We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory...We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.展开更多
The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The s...Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.展开更多
Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts ...Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts between developers and local stakeholders,increasing development costs.A qualitative multicase study was conducted in this study to analyze the land transaction trajectories of China's OIPs.Four OIPs were selected to reveal the underlying mechanisms from the perspectives of institutional arrangements,governance mechanisms,and enterprise heterogeneity.The findings indicate that in host countries with insufficient institutional development,local governments are more inclined to directly engage in OIP land acquisition.High-level intergovernmental mechanisms facilitate land acquisition processes,although their efficacy depends largely on administrative power allocation across parks in host countries.The results also indicate that enterprise characteristics significantly influence land acquisition,where microscale private enterprises lacking political connections often employ low-cost,bottom-up strategies by leveraging international experience.In summary,policy-makers in developing countries should prioritize enhancing OIP governance to mitigate transaction costs,promote diversified land supply,and optimize land allocation.By depicting China's OIP land acquisition processes,this study deepens the academic understanding of OIP governance in developing countries and related international land transactions,offering practical OIP management insights for governments in both host and parent countries.展开更多
Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions,a fast calculation method for three-impulse contingency return trajectories is proposed.Firstly,a t...Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions,a fast calculation method for three-impulse contingency return trajectories is proposed.Firstly,a three-impulse contingency return trajectory scheme is presented by combining the Lambert transfer and maneuver at the special point.Secondly,a calculation model of three-impulse contingency return trajectories is established.Then,fast calculation methods are proposed by adopting the high-order Taylor expansion of differential algebra in the twobody trajectory dynamics model and perturbed trajectory dynamics model.Finally,the performance of the proposed methods is verified by numerical simulation.The results indicate that the fast calculation method of two-body trajectory has higher calculation efficiency compared to the semi-analytical calculation method under a certain accuracy condition.Due to its high efficiency,the characteristics of the three-impulse contingency return trajectories under different contingency scenarios are further analyzed expeditiously.These findings can be used for the design of contingency return trajectories in future manned lunar landing missions.展开更多
In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat...In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat’s launch speed,ejection direction,ejection angle,altitude of the aircraft,distance/height from the aircraft rudder and canopy,pilot and ejection seat weight.With the model algorithm proposed,the ejection seat trajectory model was developed on MATLAB.The ejection seat trajectory model is based on point mass trajectory mathematical model.In this study,an analytical study of the problem has been made for modeling the flight trajectory of the ejection seat after it has been ejected.Past studies were used as a basis for validation and simulation.By writing a generic MATLAB code,a user interface was developed and presented to the user as a module.This generic code that has been developed could be used for simulations by users in the future by revising it in accordance with their own job descriptions.展开更多
A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching...A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.展开更多
The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies m...The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.展开更多
The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communica...The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi...Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.展开更多
