AIM: To investigate oxaliplatin-induced severe anaphylactic reactions (SAR) in metastatic colorectal cancer in a retrospective case series analysis and to conduct a systemic literature review. METHODS: During a 6-year...AIM: To investigate oxaliplatin-induced severe anaphylactic reactions (SAR) in metastatic colorectal cancer in a retrospective case series analysis and to conduct a systemic literature review. METHODS: During a 6-year period from 2006 to 2011 at Kaohsiung Veterans General Hospital, a total of 412 patients exposed to oxaliplatin-related chemotherapy were retrospectively reviewed. Relevant Englishlanguage studies regarding life-threatening SAR following oxaliplatin were also reviewed in MEDLINE and PubMed search. RESULTS: Eight patients (1.9%, 8 of 412 cases) were identified. Seven patients were successful resuscitated without any sequelae and one patient expired. We changed the chemotherapy regimen in five patients and rechallenged oxaliplatin use in patient 3. Twenty-three relevant English-language studies with 66 patients were reported. Patients received a median of 10 cycles of oxaliplatin (range, 2 to 29). Most common symptoms were respiratory distress (60%), fever (55%), and hypotension (54%). Three fatal events were reported (4.5%). Eleven patients (16%) of the 66 cases were rechallenged by oxaliplatin. CONCLUSION: SAR must be considered in patients receiving oxaliplatin-related chemotherapy, especially in heavily pretreated patients. Further studies on the mechanism, predictors, preventive methods and management of oxaliplatin-related SAR are recommended.展开更多
A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing ...A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing various modes of mass transport and chemical reactions. To illustrate the application and value of this approach for reactor optimizations, the model is applied to the case of series reactions with a desirable intermediate compound and the risk of degradation of this compound if the process conditions are not optimized. The modeling results show that in such cases, which are very common in practice, replacing the conventional uniform catalyst distribution with a novel non-uniform distribution will significantly improve the performance of the reactor and the production of the desirable compound. Various catalyst distribution options are compared, and a novel non-uniform loading of catalyst is identified that gives a much better performance compared to the conventional approach. The model is versatile and useful for both the design as well as the optimization of the catalytic fixed-bed reactors in a wide variety of reactor and reaction conditions.展开更多
One of the main challenges in the design and operation of catalytic reactors for reactions with multiple paths/steps is the occurrence of undesirable reactions and products. In these cases, two main factors need to be...One of the main challenges in the design and operation of catalytic reactors for reactions with multiple paths/steps is the occurrence of undesirable reactions and products. In these cases, two main factors need to be considered in the reactor performance: the “conversion” of the feed and the “selectivity” of the process, which is the conversion split between the desired and the undesired products. In this work, a comprehensive model is developed and used to assess the impact of pore-size distribution (PSD) on both conversion and selectivity in series catalytic reactions. In particular, the evaluation considers the effects of various combinations of micro- and macro-porosity, the potential advantages of radial variation of the porosity in the catalyst pellets, and the effect of pellet size. Results show that, for series reactions, when the formation of the desired product is followed by an undesirable degradation reaction, higher porosity in pellets, particularly in the micro-range, gives higher overall conversion, but lowers selectivity towards the formation of the desired product. Selectivity in these pellets can be improved by using a non-uniform PSD that provides a radial gradient of effective diffusivity in pellets increasing from the center to the outer pellet surface. The pellet size also has a significant effect, and larger pellets show lower selectivity in most cases. In general, conversion and selectivity trends move in opposite directions with changes in PSD and the pore structural properties of pellets. Therefore, finding the optimum design of pellets is an optimization process that requires process modeling. Consequently, selecting the best catalyst properties involves optimization, and the needed tool is a comprehensive mathematical model that takes into account the details of mass transport and reaction kinetics in the catalyst pellets. Our primary objective has been the development of a flexible mathematical model that would be applicable to a wide range of conditions and can be used as a 展开更多
In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates ...In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.展开更多
This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-oft...This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-ofthe-art spatiotemporal forecasting neural network.To address the issue of shifted distribution in autoregressive long-term prediction,two training techniques are proposed:unrolled training and injecting noise training.These techniques significantly improve the stability and robustness of the model.Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner(Cabra burner)have been generated for model validation.The effects of model architecture,unrolled time,noise amplitude,and training dataset size on the long-term predictive performance are explored.The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady.展开更多
