更多
Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effecti...Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.展开更多
Defining suitable enzymes for reaction steps in novel synthetic pathways is crucial for developing microbial cell factories for non-natural products.Here,we developed a computational workflow to identify C12 alcohol-a...Defining suitable enzymes for reaction steps in novel synthetic pathways is crucial for developing microbial cell factories for non-natural products.Here,we developed a computational workflow to identify C12 alcohol-active UDP-glycosyltransferases.The workflow involved three steps:(1)assembling initial candidates of putative UDP-glycosyltransferases,(2)refining selection by examining conserved regions,and(3)3D structure prediction and molecular docking.Genomic sequences from Candida,Pichia,Rhizopus,and Thermotoga,known for lauryl glucoside synthesis via whole-cell biocatalysis,were screened.Out of 240 predicted glycosyltransferases,8 candidates annotated as glycosyltransferases were selected after filtering out those with signal peptides and identifying conserved UDP-glycosyltransferase regions.These proteins underwent 3D structure prediction and molecular docking with 1-dodecanol.RO3G,a candidate from Rhizopus delemar RA 99-880 with a relatively high ChemPLP fitness score,was selected and expressed in Escherichia coli BL21(DE3).It was further characterized using a feeding experiment with 1-dodecanol.Results confirmed that the RO3G-expressing strain could convert 1-dodecanol to lauryl glucoside,as quantified by HPLC and identified by targeted LC-MS.Monitoring the growth and fermentation profiles of the engineered strain revealed that RO3G expression did not affect cell growth.Interestingly,acetate,a major fermentation product,was reduced in the RO3G-expressing strain compared to the GFP-expressing strain,suggesting a redirection of flux from acetate to other pathways.Overall,this work presents a successful workflow for discovering UDP-glycosyltransferase enzymes with confirmed activity toward 1-dodecanol for lauryl glucoside production.展开更多
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through large Research Project under Grant Number RGP2/302/45supported by the Deanship of Scientific Research,Vice Presidency forGraduate Studies and Scientific Research,King Faisal University,Saudi Arabia(Grant Number A426).
文摘Based on theWorld Health Organization(WHO),Meningitis is a severe infection of the meninges,the membranes covering the brain and spinal cord.It is a devastating disease and remains a significant public health challenge.This study investigates a bacterial meningitis model through deterministic and stochastic versions.Four-compartment population dynamics explain the concept,particularly the susceptible population,carrier,infected,and recovered.The model predicts the nonnegative equilibrium points and reproduction number,i.e.,the Meningitis-Free Equilibrium(MFE),and Meningitis-Existing Equilibrium(MEE).For the stochastic version of the existing deterministicmodel,the twomethodologies studied are transition probabilities and non-parametric perturbations.Also,positivity,boundedness,extinction,and disease persistence are studiedrigorouslywiththe helpofwell-known theorems.Standard and nonstandard techniques such as EulerMaruyama,stochastic Euler,stochastic Runge Kutta,and stochastic nonstandard finite difference in the sense of delay have been presented for computational analysis of the stochastic model.Unfortunately,standard methods fail to restore the biological properties of the model,so the stochastic nonstandard finite difference approximation is offered as an efficient,low-cost,and independent of time step size.In addition,the convergence,local,and global stability around the equilibria of the nonstandard computational method is studied by assuming the perturbation effect is zero.The simulations and comparison of the methods are presented to support the theoretical results and for the best visualization of results.
基金The authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project(Grant No.PNURSP2022R61),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In 2021,most of the developing countries are fighting polio,and parents are concerned with the disabling of their children.Poliovirus transmits from person to person,which can infect the spinal cord,and paralyzes the parts of the body within a matter of hours.According to the World Health Organization(WHO),18 million currently healthy people could have been paralyzed by the virus during 1988–2020.Almost all countries but Pakistan,Afghanistan,and a fewmore have been declared polio-free.The mathematical modeling of poliovirus is studied in the population by categorizing it as susceptible individuals(S),exposed individuals(E),infected individuals(I),and recovered individuals(R).In this study,we study the fundamental properties such as positivity and boundedness of the model.We also rigorously study the model’s stability and equilibria with or without poliovirus.For numerical study,we design the Euler,Runge–Kutta,and nonstandard finite difference method.However,the standard techniques are time-dependent and fail to present the results for an extended period.The nonstandard finite difference method works well to study disease dynamics for a long time without any constraints.Finally,the results of different methods are compared to prove their effectiveness.