BACKGROUND Depression is a significant risk factor for diabetes,particularly type 2 diabetes.However,depressive symptoms differ from clinical depression.Previous research has not fully considered the relationship betw...BACKGROUND Depression is a significant risk factor for diabetes,particularly type 2 diabetes.However,depressive symptoms differ from clinical depression.Previous research has not fully considered the relationship between the trajectory of depressive symptoms and the risk of developing diabetes over time.AIM To investigate the association between depressive symptoms,their trajectories,and the risk of developing diabetes in two prospective cohort studies.METHODS In the first phase we analyzed the association between depressive symptoms and the risk of developing diabetes separately using the Health and Retirement Study(HRS).Depressive symptom trajectories were assessed by examining changes in depressive symptoms at baseline and again 8 years later.We then identified specific depressive symptom trajectories that increased the risk of diabetes in the second phase.Finally,we confirmed the association between depressive symptoms and their trajectories with diabetes risk using the English Longitudinal Study of Ageing(ELSA)as a validation study.Depressive symptom trajectories were categorized into five states based on changes in the modified 8-item Center for Epidemiological Studies-Depression scores:Persistently high;increasing;fluctuating;decreasing;and persistently low.Diabetes mellitus was defined as self-reported,physician-diagnosed diabetes.Cox proportional hazards models were used to assess hazard ratios(HR)and 95%confidence intervals(CI),adjusting for potential confounders.RESULTS In the first phase a total of 27658 participants were included(HRS:18633,ELSA:9025),among whom 6582 had depressive symptoms(HRS:4547,ELSA:2035),6407 had somatic depressive symptoms(HRS:4414,ELSA:1993),and 26415 had cognitive-affective depressive symptoms(HRS:17755,ELSA:8660).We found that overall depressive symptoms(HRS:HR=1.14,95%CI:1.07-1.22;ELSA:HR=1.18,95%CI:1.03-1.34)and somatic depressive symptoms(HRS:HR=1.14,95%CI:1.07-1.22;ELSA:HR=1.25,95%CI:1.10-1.42)increased the risk of diabetes,while cognitive depressive symptoms were not associated with diabetes risk.Over an 8-year follow-up we identified 19729 trajectories of overall,somatic,and cognitive-affective depressive symptoms(HRS:13918,ELSA:5811).In the second phase we found that persistently high(HRS:HR=1.22,95%CI:1.06-1.40,ELSA:HR=1.54,95%CI:1.16-2.05 in total and HRS:HR=1.24,95%CI:1.07-1.43,ELSA:HR=1.79,95%CI:1.36-2.35 in somatic)and fluctuating(HRS:HR=1.09,95%CI:1.01-1.17,ELSA:HR=1.33,95%CI:1.14-1.55 in total and HRS:HR=1.10,95%CI:1.02-1.18,ELSA:HR=1.31,95%CI:1.13-1.53 in somatic)trajectories of overall and somatic depressive symptoms increased the risk of diabetes,while increasing trajectories may also raise diabetes risk.However,decreasing trajectories were not associated with diabetes risk.Cognitive-affective depressive symptoms showed no association with diabetes risk regardless of trajectory changes.Sensitivity analyses confirmed the reliability of the findings.CONCLUSION Persistently high and fluctuating trajectories of overall and somatic depressive symptoms increased the risk of diabetes,while decreasing trajectories were not associated with diabetes risk.In contrast trajectories of cognitiveaffective depressive symptoms show no relationship with diabetes risk.Focusing on depressive symptom trajectories,particularly those of somatic depressive symptoms,represented a viable strategy for future diabetes prevention.展开更多
In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD...In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.展开更多
The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone ver...The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone vertical growth trajectories by detecting the stable isotope signatures(2H and 18O compositions)of different shells in 27 hailstones from 9hailstorms,which allowed us to capture the ambient temperature during hailstone growth.The vertical growth trajectories were obtained by comparing the isotopic compositions of water condensate in clouds,derived from the Adiabatic Model,with those measured in hailstones.Although hailstone growth was primarily observed in the–10°C to–30°C temperature layer,the embryo formation height and subsequent growth trajectories significantly varied among hailstones.Embryos formed over a wide range of temperatures(–8.7°C to–33.4°C);four originated at temperatures above–15°C and 16originated at temperatures below–20°C,suggesting ice nuclei composed of bioproteins and mineral dust,respectively.Among the 27 measured hailstones,3 exhibited minimal vertical movement,16 exhibited a monotonic rise or fall,and the remaining 8 exhibited alternating up-down trajectories;only one experienced“recycling”during up-down drifting.Trajectory analysis revealed that similar-sized hailstones from a single storm tended to form at similar heights,whereas those larger than 25 mm in diameter exhibited at least one period of upward growth.Vertical trajectories derived from isotopic analysis were corroborated by radar hydrometeor observations.展开更多
Based on a new bilinear equation,we investigated some new dynamic behaviors of the(2+1)-dimensional shallow water wave model,such as hybridization behavior between different solitons,trajectory equations for lump coll...Based on a new bilinear equation,we investigated some new dynamic behaviors of the(2+1)-dimensional shallow water wave model,such as hybridization behavior between different solitons,trajectory equations for lump collisions,and evolution behavior of multi-breathers.Firstly,the N-soliton solution of Ito equation is studied,and some high-order breather waves can be obtained from the N-soliton solutions through paired-complexification of parameters.Secondly,the high-order lump solutions and the hybrid solutions are obtained by employing the long-wave limit method,and the motion velocity and trajectory equations of high-order lump waves are analyzed.Moreover,based on the trajectory equations of the higher-order lump solutions,we give and prove the trajectory theorem of 1-lump before and after interaction with nsoliton.Finally,we obtain some new lump solutions from the multi-solitons by constructing a new test function and using the parameter limit method.Meanwhile,some evolutionary behaviors of the obtained solutions are shown through a large number of three-dimensional graphs with different and appropriate parameters.展开更多
