As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or...Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.展开更多
Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only...Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.展开更多
As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional gam...As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.展开更多
A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven sym...A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven symmetric inductor to the f SR of the single-ended driven inductor is firstly predicted and explained.Compared with a single-ended configuration,experimental data demonstrate that the differential inductor offers a 127% greater maximum quality factor and a broader range of operating frequencies.Two differential inductors with low parasitical capacitance are developed and validated.展开更多
The optimum pressure ratio distribution of a multistage reciprocating compressor is presented based on the assumption, i.e. the inter stage cooling is perfect and there are no pressure losses. The optimization of the...The optimum pressure ratio distribution of a multistage reciprocating compressor is presented based on the assumption, i.e. the inter stage cooling is perfect and there are no pressure losses. The optimization of the two or three stage pressure ratio is analyzed in two cases of constant heat transfer rate for the inter cooler or constant inter stage inlet temperature, based on the minimum of the sum of theoretical compression power at each stage about a multi stage reciprocating compressor. Furthermore, with an example of two stage compressor the influence on the sum of the power of each stage is analyzed when practical pressure ratio deviates from the optimum value. It is obtained that under different cooling conditions the optimum pressure ratio distribution of the multi stage compression is various, and the change of the optimum pressure ratio within a small range has little influence on the sum of the power each stage. For the two stage compression, this range can be represented as ε 1=(0 96～1 06)ε 1j .展开更多
[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition ...[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition model of potato inter- cropped with sugarcane. [Method]Two field experiments adopted the optimum com- pound design （311-A） were conducted in Long＇an County of Guangxi Province in 2011 and 2012, respectively. The polynomial regression models of fertilizer applica- tion and quadratic of three factors were established by SAS statistical analysis soft- ware, and optimum nutrient simulation models of potato were obtained by computer processing. [Result] The combined application of low nitrogen and mid-high potassi- um and phosphorus fertilizer contributed to higher potato yield in experimental condi- tion. The regression model of potato yield （Yll and Y12） and dosage of N（X1）, P （X2）, K（X3） were established by using SAS statistical analysis software, in 2011 and 2012, respectively. They were Y11 =14 725.28 -415.39X1 ＋741.99X2 ＋607.83）（3-447.92X1X2- 144.09X1X3 -405.83X2X3 -267.82X1^2-795.67X2^2 -642.10X3^2, R =0.927 2; and Y12 =14 342.60 -896.25X1 ＋548.62X2 ＋925.51 X3 ＋67.81 X1X2 ＋531.60X1X3 -99.00X2X3 -904.00X1^2 - 1121.36X2^2-596.64X3^2,R=0.926 6. The regression mathematics model of potato yields preferably fit with actual situation in the locality, and have higher practical value, so it could be used for fertilizer decision and forecast. Using the computer to carry on the optimization, the N, P and K dosage of the best potato yield intercropped with sugarcane was obtained. The dosage of N, P2O5, K2O were 108.8-140.6, 172.5-204.4 and 285.9 kg/hm2, respectively. [Conclusion] The best N, P and K ratio of potato yield intercropped with sugarcane was 1：（1.23-1.68）：（2.03-2.63）.展开更多
This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combin...This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.展开更多
Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the ...Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the question of an optimum age for foreign language learning is not a simple one which is only related to age. There are different optimum ages for different aims and demands of learning foreign language.展开更多
By the optimum theory, a new cutting analytical method of the membrane structure is developed. The B-spline curve is applied to make smooth the boundary of the membrane strip. By this method, the cutting accuracy is i...By the optimum theory, a new cutting analytical method of the membrane structure is developed. The B-spline curve is applied to make smooth the boundary of the membrane strip. By this method, the cutting accuracy is improved. Finally, a cutting analysis example of a tension membrane structure is given.展开更多
The impedance of a solid state active phased array antenna varing with frequency and beam scanning scanning angle be matched with the solid state active matching network (SSAMN). In order to adjust and measure the rad...The impedance of a solid state active phased array antenna varing with frequency and beam scanning scanning angle be matched with the solid state active matching network (SSAMN). In order to adjust and measure the radar conveniently and Securely, it is necessary for the impedance of the simulator of the phased array antennas to be optimized.Having selected the PIN dilde controlling circuits and the circuit parameters optimized,the simulator circuit is determined through numerical computation The experiment is given in support of the simulation.展开更多
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金funded by Scientific Research Deanship at University of Hail-Saudi Arabia through Project Number RG-23092.
文摘Cyberbullying on social media poses significant psychological risks,yet most detection systems over-simplify the task by focusing on binary classification,ignoring nuanced categories like passive-aggressive remarks or indirect slurs.To address this gap,we propose a hybrid framework combining Term Frequency-Inverse Document Frequency(TF-IDF),word-to-vector(Word2Vec),and Bidirectional Encoder Representations from Transformers(BERT)based models for multi-class cyberbullying detection.Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships,fused with BERT’s contextual embeddings to capture syntactic and semantic complexities.We evaluate the framework on a publicly available dataset of 47,000 annotated social media posts across five cyberbullying categories:age,ethnicity,gender,religion,and indirect aggression.Among BERT variants tested,BERT Base Un-Cased achieved the highest performance with 93%accuracy(standard deviation across±1%5-fold cross-validation)and an average AUC of 0.96,outperforming standalone TF-IDF(78%)and Word2Vec(82%)models.Notably,it achieved near-perfect AUC scores(0.99)for age and ethnicity-based bullying.A comparative analysis with state-of-the-art benchmarks,including Generative Pre-trained Transformer 2(GPT-2)and Text-to-Text Transfer Transformer(T5)models highlights BERT’s superiority in handling ambiguous language.This work advances cyberbullying detection by demonstrating how hybrid feature extraction and transformer models improve multi-class classification,offering a scalable solution for moderating nuanced harmful content.
