Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making ...Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.展开更多
Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential bec...Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential because it allows timely intervention,which can slow disease progression and improve outcomes.Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD.In addition,the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis.Artificial intelligence(AI)techniques,especially deep and automated learning models,provide promising solutions to address deficiencies in manual diagnosis.This study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical images.Handwritten graphic images of the PD dataset are enhanced using two overlapping filters,the average filter and the Laplacian filter,to improve image quality and highlight essential features.The enhanced images are segmented to isolate regions of interest(ROIs)from the rest of the image using a gradient vector flow(GVF)algorithm,which ensures that features are extracted from only relevant regions.The segmented ROIs are fed into convolutional neural network(CNN)models,namely DenseNet169,MobileNet,and VGG16,to extract fine and deep feature maps that capture complex patterns and representations relevant to PD diagnosis.Fine and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet,MobileNet-VGG16,DenseNet169-VGG16,and DenseNet169-MobileNet-VGG16 models.This fusion technique aims to combine complementary and robust features from several models,which improves the extracted features.Two feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set:Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS).These algorithms identify and retain the most strongly correlated features while eliminating redundant and weakly correlated features,thus optimizing the features to improve system performance.The fused and enhanced feature vectors are fed into two powerful classifiers,XGBoost and random forest(RF),for accurate classification and differentiation between individuals with PD and healthy controls.The proposed hybrid systems show superior performance,where the RF classifier used the combined features from the DenseNet169-MobileNet-VGG16 models with the ACO feature selection method,achieving outstanding results:area under the curve(AUC)of 99%,sensitivity of 99.6%,99.3%accuracy,99.35%accuracy,and 99.65%specificity.展开更多
OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models ...OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)patients.Furthermore,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of TCM.RESULTS:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.CONCLUSIONS:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.展开更多
The purpose of this study was to establish factors that lead to poor integration of Information and communication technology （ICT） for teaching and learning in schools in Kenya, despite comprehensive policy, institu...The purpose of this study was to establish factors that lead to poor integration of Information and communication technology （ICT） for teaching and learning in schools in Kenya, despite comprehensive policy, institutional, infrastructural frameworks and capacity building by the Ministry of Education. The subject of this study was administered by use of questionnaires in three categories of public schools： national school, provincial schools and district schools. The respondents were students from each level that is from one, two, three and four and teachers based on the most offered subjects in the secondary schools. The computer assisted learning facilities were classified into computers, internet and content in optical media. In national school Internet based research, optical media content provided by Kenya Institute of Curriculum Development and Cyber School program for science subjects was used in learning. In provincial school, it lacks adequate computers, reliable Internet and content in optical media. In district school, it lacks adequate computer, no internet connection and content in optical media. A learner management system which can be accessed by all learners by use of any internet access devices like mobile phone access will be an ideal tool with over 4,000,000 mobile phone subscribers currently in Kenya.展开更多
The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Th...The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.展开更多
This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations...This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.展开更多
Owing to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for underwater sensor networks. Moreover, network coding is proved to be an effect...Owing to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for underwater sensor networks. Moreover, network coding is proved to be an effective coding technique for throughput and robustness of networks. In this paper, we propose a Reliable Braided Multipath Routing with Network Coding for underwater sensor networks （RBMR-NC）. Disjoint multi-path algorithm is used to build independent actual paths, as called main paths. Some braided paths on each main path are built according to the braided multi-path algorithm, which are called logic paths. When a data packet is transmitted by these nodes, the nodes can employ network coding to encode packets coming from the same group in order to further reduce relativity among these packets, and enhance the probability of successful decoding at the sink node. Braided multi-path can make the main paths to be multiplexed to reduce the probability of long paths. This paper mainly employs successful delivery rate to evaluate RBMR-NC model with theoretical analysis and simulation methods. The results indicate that the proposed RBMR-NC protocol is valuable to enhance network reliability and to reduce system redundancy.展开更多