1. Background Driven by ongoing economic expansion and low-altitude aviation development, the global air transportation industry has experienced significant growth in recent decades, resulting in increasing airspace c...1. Background Driven by ongoing economic expansion and low-altitude aviation development, the global air transportation industry has experienced significant growth in recent decades, resulting in increasing airspace complexity, and considerable challenges for Air Traffic Control(ATC). As the fundamental technique of the ATC system, Flight Trajectory Prediction(FTP) forecasts future traffic dynamics to support critical applications(such as conflict detection), and also serves as a cornerstone for future Trajectory-based Operations(TBO).展开更多
This study sought to analyze the occupational habits of immigrant workers in the formal Brazilian labor market from a sociological perspective.It was understood that a study on occupation should observe professional a...This study sought to analyze the occupational habits of immigrant workers in the formal Brazilian labor market from a sociological perspective.It was understood that a study on occupation should observe professional and employment history,which may include an analysis over time of workers in the same profession or in periods of professional mobility.In order to understand occupational trajectories and related aspects,this study used data that were collected through semi-structured interviews and surveys with immigrant workers who were employed in the Brazilian formal labor market.The interviews and surveys revealed a strong polarization among the immigrant workers present in Brazil-while a small part holds highly skilled occupations,the majority participate at the base of the productivity hierarchy.展开更多
This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor gro...This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 12234013)the Natural Science Foundation of Shandong Province (Grant No. ZR2021LLZ009)。
文摘We present a formulation of the single-trajectory entropy using the trajectories ensemble. The single-trajectory entropy is affected by its surrounding trajectories via the distribution function. The single-trajectory entropies are studied in two typical potentials, i.e., harmonic potential and double-well potential, and in viscous environment by interacting trajectory method. The results of the trajectory methods are in agreement well with the numerical methods(Monte Carlo simulation and difference equation). The single-trajectory entropies increasing(decreasing) could be caused by absorption(emission) heat from(to) the thermal environment. Also, some interesting trajectories, which correspond to the rare evens in the processes, are demonstrated.
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the Key Research and Development Program of the Ministry of Science and Technology of China(grant number:2016YF0900605)the Key Research and Development Program of Hebei Province(grant number:192777129D)+1 种基金the Joint Fund for Iron and Steel of the Natural Science Foundation of Hebei Province(grant number:H2016209058)the National Natural Science Foundation for Regional Joint Fund of China(grant number:U22A20364)。
文摘Objective We aimed to investigate the patterns of fasting blood glucose(FBG)trajectories and analyze the relationship between various occupational hazard factors and FBG trajectories in male steelworkers.Methods The study cohort included 3,728 workers who met the selection criteria for the Tanggang Occupational Cohort(TGOC)between 2017 and 2022.A group-based trajectory model was used to identify the FBG trajectories.Environmental risk scores(ERS)were constructed using regression coefficients from the occupational hazard model as weights.Univariate and multivariate logistic regression analyses were performed to explore the effects of occupational hazard factors using the ERS on FBG trajectories.Results FBG trajectories were categorized into three groups.An association was observed between high temperature,noise exposure,and FBG trajectory(P<0.05).Using the first quartile group of ERS1 as a reference,the fourth quartile group of ERS1 had an increased risk of medium and high FBG by 1.90and 2.21 times,respectively(odds ratio[OR]=1.90,95%confidence interval[CI]:1.17–3.10;OR=2.21,95%CI:1.09–4.45).Conclusion An association was observed between occupational hazards based on ERS and FBG trajectories.The risk of FBG trajectory levels increase with an increase in ERS.