基金This research is supported by the National Natural Science Foundation of China(Grant Nos.61502243,61802193)Natural Science Foundation of Jiangsu Province(BK20170934)+4 种基金Zhejiang Engineering Research Center of Intelligent Medicine under 2016E10011China Postdoctoral Science Foundation(2018M632349)NUPTSF(NY217136)Foundation of Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province(SHEL221-001)Natural Science Foundation of the Higher Education Institutions of Jiangsu Province in China(16KJD520003).
文摘Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods.This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning.The method obtains seed genes based on prior knowledge.Candidate genes are mined from biomedical literature.The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes.Furthermore,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of COVID-19.Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.
文摘This paper proposes an innovative approach to social science research based on quantum theory,integrating quantum probability,quantum game theory,and quantum statistical methods into a comprehensive interdisciplinary framework for both theoretical and empirical investigation.The study elaborates on how core quantum concepts such as superposition,interference,and measurement collapse can be applied to model social decision making,cognition,and interactions.Advanced quantum computational methods and algorithms are employed to transition from theoretical model development to simulation and experimental validation.Through case studies in international relations,economic games,and political decision making,the research demonstrates that quantum models possess significant advantages in explaining irrational and context-dependent behaviors that traditional methods often fail to capture.The paper also explores the potential applications of quantum social science in policy formulation and public decision making,addresses the ethical,privacy,and social equity challenges posed by quantum artificial intelligence,and outlines future research directions at the convergence of quantum AI,quantum machine learning,and big data analytics.The findings suggest that quantum social science not only offers a novel perspective for understanding complex social phenomena but also lays the foundation for more accurate and efficient systems in social forecasting and decision support.
基金Project supported by the National Key Foundation for Exploring Scientific Instrument （No. 2013YQ03065102）, the National Basic Research Program （973） of China （No. 2012CB316503）, and the National Natural Science Foundation of China （Nos. 31327901, 61475010, 31361163004, and 61428501）
文摘The broad applicability of super-resolution microscopy has been widely demonstrated in various areas and disciplines. The optimization and improvement of algorithms used in super-resolution microscopy are of great importance for achieving optimal quality of super-resolution imaging. In this review, we comprehensively discuss the computational methods in different types of super-resolution microscopy, including deconvolution microscopy, polarization-based super-resolution microscopy, structured illumination microscopy, image scanning microscopy, super-resolution optical fluctuation imaging microscopy, single-molecule localization microscopy, Bayesian super-resolution microscopy, stimulated emission depletion microscopy, and translation microscopy. The development of novel computational methods would greatly benefit super-resolution microscopy and lead to better resolution, improved accuracy, and faster image processing.
基金supportedby the GRP-CF award (Grant No. GRP-CF-2011-19-P-Gao-Huang)a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST)
文摘Although three-dimensional protein structure determination using nuclear magnetic res- onance （NMR） spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from special- ists in bioinformatics and computational biology. In this paper, we review recent advances in com- putational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also dis- cuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[KFU250259].
文摘Streptococcus suis(S.suis)is a major disease impacting pig farming globally.It can also be transferred to humans by eating raw pork.A comprehensive study was recently carried out to determine the indices throughmultiple geographic regions in China.Methods:The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features,including positivity,boundedness equilibria,reproduction number,and parameter sensitivity.Stochastic Euler,Runge Kutta,and EulerMaruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population.However,the dynamic qualities of the suggested model cannot be restored using these techniques.Results:For the stochastic delay differential equations of the model,the non-standard finite difference approach in the sense of stochasticity is developed to avoid several problems such as negativity,unboundedness,inconsistency,and instability of the findings.Results from traditional stochastic methods either converge conditionally or diverge over time.The stochastic non-negative step size convergence nonstandard finite difference(NSFD)method unconditionally converges to the model’s true states.Conclusions:This study improves our understanding of the dynamics of streptococcus suis infection using versions of stochastic with delay approaches and opens up new avenues for the study of cognitive processes and neuronal analysis.Theplotted interaction behaviour and new solution comparison profiles.
基金supported by grants from the National Science Foundation of China(Grant Nos.12375038 and 12075171 to ZJT,and 12205223 to YLT).