An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained c...An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.展开更多
Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is e...Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is essential and critical. In this paper, we have solved two problems regarding to how to determine the position relation between points and curves without revealing any private information. Two protocols have been proposed in order to solve the problems in different conditions. In addition, some building blocks have been developed, such as scalar product protocol, so that we can take advantage of them to settle the privacy-preserving computational geometry problems which are a kind of special secure multi-party computation problems. Moreover, oblivious transfer and power series expansion serve as significant parts in our protocols. Analyses and proofs have also been given to argue our conclusion.展开更多
There are some disadvantages, such as complicated wiring, high cost, poor monitoring flexibility, low accuracy and high energy consumption in traditional greenhouse environment monitoring system which based on previou...There are some disadvantages, such as complicated wiring, high cost, poor monitoring flexibility, low accuracy and high energy consumption in traditional greenhouse environment monitoring system which based on previous wireless sensor networks （WSN）. Aiming at these problems, a greenhouse environmental parameter monitoring system had been designed based on internet of things technology in this paper. A set of control system with good robustness, strong adaptive ability and small overshoot was set up by combining the fuzzy proportion-integral-derivative （PID） control. The system was composed of a number of independent greenhouse monitoring systems. The server could provide remote monitoring access management services after the collected data were transmitted. The data transmission part of greenhouse was based on ZigBee networking protocol. And the data were sent to intelligent system via gateway connected to the internet. Compared to the classical PID control and fuzzy control, the fuzzy PID control could quickly and accurately adjust the corresponding parameters to the set target. The overshoot was also relatively small. The simulation results showed that the amount of overshoot was reduced 20% compared with classical PID control.展开更多
Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, b...Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, but their per-formance decreases with correlated noises. Coherence based methods can be improved when the cross power spectral density (CPSD) of correlated noise signals is available. In this paper, we propose a new method for estimation of the CPSD of the noise, which is based on the minimum tracking technique. Despite the fact that the proposed estimator does not need to implement a voice activity detector (VAD), its performance is comparable to a CPSD estimator that uses an ideal VAD.展开更多
To solve the disability of conventional model used in electrical leak location when measurement electrodes were buried under the liner, a new model of high voltage DC leak detection is developed. For single-liner land...To solve the disability of conventional model used in electrical leak location when measurement electrodes were buried under the liner, a new model of high voltage DC leak detection is developed. For single-liner landfill, the waste material layer, the geomembrane liner and the soil under the liner are simulated with infinite horizontal layers. The leak is regarded as two parts, one being negative current source at the entrance, and the other positive current source of the same size at the exit. Comparisons between the new theoretical model and conventional model show that conventional model is efficient in locating leaks in geomembane liner associating the dipole scanning above the liner but is ineffective when the measurement electrodes were buried under the liner. The new theoretical model data are in excellent agreement with experimental data not only above the liner but also under the liner.展开更多