基金Philosophy and Social Science Planning Projects in Yunnan Province,No.QN202428China Postdoctoral Science Foundation,No.2024M752918。
文摘Foreign-funded overseas industrial parks(OIPs)are crucial for attracting foreign investment and promoting globalization in developing countries.However,large-scale land acquisition for these parks generates conflicts between developers and local stakeholders,increasing development costs.A qualitative multicase study was conducted in this study to analyze the land transaction trajectories of China's OIPs.Four OIPs were selected to reveal the underlying mechanisms from the perspectives of institutional arrangements,governance mechanisms,and enterprise heterogeneity.The findings indicate that in host countries with insufficient institutional development,local governments are more inclined to directly engage in OIP land acquisition.High-level intergovernmental mechanisms facilitate land acquisition processes,although their efficacy depends largely on administrative power allocation across parks in host countries.The results also indicate that enterprise characteristics significantly influence land acquisition,where microscale private enterprises lacking political connections often employ low-cost,bottom-up strategies by leveraging international experience.In summary,policy-makers in developing countries should prioritize enhancing OIP governance to mitigate transaction costs,promote diversified land supply,and optimize land allocation.By depicting China's OIP land acquisition processes,this study deepens the academic understanding of OIP governance in developing countries and related international land transactions,offering practical OIP management insights for governments in both host and parent countries.
基金co-supported by the National Natural Science Foundation of China(No.12072365)the Technology Innovation Team of Manned Space Engineering,China。
文摘Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions,a fast calculation method for three-impulse contingency return trajectories is proposed.Firstly,a three-impulse contingency return trajectory scheme is presented by combining the Lambert transfer and maneuver at the special point.Secondly,a calculation model of three-impulse contingency return trajectories is established.Then,fast calculation methods are proposed by adopting the high-order Taylor expansion of differential algebra in the twobody trajectory dynamics model and perturbed trajectory dynamics model.Finally,the performance of the proposed methods is verified by numerical simulation.The results indicate that the fast calculation method of two-body trajectory has higher calculation efficiency compared to the semi-analytical calculation method under a certain accuracy condition.Due to its high efficiency,the characteristics of the three-impulse contingency return trajectories under different contingency scenarios are further analyzed expeditiously.These findings can be used for the design of contingency return trajectories in future manned lunar landing missions.
文摘In this study,it is aimed to develop a generic model which calculates the trajectory of the ejection seat from the jet aircraft,by taking into account the parameters that will affect the seat movement such as the seat’s launch speed,ejection direction,ejection angle,altitude of the aircraft,distance/height from the aircraft rudder and canopy,pilot and ejection seat weight.With the model algorithm proposed,the ejection seat trajectory model was developed on MATLAB.The ejection seat trajectory model is based on point mass trajectory mathematical model.In this study,an analytical study of the problem has been made for modeling the flight trajectory of the ejection seat after it has been ejected.Past studies were used as a basis for validation and simulation.By writing a generic MATLAB code,a user interface was developed and presented to the user as a module.This generic code that has been developed could be used for simulations by users in the future by revising it in accordance with their own job descriptions.
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.