文摘RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R899)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabiasupported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia(KFU252831)。
文摘This study investigates the transmission dynamics of conjunctivitis using stochastic delay differential equations(SDDEs).A delayed stochastic model is formulated by dividing the population into five distinct compartments:susceptible,exposed,infected,environmental irritants,and recovered individuals.The model undergoes thorough analytical examination,addressing key dynamical properties including positivity,boundedness,existence,and uniqueness of solutions.Local and global stability around the equilibrium points is studied with respect to the basic reproduction number.The existence of a unique global positive solution for the stochastic delayed model is established.In addition,a stochastic nonstandard finite difference scheme is developed,which is shown to be dynamically consistent and convergent toward the equilibrium states.The scheme preserves the essential qualitative features of the model and demonstrates improved performance when compared to existing numerical methods.Finally,the impact of time delays and stochastic fluctuations on the susceptible and infected populations is analyzed.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.
基金National Natural Science Foundation of China,No.31971180 and No.11474013.
文摘Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
文摘Two methods based on a slight modification of the regular traffic assignmentalgorithms are proposed to directly compute turn flows instead of estimating them from link flows orobtaining them by expanding the networks. The first one is designed on the path-turn incidencerelationship, and it is similar to the computational procedure of link flows. It applies to thetraffic assignment algorithms that can provide detailed path structures. The second utilizes thelink-turn incidence relationship and the conservation of flow on links, a law deriving from thisrelationship. It is actually an improved version of Dial's logit assignment algorithm. The proposedapproaches can avoid the shortcomings both of the estimation methods, e. g. Furness's model andFrator's model, and of the network-expanding method in precision, stability and computation scale.Finally, they are validated by numerical examples.
基金supported in part by the National Science Foundation(NSF)Center for Advanced Design and Manufacturing of Integrated Microfluidics(I.P.,IIP-1841473)the National Science Foundation(Z.P.,DMS-1951526 and PHY-2210366.)+1 种基金the American Society of Hematology(Z.P.,Scholar Award)the US-UK Fulbright Commission(B.O.,All-Disciplines Fulbright Award).
文摘Numerical modeling has played a pivotal role in advancing inertial microfluidics,tracing its development from inception and offering deeper insights into the microscale phenomena governing inertial focusing.These computational approaches have simultaneously supported the proliferation of on-chip technologies.Initially adopted across diverse industries for passive and high-throughput operations such as trapping,separation,and sorting of particles,the greatest potential of inertial microfluidics lies in biomedical applications,where it serves as a cornerstone for processing cells in clinical and research settings.As the range of applications continues to expand,microfluidic devices are evolving into increasingly complex systems capable of handling diverse cell types and particles within miniature chip architectures.This growing complexity necessitates the enhancement of conventional numerical techniques and the integration of innovative computational approaches to address these emerging challenges.This review aims to provide an overview of the available numerical techniques,highlighting their advantages and limitations.We explore recent strides in computational inertial microfluidics,emphasizing advancements within the last four years and the emergence of innovative methodologies such as smoothed particle hydrodynamics.Furthermore,we describe the nascent role of machine learning in inertial microfluidics,noting its limited adoption compared to conventional microfluidics and highlighting the potential to transform the field,as well as challenges that need to be overcome.
基金Projects(2009G008-B,2010G018-E-3) supported by Key Projects of China Railway Ministry Science and Technology Research and Development ProgramProject(CX2013B076) supported by Hunan Provincial Innovation Foundation For Postgraduate,China
文摘Based on reasonable assumptions that simplified the calculational model,a simple and practical method was proposed to calculate the post-construction settlement of high-speed railway bridge pile foundation by using the Mesri creep model to describe the soil characteristics and the Mindlin-Geddes method considering pile diameter to calculate the vertical additional stress of pile bottom.A program named CPPS was designed for this method to calculate the post-construction settlement of a high-speed railway bridge pile foundation.The result indicates that the post-construction settlement in 100 years meets the requirements of the engineering specifications,and in the first two decades,the post-construction settlement is about 80% of its total settlement,while the settlement in the rest eighty years tends to be stable.Compared with the measured settlement after laying railway tracks,the calculational result is closed to that of the measured,and the results are conservative with a high computational accuracy.It is noted that the method can be used to calculate the post-construction settlement for the preliminary design of high-speed railway bridge pile foundation.
基金supported by the National Natural Science Foundation of China (61603398)。
文摘Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.