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification m...Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.展开更多
文摘AIM: To investigate oxaliplatin-induced severe anaphylactic reactions (SAR) in metastatic colorectal cancer in a retrospective case series analysis and to conduct a systemic literature review. METHODS: During a 6-year period from 2006 to 2011 at Kaohsiung Veterans General Hospital, a total of 412 patients exposed to oxaliplatin-related chemotherapy were retrospectively reviewed. Relevant Englishlanguage studies regarding life-threatening SAR following oxaliplatin were also reviewed in MEDLINE and PubMed search. RESULTS: Eight patients (1.9%, 8 of 412 cases) were identified. Seven patients were successful resuscitated without any sequelae and one patient expired. We changed the chemotherapy regimen in five patients and rechallenged oxaliplatin use in patient 3. Twenty-three relevant English-language studies with 66 patients were reported. Patients received a median of 10 cycles of oxaliplatin (range, 2 to 29). Most common symptoms were respiratory distress (60%), fever (55%), and hypotension (54%). Three fatal events were reported (4.5%). Eleven patients (16%) of the 66 cases were rechallenged by oxaliplatin. CONCLUSION: SAR must be considered in patients receiving oxaliplatin-related chemotherapy, especially in heavily pretreated patients. Further studies on the mechanism, predictors, preventive methods and management of oxaliplatin-related SAR are recommended.
文摘A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing various modes of mass transport and chemical reactions. To illustrate the application and value of this approach for reactor optimizations, the model is applied to the case of series reactions with a desirable intermediate compound and the risk of degradation of this compound if the process conditions are not optimized. The modeling results show that in such cases, which are very common in practice, replacing the conventional uniform catalyst distribution with a novel non-uniform distribution will significantly improve the performance of the reactor and the production of the desirable compound. Various catalyst distribution options are compared, and a novel non-uniform loading of catalyst is identified that gives a much better performance compared to the conventional approach. The model is versatile and useful for both the design as well as the optimization of the catalytic fixed-bed reactors in a wide variety of reactor and reaction conditions.
文摘One of the main challenges in the design and operation of catalytic reactors for reactions with multiple paths/steps is the occurrence of undesirable reactions and products. In these cases, two main factors need to be considered in the reactor performance: the “conversion” of the feed and the “selectivity” of the process, which is the conversion split between the desired and the undesired products. In this work, a comprehensive model is developed and used to assess the impact of pore-size distribution (PSD) on both conversion and selectivity in series catalytic reactions. In particular, the evaluation considers the effects of various combinations of micro- and macro-porosity, the potential advantages of radial variation of the porosity in the catalyst pellets, and the effect of pellet size. Results show that, for series reactions, when the formation of the desired product is followed by an undesirable degradation reaction, higher porosity in pellets, particularly in the micro-range, gives higher overall conversion, but lowers selectivity towards the formation of the desired product. Selectivity in these pellets can be improved by using a non-uniform PSD that provides a radial gradient of effective diffusivity in pellets increasing from the center to the outer pellet surface. The pellet size also has a significant effect, and larger pellets show lower selectivity in most cases. In general, conversion and selectivity trends move in opposite directions with changes in PSD and the pore structural properties of pellets. Therefore, finding the optimum design of pellets is an optimization process that requires process modeling. Consequently, selecting the best catalyst properties involves optimization, and the needed tool is a comprehensive mathematical model that takes into account the details of mass transport and reaction kinetics in the catalyst pellets. Our primary objective has been the development of a flexible mathematical model that would be applicable to a wide range of conditions and can be used as a
文摘In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.
基金support from the National Natural Science Foundation of China(Grant No.52250710681 and 52022091)Support from the UK Engineering and Physical Sciences Research Council under the project“UK Consortium on Mesoscale Engineering Sciences(UKCOMES)”(Grant No.EP/X035875/1)is also acknowledged.
文摘This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-ofthe-art spatiotemporal forecasting neural network.To address the issue of shifted distribution in autoregressive long-term prediction,two training techniques are proposed:unrolled training and injecting noise training.These techniques significantly improve the stability and robustness of the model.Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner(Cabra burner)have been generated for model validation.The effects of model architecture,unrolled time,noise amplitude,and training dataset size on the long-term predictive performance are explored.The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
文摘Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.