The pathogenesis of hypertrophic cardiomyopathy(HCM)is very complicated,particularly regarding the role of circular RNA(circRNA).This research pays special attention to the relationships of the circRNA-mediated networ...The pathogenesis of hypertrophic cardiomyopathy(HCM)is very complicated,particularly regarding the role of circular RNA(circRNA).This research pays special attention to the relationships of the circRNA-mediated network,including RNA-RNA relationships and RNA-RNA binding protein(RNA-RBP)relationships.We use the parameter framework technology proposed in this paper to screen differentially expressed circRNA,messenger RNA(mRNA),and microRNA(miRNA)from the expression profile of samples related to HCM.And 31 pairs of circRNA and mRNA relationship pairs were extracted,combined with the miRNA targeting database;145 miRNA-mRNA relationship pairs were extracted;268 circRNA-mRNA-miRNA triads were established through the common mRNA in the 2 types of relationship pairs.Thus,268 circRNA-miRNA regulatory relationships were deduced and 30 circRNARBP relationship pairs were analyzed at the protein level.On this basis,a circRNA-mediated regulatory network corresponding to the two levels of RNA-RNA and RNA-RBP was established.And then the roles of circRNA in HCM were analyzed through circRNA-mRNA,circRNA-miRNA,and circRNA-RBP,and the possible role in disease development mas inferred.展开更多
In this paper,we propose and numerically investigate a novel circular lattice photonic crystal fiber(CL-PCF)using controllable GeO_(2) doped silica,suitable for modes carrying quantized orbital angular momentum(OAM).L...In this paper,we propose and numerically investigate a novel circular lattice photonic crystal fiber(CL-PCF)using controllable GeO_(2) doped silica,suitable for modes carrying quantized orbital angular momentum(OAM).Large effective index separations between 25 supported vector modes(≥10^(-4))are confirmed over large bandwidth(C+L bands)leading to 48 OAM modes bearing information.The simulations show that the modes in the proposed CLPCF have good features including low and flat dispersion(within 51.82 ps/km/nm),low confinement loss(lower than 0.002 d B/m),high effective mode area(88.5μm^(2))and low nonlinearity(1.22 W^(-1)·km^(-1)).These promising results show that the proposed CL-PCF could be an arguably candidate in fiber-based OAM multiplexing or other applications using OAM states.展开更多
As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiom...As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.展开更多
In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of...In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.展开更多
A mobile molecular Doppler wind lidar （DWL） based on double-edge technique is presented for wind measurement at altitudes from 10 km to 40 km. A triple Fabry-Perot etalon is employed as a frequency discriminator to ...A mobile molecular Doppler wind lidar （DWL） based on double-edge technique is presented for wind measurement at altitudes from 10 km to 40 km. A triple Fabry-Perot etalon is employed as a frequency discriminator to determine the Doppler shift proportional to the wind velocity. The lidar operates at 355 nm with a 45-cm aperture telescope and a matching azimuth-over-elevation scanner that can provide full hemispherical pointing. In order to guarantee the wind accuracy, different forms of calibration function of detectors in different count rates response range would be especially valuable. The accuracy of wind velocity iteration is improved greatly because of application of the calibration function of linearity at the ultra low light intensity especially at altitudes from 10 km to 40 km. The calibration functions of nonlinearity make the transmission of edge channel 1 and edge channel 2 increase 38.9% and 27.7% at about 1 M count rates, respectively. The dynamic range of wind field measurement may also be extended because of consideration of the response function of detectors in their all possible operating range.展开更多
文摘Multiple Sclerosis(MS)poses significant health risks.Patients may face neurodegeneration,mobility issues,cognitive decline,and a reduced quality of life.Manual diagnosis by neurologists is prone to limitations,making AI-based classification crucial for early detection.Therefore,automated classification using Artificial Intelligence(AI)techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages.This study developed hybrid systems integrating XGBoost(eXtreme Gradient Boosting)with multi-CNN(Convolutional Neural Networks)features based on Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS)algorithms for early classification of MRI(Magnetic Resonance Imaging)images in a multi-class and binary-class MS dataset.All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization(CLAHE).Then,the Gradient Vector Flow(GVF)algorithm was applied to select white matter(regions of interest)within the brain and segment them from the surrounding brain structures.These regions of interest were processed by CNN models(ResNet101,DenseNet201,and MobileNet)to extract deep feature maps,which were then combined into fused feature vectors of multi-CNN model combinations(ResNet101-DenseNet201,DenseNet201-MobileNet,ResNet101-MobileNet,and ResNet101-DenseNet201-MobileNet).The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features.The XGBoost classifier employed the resultant feature vectors for classification.All developed hybrid systems displayed promising outcomes.For multiclass classification,the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4%accuracy,99.45%precision,and 99.75%specificity,surpassing prior studies(93.76%accuracy).It reached 99.6%accuracy,99.65%precision,and 99.55%specificity in binary-class classification.These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.