基金the National Natural Science Foundation of China(No.51579147)
文摘The roll motions are influenced by significant viscous effects such as the flow separation.The 3D simulations of free decay roll motions for the ship model DTMB 5512 are carried out by Reynold averaged NavierStokes(RANS) method based on the dynamic mesh technique.A new moving mesh technique is adopted and discussed in details for the present simulations.The purpose of the research is to obtain accurate numerical prediction for roll motions with their respective numerical/modeling errors and uncertainties.Errors and uncertainties are estimated by performing the modern verification and validation(V&V) procedures.Simulation results for the free-floating surface combatant are used to calculate the linear,nonlinear damping coefficients and resonant frequencies including a wide range of forward speed.The present work can provide a useful reference to calculate roll damping by computational fluid dynamics(CFD) method and simulate a general ship motions in waves.
基金National Natural Science Foundation of China(Grant No.51879159)the National Key Research and Development Program of China(Grant Nos.2019YFB1704200 and 2019YFC0312400)+2 种基金the Chang Jiang Scholars Program(Grant No.T2014099)the Shanghai Excellent Academic Leaders Program(Grant No.17XD1402300)the Innovative Special Project of Numerical Tank of Ministry of Industry and Information Technology of China(Grant No.2016-23/09).
文摘The present paper reviews the recent developments of a high⁃order⁃spectral method(HOS)and the combination with computational fluid dynamics(CFD)method for wave⁃structure interactions.As the numerical simulations of wave⁃structure interaction require efficiency and accuracy,as well as the ability in calculating in open sea states,the HOS method has its strength in both generating extreme waves in open seas and fast convergence in simulations,while computational fluid dynamics(CFD)method has its advantages in simulating violent wave⁃structure interactions.This paper provides the new thoughts for fast and accurate simulations,as well as the future work on innovations in fine fluid field of numerical simulations.
基金Project supported by the National Key R&D Program of China(Grant No.2016YFA0501100)the National Natural Science Foundation of China(Grant Nos.91530321,31570742,and 31570727)Science and Technology Planning Project of Hunan Province,China(Grant No.2017RS3033)
文摘Three-dimensional(3 D)reconstruction of icosahedral viruses has played a crucial role in the development of cryoelectron microscopy single-particle reconstruction,with many cryo-electron microscopy techniques first established for structural studies of icosahedral viruses,owing to their high symmetry and large mass.This review summarizes the computational methods for icosahedral and symmetry-mismatch reconstruction of viruses,as well as the likely challenges and bottlenecks in virus reconstruction,such as symmetry mismatch reconstruction,contrast transformation function(CTF)correction,and particle distortion.
文摘Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.
基金This work(Grant No.RGNS 64-069)was financially supported by Office of the Permanent Secretary,Ministry of Higher Education,Science,Research and Innovationpartially supported by Chiang Mai University.
文摘Defining suitable enzymes for reaction steps in novel synthetic pathways is crucial for developing microbial cell factories for non-natural products.Here,we developed a computational workflow to identify C12 alcohol-active UDP-glycosyltransferases.The workflow involved three steps:(1)assembling initial candidates of putative UDP-glycosyltransferases,(2)refining selection by examining conserved regions,and(3)3D structure prediction and molecular docking.Genomic sequences from Candida,Pichia,Rhizopus,and Thermotoga,known for lauryl glucoside synthesis via whole-cell biocatalysis,were screened.Out of 240 predicted glycosyltransferases,8 candidates annotated as glycosyltransferases were selected after filtering out those with signal peptides and identifying conserved UDP-glycosyltransferase regions.These proteins underwent 3D structure prediction and molecular docking with 1-dodecanol.RO3G,a candidate from Rhizopus delemar RA 99-880 with a relatively high ChemPLP fitness score,was selected and expressed in Escherichia coli BL21(DE3).It was further characterized using a feeding experiment with 1-dodecanol.Results confirmed that the RO3G-expressing strain could convert 1-dodecanol to lauryl glucoside,as quantified by HPLC and identified by targeted LC-MS.Monitoring the growth and fermentation profiles of the engineered strain revealed that RO3G expression did not affect cell growth.Interestingly,acetate,a major fermentation product,was reduced in the RO3G-expressing strain compared to the GFP-expressing strain,suggesting a redirection of flux from acetate to other pathways.Overall,this work presents a successful workflow for discovering UDP-glycosyltransferase enzymes with confirmed activity toward 1-dodecanol for lauryl glucoside production.