基金supported by King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding Program(ORF-2025-951).
文摘Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.
文摘As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.
文摘A distributed capacitance model for monolithic inductors is developed to predict the equivalently parasitical capacitances of the inductor.The ratio of the self-resonant frequency (f SR) of the differential-driven symmetric inductor to the f SR of the single-ended driven inductor is firstly predicted and explained.Compared with a single-ended configuration,experimental data demonstrate that the differential inductor offers a 127% greater maximum quality factor and a broader range of operating frequencies.Two differential inductors with low parasitical capacitance are developed and validated.
文摘The optimum pressure ratio distribution of a multistage reciprocating compressor is presented based on the assumption, i.e. the inter stage cooling is perfect and there are no pressure losses. The optimization of the two or three stage pressure ratio is analyzed in two cases of constant heat transfer rate for the inter cooler or constant inter stage inlet temperature, based on the minimum of the sum of theoretical compression power at each stage about a multi stage reciprocating compressor. Furthermore, with an example of two stage compressor the influence on the sum of the power of each stage is analyzed when practical pressure ratio deviates from the optimum value. It is obtained that under different cooling conditions the optimum pressure ratio distribution of the multi stage compression is various, and the change of the optimum pressure ratio within a small range has little influence on the sum of the power each stage. For the two stage compression, this range can be represented as ε 1=(0 96～1 06)ε 1j .
基金Supported by Guangxi Science and Technology Research Projects (GKG10100004-10)The Earmarked Fund for China Agriculture Research System (CARS-20-3-5)Science and Technology Development Fund Project of Guangxi Academy of Agricultural Science (GNK 2011jz07)~~
文摘[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition model of potato inter- cropped with sugarcane. [Method]Two field experiments adopted the optimum com- pound design （311-A） were conducted in Long＇an County of Guangxi Province in 2011 and 2012, respectively. The polynomial regression models of fertilizer applica- tion and quadratic of three factors were established by SAS statistical analysis soft- ware, and optimum nutrient simulation models of potato were obtained by computer processing. [Result] The combined application of low nitrogen and mid-high potassi- um and phosphorus fertilizer contributed to higher potato yield in experimental condi- tion. The regression model of potato yield （Yll and Y12） and dosage of N（X1）, P （X2）, K（X3） were established by using SAS statistical analysis software, in 2011 and 2012, respectively. They were Y11 =14 725.28 -415.39X1 ＋741.99X2 ＋607.83）（3-447.92X1X2- 144.09X1X3 -405.83X2X3 -267.82X1^2-795.67X2^2 -642.10X3^2, R =0.927 2; and Y12 =14 342.60 -896.25X1 ＋548.62X2 ＋925.51 X3 ＋67.81 X1X2 ＋531.60X1X3 -99.00X2X3 -904.00X1^2 - 1121.36X2^2-596.64X3^2,R=0.926 6. The regression mathematics model of potato yields preferably fit with actual situation in the locality, and have higher practical value, so it could be used for fertilizer decision and forecast. Using the computer to carry on the optimization, the N, P and K dosage of the best potato yield intercropped with sugarcane was obtained. The dosage of N, P2O5, K2O were 108.8-140.6, 172.5-204.4 and 285.9 kg/hm2, respectively. [Conclusion] The best N, P and K ratio of potato yield intercropped with sugarcane was 1：（1.23-1.68）：（2.03-2.63）.
基金The National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z202)the National Key Technology R & D Program of China during the 11th Five-Year Plan Period(No. 2006BAJ18B03)the Fundamental Research Funds for the Central Universities (No. DUT10RC(3) 112)
文摘This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.
文摘Age is one of the factors which influnce foreign language learning, but not the most important one. Comparision on the effect of foreign language learning between adults and children cannot rely merely on age. So the question of an optimum age for foreign language learning is not a simple one which is only related to age. There are different optimum ages for different aims and demands of learning foreign language.
文摘By the optimum theory, a new cutting analytical method of the membrane structure is developed. The B-spline curve is applied to make smooth the boundary of the membrane strip. By this method, the cutting accuracy is improved. Finally, a cutting analysis example of a tension membrane structure is given.
文摘The impedance of a solid state active phased array antenna varing with frequency and beam scanning scanning angle be matched with the solid state active matching network (SSAMN). In order to adjust and measure the radar conveniently and Securely, it is necessary for the impedance of the simulator of the phased array antennas to be optimized.Having selected the PIN dilde controlling circuits and the circuit parameters optimized,the simulator circuit is determined through numerical computation The experiment is given in support of the simulation.