基金supported by National Natural Science Foundation of China(Grant No.52375380)National Key R&D Program of China(Grant No.2022YFB3402200)the Key Project of National Natural Science Foundation of China(Grant No.12032018).
文摘The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.
基金funded in part by the National Natural Science Foundation of China under Grant 62401167 and 62192712in part by the Key Laboratory of Marine Environmental Survey Technology and Application,Ministry of Natural Resources,P.R.China under Grant MESTA-2023-B001in part by the Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology under Grant JCKYS2022604SSJS007.
文摘The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
基金funded by the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-503)the National Natural Science Foundation of China(No.51974254)。
文摘Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
文摘BACKGROUND Depression is a significant risk factor for diabetes,particularly type 2 diabetes.However,depressive symptoms differ from clinical depression.Previous research has not fully considered the relationship between the trajectory of depressive symptoms and the risk of developing diabetes over time.AIM To investigate the association between depressive symptoms,their trajectories,and the risk of developing diabetes in two prospective cohort studies.METHODS In the first phase we analyzed the association between depressive symptoms and the risk of developing diabetes separately using the Health and Retirement Study(HRS).Depressive symptom trajectories were assessed by examining changes in depressive symptoms at baseline and again 8 years later.We then identified specific depressive symptom trajectories that increased the risk of diabetes in the second phase.Finally,we confirmed the association between depressive symptoms and their trajectories with diabetes risk using the English Longitudinal Study of Ageing(ELSA)as a validation study.Depressive symptom trajectories were categorized into five states based on changes in the modified 8-item Center for Epidemiological Studies-Depression scores:Persistently high;increasing;fluctuating;decreasing;and persistently low.Diabetes mellitus was defined as self-reported,physician-diagnosed diabetes.Cox proportional hazards models were used to assess hazard ratios(HR)and 95%confidence intervals(CI),adjusting for potential confounders.RESULTS In the first phase a total of 27658 participants were included(HRS:18633,ELSA:9025),among whom 6582 had depressive symptoms(HRS:4547,ELSA:2035),6407 had somatic depressive symptoms(HRS:4414,ELSA:1993),and 26415 had cognitive-affective depressive symptoms(HRS:17755,ELSA:8660).We found that overall depressive symptoms(HRS:HR=1.14,95%CI:1.07-1.22;ELSA:HR=1.18,95%CI:1.03-1.34)and somatic depressive symptoms(HRS:HR=1.14,95%CI:1.07-1.22;ELSA:HR=1.25,95%CI:1.10-1.42)increased the risk of diabetes,while cognitive depressive symptoms were not associated with diabetes risk.Over an 8-year follow-up we identified 19729 trajectories of overall,somatic,and cognitive-affective depressive symptoms(HRS:13918,ELSA:5811).In the second phase we found that persistently high(HRS:HR=1.22,95%CI:1.06-1.40,ELSA:HR=1.54,95%CI:1.16-2.05 in total and HRS:HR=1.24,95%CI:1.07-1.43,ELSA:HR=1.79,95%CI:1.36-2.35 in somatic)and fluctuating(HRS:HR=1.09,95%CI:1.01-1.17,ELSA:HR=1.33,95%CI:1.14-1.55 in total and HRS:HR=1.10,95%CI:1.02-1.18,ELSA:HR=1.31,95%CI:1.13-1.53 in somatic)trajectories of overall and somatic depressive symptoms increased the risk of diabetes,while increasing trajectories may also raise diabetes risk.However,decreasing trajectories were not associated with diabetes risk.Cognitive-affective depressive symptoms showed no association with diabetes risk regardless of trajectory changes.Sensitivity analyses confirmed the reliability of the findings.CONCLUSION Persistently high and fluctuating trajectories of overall and somatic depressive symptoms increased the risk of diabetes,while decreasing trajectories were not associated with diabetes risk.In contrast trajectories of cognitiveaffective depressive symptoms show no relationship with diabetes risk.Focusing on depressive symptom trajectories,particularly those of somatic depressive symptoms,represented a viable strategy for future diabetes prevention.