文摘Parkinson’s disease(PD)is a progressive neurodegenerative disorder characterized by tremors,rigidity,and decreased movement.PD poses risks to individuals’lives and independence.Early detection of PD is essential because it allows timely intervention,which can slow disease progression and improve outcomes.Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD.In addition,the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis.Artificial intelligence(AI)techniques,especially deep and automated learning models,provide promising solutions to address deficiencies in manual diagnosis.This study develops robust systems for PD diagnosis by analyzing handwritten helical and wave graphical images.Handwritten graphic images of the PD dataset are enhanced using two overlapping filters,the average filter and the Laplacian filter,to improve image quality and highlight essential features.The enhanced images are segmented to isolate regions of interest(ROIs)from the rest of the image using a gradient vector flow(GVF)algorithm,which ensures that features are extracted from only relevant regions.The segmented ROIs are fed into convolutional neural network(CNN)models,namely DenseNet169,MobileNet,and VGG16,to extract fine and deep feature maps that capture complex patterns and representations relevant to PD diagnosis.Fine and deep feature maps extracted from individual CNN models are combined into fused feature vectors for DenseNet169-MobileNet,MobileNet-VGG16,DenseNet169-VGG16,and DenseNet169-MobileNet-VGG16 models.This fusion technique aims to combine complementary and robust features from several models,which improves the extracted features.Two feature selection algorithms are considered to remove redundancy and weak correlations within the combined feature set:Ant Colony Optimization(ACO)and Maximum Entropy Score-based Selection(MESbS).These algorithms identify and retain the most strongly correlated features while eliminating redundant and weakly correlated features,thus optimizing the features to improve system performance.The fused and enhanced feature vectors are fed into two powerful classifiers,XGBoost and random forest(RF),for accurate classification and differentiation between individuals with PD and healthy controls.The proposed hybrid systems show superior performance,where the RF classifier used the combined features from the DenseNet169-MobileNet-VGG16 models with the ACO feature selection method,achieving outstanding results:area under the curve(AUC)of 99%,sensitivity of 99.6%,99.3%accuracy,99.35%accuracy,and 99.65%specificity.
基金the China Academy of Chinese Medical Sciences,Independent Topic Project:Application Research on Named Entity Recognition and Relationship Extraction of Case Records of Renowned Traditional Chinese Medicine Practitioners(No.Z0643).China Academy of Chinese Medical Sciences,Independent Topic Project:Analysis of Research Directions and Scope in the Discipline of Traditional Chinese Medicine Statistics(No.Z0723)China Academy of Chinese Medical Sciences,Science and Technology Innovation Project:Real-world Effectiveness Evaluation of Traditional Chinese Medicine and Translational Application Research on Causal Inference(No.CI2021A04706).China Academy of Chinese Medical Sciences,Science and Technology Innovation Project:Research on Causal Inference Methodology for Real-world Clinical Evaluation in Traditional Chinese Medicine(No.CI2021B003)National Key Research and Development Program of China:Integrated Evaluation Model and Key Technologies of"Syndrome-Disease-Prescription"for Traditional Chinese Medicine in the Prevention and Treatment of Coronary Heart Disease—Statistical Data Analysis and Data Mining(No.2017YFC1700406-2)。
文摘OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM drugs.METHODS:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)patients.Furthermore,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of TCM.RESULTS:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.CONCLUSIONS:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.
文摘The purpose of this study was to establish factors that lead to poor integration of Information and communication technology （ICT） for teaching and learning in schools in Kenya, despite comprehensive policy, institutional, infrastructural frameworks and capacity building by the Ministry of Education. The subject of this study was administered by use of questionnaires in three categories of public schools： national school, provincial schools and district schools. The respondents were students from each level that is from one, two, three and four and teachers based on the most offered subjects in the secondary schools. The computer assisted learning facilities were classified into computers, internet and content in optical media. In national school Internet based research, optical media content provided by Kenya Institute of Curriculum Development and Cyber School program for science subjects was used in learning. In provincial school, it lacks adequate computers, reliable Internet and content in optical media. In district school, it lacks adequate computer, no internet connection and content in optical media. A learner management system which can be accessed by all learners by use of any internet access devices like mobile phone access will be an ideal tool with over 4,000,000 mobile phone subscribers currently in Kenya.
