Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a s...Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.展开更多
Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading fau...Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.展开更多
OBJECTIVE To observe the effect of preoperative chemoradiotherapy for inflammatory breast cancer. METHODS From December 1996 to December 2000, we received and treated 21 patients with inflammatory breast carcinoma wi...OBJECTIVE To observe the effect of preoperative chemoradiotherapy for inflammatory breast cancer. METHODS From December 1996 to December 2000, we received and treated 21 patients with inflammatory breast carcinoma with a combinedmodality treatment. The chemotherapy protocol consisted of cyclophosphamide （CTX）, pirarubicin （THP-ADM） and 5-fluorouracil （5-FU） or CTX, 5-Fu and methotrexate （MTX）. The same infusion scheme was repeated on day 21. After 3-4 cycles the patients were treated with radiotherapy. When the radiation dose reached 40 Gy, the patients who were unable or unwilling to under go an operation received continued radiotherapy. When the radiation dose to the supra clavicular fossa and internal mammary lymph nodes reached 60 Gy and 50 Gy respectively, the radiotherapy was stopped. Chemotherapy was then continued with the original scheme. Patients who had indications for surgery and were willing to under go an operation received no treatment for 2 weeks, after which a total mastectomy was performed. Chemotherapy and radiotherapy was resumed with the original scheme after the operations. When the radiation dose reached 50 Gy, radiotherapy was stopped. RESULTS All patients were followed-up for more than 5 years with a follow-up rate of 100%. The overall 3 and 5-year survival rates of these patients were 42.9%, and 23.8% respectively. For patients in Stage lliB the 3 and 5-year survival rates were 50.0% and 27.8% respectively, and for patients in Stage IV, the 3 and 5-year survival rates were both 0.0%. There was a significant difference between the 2 stage groups （P〈0.05, X^2=11.60）. For patients who received an operation, the 3 and 5-year survival rates were 80.0% and 33.3% respectively, For patients who were not treated with an operation, the 3 and 5-year survival rates were both 0.0%, There was a significant difference between the operated and nonoperated groups （P〈0.05, X2=11.64）. CONCLUSION The prognosis of inflammatory breast carcinoma is poor. Before operation, a combined-modality treatment （first chemotherapy, then local therapy, finally chemotherapy and radiotherapy） is the best treatment method.展开更多
Three-dimensional porous nitrogen-doped graphene aerogels （NGAs） were synthesized by using graphene oxide （GO） and chitosan （CS） via a self-assembly process by one-pot hydrothermal method. The morphology and str...Three-dimensional porous nitrogen-doped graphene aerogels （NGAs） were synthesized by using graphene oxide （GO） and chitosan （CS） via a self-assembly process by one-pot hydrothermal method. The morphology and struc- ture of the as-prepared materials were characterized by means of scanning electron microscopy, transmission elec- tron microscopy, X-ray diffraction, XPS spectroscopy, Raman spectroscopy, nitrogen adsorption/desorption meas- urement and Fourier transform infrared spectroscopy. The electrochemical performance of NGAs was studied by cyclic voltammetry, galvanostatic charge/discharge and impedance spectroscopy measurements. The microstructure, surface area and capacitance of NGAs could be facilely controlled by adding different amounts of chitosan. The prepared NGA-4 showed a specific capacitance of 148.0 F/g at the discharge current density of 0.5 A/g and also re- tained 95.3% of the initial capacitance after 5000 cycles at the scan rate of 10 mV/s. It provided a possible way to obtain graphene based materials with high surface area and capacitance.展开更多
基金the Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.
基金supported by Shanghai Rising-Star Program(No.22QA1403900)the National Natural Science Foundation of China(No.71804106)the Noncarbon Energy Conversion and Utilization Institute under the Shanghai Class IV Peak Disciplinary Development Program.
文摘Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.
文摘OBJECTIVE To observe the effect of preoperative chemoradiotherapy for inflammatory breast cancer. METHODS From December 1996 to December 2000, we received and treated 21 patients with inflammatory breast carcinoma with a combinedmodality treatment. The chemotherapy protocol consisted of cyclophosphamide （CTX）, pirarubicin （THP-ADM） and 5-fluorouracil （5-FU） or CTX, 5-Fu and methotrexate （MTX）. The same infusion scheme was repeated on day 21. After 3-4 cycles the patients were treated with radiotherapy. When the radiation dose reached 40 Gy, the patients who were unable or unwilling to under go an operation received continued radiotherapy. When the radiation dose to the supra clavicular fossa and internal mammary lymph nodes reached 60 Gy and 50 Gy respectively, the radiotherapy was stopped. Chemotherapy was then continued with the original scheme. Patients who had indications for surgery and were willing to under go an operation received no treatment for 2 weeks, after which a total mastectomy was performed. Chemotherapy and radiotherapy was resumed with the original scheme after the operations. When the radiation dose reached 50 Gy, radiotherapy was stopped. RESULTS All patients were followed-up for more than 5 years with a follow-up rate of 100%. The overall 3 and 5-year survival rates of these patients were 42.9%, and 23.8% respectively. For patients in Stage lliB the 3 and 5-year survival rates were 50.0% and 27.8% respectively, and for patients in Stage IV, the 3 and 5-year survival rates were both 0.0%. There was a significant difference between the 2 stage groups （P〈0.05, X^2=11.60）. For patients who received an operation, the 3 and 5-year survival rates were 80.0% and 33.3% respectively, For patients who were not treated with an operation, the 3 and 5-year survival rates were both 0.0%, There was a significant difference between the operated and nonoperated groups （P〈0.05, X2=11.64）. CONCLUSION The prognosis of inflammatory breast carcinoma is poor. Before operation, a combined-modality treatment （first chemotherapy, then local therapy, finally chemotherapy and radiotherapy） is the best treatment method.
基金We are grateful to Research Center of Laser Fusion, China Academy of Engineering Physics. This work was financially supported by the National Natural Science Foundation of China （No. 51502274）, the Research Fund for the Doctoral Program of Southwest University of Science and Technology （Nos. 13zx7124, 15zx7137, 16zx7142） and the Research Fund for Joint Laboratory for Extreme Conditions Matter Properties （Nos. 13zxjk04, 14tdjk03）.
文摘Three-dimensional porous nitrogen-doped graphene aerogels （NGAs） were synthesized by using graphene oxide （GO） and chitosan （CS） via a self-assembly process by one-pot hydrothermal method. The morphology and struc- ture of the as-prepared materials were characterized by means of scanning electron microscopy, transmission elec- tron microscopy, X-ray diffraction, XPS spectroscopy, Raman spectroscopy, nitrogen adsorption/desorption meas- urement and Fourier transform infrared spectroscopy. The electrochemical performance of NGAs was studied by cyclic voltammetry, galvanostatic charge/discharge and impedance spectroscopy measurements. The microstructure, surface area and capacitance of NGAs could be facilely controlled by adding different amounts of chitosan. The prepared NGA-4 showed a specific capacitance of 148.0 F/g at the discharge current density of 0.5 A/g and also re- tained 95.3% of the initial capacitance after 5000 cycles at the scan rate of 10 mV/s. It provided a possible way to obtain graphene based materials with high surface area and capacitance.
基金the National Natural Science Foundation of China (Grant No.71804106)Shanghai Sailing Program (No.17YF1406800)+1 种基金Shanghai Chenguang Program (No. 17CG57)The Key Fund of Shanghai Science Technology Committee (No.16020500900).