文摘In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
基金supported by the National Natural Science Foundation of China(Grant No.42030607)the Beijing Municipal Science and Technology Commission(Grant No.Z251100004525005)the National Science Foundation/National Center for Atmospheric Research,and NASA(Grant No.80NSSC22M0129)。
文摘The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone vertical growth trajectories by detecting the stable isotope signatures(2H and 18O compositions)of different shells in 27 hailstones from 9hailstorms,which allowed us to capture the ambient temperature during hailstone growth.The vertical growth trajectories were obtained by comparing the isotopic compositions of water condensate in clouds,derived from the Adiabatic Model,with those measured in hailstones.Although hailstone growth was primarily observed in the–10°C to–30°C temperature layer,the embryo formation height and subsequent growth trajectories significantly varied among hailstones.Embryos formed over a wide range of temperatures(–8.7°C to–33.4°C);four originated at temperatures above–15°C and 16originated at temperatures below–20°C,suggesting ice nuclei composed of bioproteins and mineral dust,respectively.Among the 27 measured hailstones,3 exhibited minimal vertical movement,16 exhibited a monotonic rise or fall,and the remaining 8 exhibited alternating up-down trajectories;only one experienced“recycling”during up-down drifting.Trajectory analysis revealed that similar-sized hailstones from a single storm tended to form at similar heights,whereas those larger than 25 mm in diameter exhibited at least one period of upward growth.Vertical trajectories derived from isotopic analysis were corroborated by radar hydrometeor observations.
基金Project supported by the National Natural Science Foundation of China(Grant No.12461047)the Scientific Research Project of the Hunan Education Department(Grant No.24B0478).
文摘Based on a new bilinear equation,we investigated some new dynamic behaviors of the(2+1)-dimensional shallow water wave model,such as hybridization behavior between different solitons,trajectory equations for lump collisions,and evolution behavior of multi-breathers.Firstly,the N-soliton solution of Ito equation is studied,and some high-order breather waves can be obtained from the N-soliton solutions through paired-complexification of parameters.Secondly,the high-order lump solutions and the hybrid solutions are obtained by employing the long-wave limit method,and the motion velocity and trajectory equations of high-order lump waves are analyzed.Moreover,based on the trajectory equations of the higher-order lump solutions,we give and prove the trajectory theorem of 1-lump before and after interaction with nsoliton.Finally,we obtain some new lump solutions from the multi-solitons by constructing a new test function and using the parameter limit method.Meanwhile,some evolutionary behaviors of the obtained solutions are shown through a large number of three-dimensional graphs with different and appropriate parameters.
文摘1. Background Driven by ongoing economic expansion and low-altitude aviation development, the global air transportation industry has experienced significant growth in recent decades, resulting in increasing airspace complexity, and considerable challenges for Air Traffic Control(ATC). As the fundamental technique of the ATC system, Flight Trajectory Prediction(FTP) forecasts future traffic dynamics to support critical applications(such as conflict detection), and also serves as a cornerstone for future Trajectory-based Operations(TBO).
文摘This study sought to analyze the occupational habits of immigrant workers in the formal Brazilian labor market from a sociological perspective.It was understood that a study on occupation should observe professional and employment history,which may include an analysis over time of workers in the same profession or in periods of professional mobility.In order to understand occupational trajectories and related aspects,this study used data that were collected through semi-structured interviews and surveys with immigrant workers who were employed in the Brazilian formal labor market.The interviews and surveys revealed a strong polarization among the immigrant workers present in Brazil-while a small part holds highly skilled occupations,the majority participate at the base of the productivity hierarchy.
基金National Natural Science Foundation of China(Project No.:12371428)Projects of the Provincial College Students’Innovation and Training Program in 2024(Project No.:S202413023106,S202413023110)。
文摘This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.
