Ligament cryopreservation enables a prolonged shelf life of allogeneic ligament grafts,which is fundamentally important to ligament ***,conventional cryopreservation techniques fail to eliminate the damage caused by i...
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Ligament cryopreservation enables a prolonged shelf life of allogeneic ligament grafts,which is fundamentally important to ligament ***,conventional cryopreservation techniques fail to eliminate the damage caused by ice crystal growth and the toxicity of cryopreservation agents(CPAs).Here,we report a novel CPA vitrification formulation primarily composed of betaine for ligament *** optimization was conducted on the methods for vitrification and rewarming,as well as the loading and unloading conditions,based on the critical cooling rate(CCR),critical warming rate(CWR),and permeation properties of the *** biomechanical and histological level tests,we demonstrate the superior performance of our method in ligament *** 30 days of vitrification cryopreservation,parameters such as the Young's modulus,tensile stress,denaturation temperature,and glycosaminoglycans content of the ligament remained essentially *** work pioneers the application of ice-free cryopreservation for ligament and holds great potential for improving the long-term storage of ligament,providing valuable insights for future cryopreservation technique development.
The paper considers the adaptive regulation for the Hammerstein and Wiener systems with event-triggered *** authors adopt a direct approach,i.e.,without identifying the unknown parameters and functions within the syst...
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The paper considers the adaptive regulation for the Hammerstein and Wiener systems with event-triggered *** authors adopt a direct approach,i.e.,without identifying the unknown parameters and functions within the systems,adaptive regulators are directly designed based on the event-triggered observations on the regulation *** adaptive regulators belong to the stochastic approximation algorithms and under moderate assumptions,the authors prove that the adaptive regulators are optimal for both the Hammerstein and Wiener systems in the sense that the squared regulation errors are asymptotically *** authors also testify the theoretical results through simulation studies.
Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is...
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Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the *** the policy improvement process,the policy gradient based method is employed.
In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process *** Exchanger Design and Rating(Aspen-EDR)was initi...
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In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process *** Exchanger Design and Rating(Aspen-EDR)was initially used to construct a first principle model(FP)of the STHE using industrial *** Genetic Algorithm(GA)was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions.A dataset comprised of optimum process conditions was generated through FP-GA integration and was utilised to develop an Artificial Neural Networks(ANN)***,the ANN model was merged with the FP model by substituting the GA,to form a GB *** developed GB model,that is,ANN and FP integration,achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP *** of the GB framework was also comparable to the FP-GA approach but it significantly reduced the computation time required for estimating the optimum process *** proposed GB-based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.
To study the dynamic behavior of a process,time-resolved data are collected at different time instants during each of a series of experiments,which are usually designed with the design of experiments or the design of ...
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To study the dynamic behavior of a process,time-resolved data are collected at different time instants during each of a series of experiments,which are usually designed with the design of experiments or the design of dynamic experiments *** utilizing such time-resolved data to model the dynamic behavior,dynamic response surface methodology(DRSM),a datadriven modeling method,has been *** approaches can be adopted in the estimation of the model parameters:stepwise regression,used in several of previous publications,and Lasso regression,which is newly incorporated in this paper for the estimation of DRSM ***,we show that both approaches yield similarly accurate models,while the computational time of Lasso is on average two magnitude *** case studies are performed to show the advantages of the proposed *** the first case study,where the concentrations of different species are modeled directly,DRSM method provides more accurate models compared to the models in the *** second case study,where the reaction extents are modeled instead of the species concentrations,illustrates the versatility of the DRSM ***,DRSM with Lasso regression can provide faster and more accurate datadriven models for a variety of organic synthesis datasets.
Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,***,the number of vehicle...
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Although the available traffic data from navigation systems have increased steadily in recent years,it only reflects average travel time and possibly Origin-Destination information as samples,***,the number of vehicles participating in the traffic-in other words,the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management-is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network *** tackle this gap,an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment *** optimally scaling the Origin-Destination matrices of the sample fleet,an appropriate model can be approximated to provide traffic flow data beside average *** iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic *** method has been tested through two different real-world traffic networks,justifying the viability of the proposed ***,the contribution of the study is a practical solution based on commonly available fleet traffic data,suggested for practitioners in traffic planning and management.
This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state in...
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This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring *** should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous *** the basis of the designed observers,the authors devise a local controller for each *** with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.
In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried *** matrix inequalities(LMIs)to be solved i...
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In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried *** matrix inequalities(LMIs)to be solved in LPV controller design process increase exponentially with the increase in a number of scheduling *** kernel functions are used to obtain the approximate LPV model of highly coupled nonlinear *** error to norm ratio of original and approximate LPV models is introduced as a measure of accuracy of the approximate LPV *** examples conclude the effectiveness of kernel-PCA for LPV model approximation as with the identification of accurate approximate LPV model,computation complexity involved in LPV controller design is decreased exponentially.
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typical...
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Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed ...
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Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number (RON) in the petroleum refining industry. Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes. A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of ±5% in process conditions, such as temperature, flow rates, etc. The generated data was used to train support vector machines (SVM), Gaussian process regression (GPR), artificial neural networks (ANN), regression trees (RT), and ensemble trees (ET). Hyperparameter tuning was performed to enhance the prediction capabilities of GPR, ANN, SVM, ET and RT models. Performance analysis of the models indicates that GPR, ANN, and SVM with R2 values of 0.99, 0.978, and 0.979 and RMSE values of 0.108, 0.262, and 0.258, respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables. ET and RT had an R2 value of 0.94 and 0.89, respectively. The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method. Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%. The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.
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