文摘The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.
文摘This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.
基金the National High-Tech Research and Development Plan of China under Grant No.2007AA01Z203(国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2007CB307101-4(国家重点基础研究发展计划(973))
基金supported by the National Natural Science Foundation of China (Grant Nos.60472060 and 60473039)the National High Technology Research and Development Programof China (863 Program,Grant No.2006AA01Z119)the Innovation Fund of Chinese Academy of Space Technology (Grant No.CAST20090801)
文摘Owing to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for underwater sensor networks. Moreover, network coding is proved to be an effective coding technique for throughput and robustness of networks. In this paper, we propose a Reliable Braided Multipath Routing with Network Coding for underwater sensor networks （RBMR-NC）. Disjoint multi-path algorithm is used to build independent actual paths, as called main paths. Some braided paths on each main path are built according to the braided multi-path algorithm, which are called logic paths. When a data packet is transmitted by these nodes, the nodes can employ network coding to encode packets coming from the same group in order to further reduce relativity among these packets, and enhance the probability of successful decoding at the sink node. Braided multi-path can make the main paths to be multiplexed to reduce the probability of long paths. This paper mainly employs successful delivery rate to evaluate RBMR-NC model with theoretical analysis and simulation methods. The results indicate that the proposed RBMR-NC protocol is valuable to enhance network reliability and to reduce system redundancy.
文摘An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.
基金Supported by the National Natural Science Foundation of China (No. 61070189, 60673065)the National High Technology Development Program (No. 2008AA01Z419)
文摘Numerous privacy-preserving issues have emerged along with the fast development of Internet, both in theory and in real-life applications. To settle the privacy-preserving problems, secure multi-party computation is essential and critical. In this paper, we have solved two problems regarding to how to determine the position relation between points and curves without revealing any private information. Two protocols have been proposed in order to solve the problems in different conditions. In addition, some building blocks have been developed, such as scalar product protocol, so that we can take advantage of them to settle the privacy-preserving computational geometry problems which are a kind of special secure multi-party computation problems. Moreover, oblivious transfer and power series expansion serve as significant parts in our protocols. Analyses and proofs have also been given to argue our conclusion.
基金Supported by the 13th Five-year National Key R&D Program:Development and Verification of Information Perception and Environment Intelligent Control System for Dairy Cattle and Beef Cattle(2016YFD0700204-02)Quality and Brand Construction of "Internet+County Characteristic Agricultural Products"(ZY17C06)
文摘There are some disadvantages, such as complicated wiring, high cost, poor monitoring flexibility, low accuracy and high energy consumption in traditional greenhouse environment monitoring system which based on previous wireless sensor networks （WSN）. Aiming at these problems, a greenhouse environmental parameter monitoring system had been designed based on internet of things technology in this paper. A set of control system with good robustness, strong adaptive ability and small overshoot was set up by combining the fuzzy proportion-integral-derivative （PID） control. The system was composed of a number of independent greenhouse monitoring systems. The server could provide remote monitoring access management services after the collected data were transmitted. The data transmission part of greenhouse was based on ZigBee networking protocol. And the data were sent to intelligent system via gateway connected to the internet. Compared to the classical PID control and fuzzy control, the fuzzy PID control could quickly and accurately adjust the corresponding parameters to the set target. The overshoot was also relatively small. The simulation results showed that the amount of overshoot was reduced 20% compared with classical PID control.
基金Project supported by the Iran Telecommunications Research Center (ITRC)
文摘Some two-microphone noise reduction techniques that work in the frequency domain exploit coherence function between two noisy signals. They have shown good results when noise signals on two sensors are uncorrelated, but their per-formance decreases with correlated noises. Coherence based methods can be improved when the cross power spectral density (CPSD) of correlated noise signals is available. In this paper, we propose a new method for estimation of the CPSD of the noise, which is based on the minimum tracking technique. Despite the fact that the proposed estimator does not need to implement a voice activity detector (VAD), its performance is comparable to a CPSD estimator that uses an ideal VAD.
基金Project supported by the National High-Technology Research and Development Program of China (Grant No. 863-2001AA644010)
文摘To solve the disability of conventional model used in electrical leak location when measurement electrodes were buried under the liner, a new model of high voltage DC leak detection is developed. For single-liner landfill, the waste material layer, the geomembrane liner and the soil under the liner are simulated with infinite horizontal layers. The leak is regarded as two parts, one being negative current source at the entrance, and the other positive current source of the same size at the exit. Comparisons between the new theoretical model and conventional model show that conventional model is efficient in locating leaks in geomembane liner associating the dipole scanning above the liner but is ineffective when the measurement electrodes were buried under the liner. The new theoretical model data are in excellent agreement with experimental data not only above the liner but also under the liner.
基金the National Natural Science Foundation of China under Grant No.61872405the Key R&D program of Sichuan Province under Grant No.2020YFS0243the Key Project of Natural Science Foundation of Guangdong Province under Grant No.2016A030311040.
文摘The pathogenesis of hypertrophic cardiomyopathy(HCM)is very complicated,particularly regarding the role of circular RNA(circRNA).This research pays special attention to the relationships of the circRNA-mediated network,including RNA-RNA relationships and RNA-RNA binding protein(RNA-RBP)relationships.We use the parameter framework technology proposed in this paper to screen differentially expressed circRNA,messenger RNA(mRNA),and microRNA(miRNA)from the expression profile of samples related to HCM.And 31 pairs of circRNA and mRNA relationship pairs were extracted,combined with the miRNA targeting database;145 miRNA-mRNA relationship pairs were extracted;268 circRNA-mRNA-miRNA triads were established through the common mRNA in the 2 types of relationship pairs.Thus,268 circRNA-miRNA regulatory relationships were deduced and 30 circRNARBP relationship pairs were analyzed at the protein level.On this basis,a circRNA-mediated regulatory network corresponding to the two levels of RNA-RNA and RNA-RBP was established.And then the roles of circRNA in HCM were analyzed through circRNA-mRNA,circRNA-miRNA,and circRNA-RBP,and the possible role in disease development mas inferred.
文摘In this paper,we propose and numerically investigate a novel circular lattice photonic crystal fiber(CL-PCF)using controllable GeO_(2) doped silica,suitable for modes carrying quantized orbital angular momentum(OAM).Large effective index separations between 25 supported vector modes(≥10^(-4))are confirmed over large bandwidth(C+L bands)leading to 48 OAM modes bearing information.The simulations show that the modes in the proposed CLPCF have good features including low and flat dispersion(within 51.82 ps/km/nm),low confinement loss(lower than 0.002 d B/m),high effective mode area(88.5μm^(2))and low nonlinearity(1.22 W^(-1)·km^(-1)).These promising results show that the proposed CL-PCF could be an arguably candidate in fiber-based OAM multiplexing or other applications using OAM states.
基金the National Natural Science Foundation of China(61761023,31760315)the Natural Science Foundation of Jiangxi Province,China(20202BABL202004,20202BAB202007)the Scientific Research Plan of the Department of Education of Jiangxi Province(GJJ190695)。
文摘As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.
文摘In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.
文摘A mobile molecular Doppler wind lidar （DWL） based on double-edge technique is presented for wind measurement at altitudes from 10 km to 40 km. A triple Fabry-Perot etalon is employed as a frequency discriminator to determine the Doppler shift proportional to the wind velocity. The lidar operates at 355 nm with a 45-cm aperture telescope and a matching azimuth-over-elevation scanner that can provide full hemispherical pointing. In order to guarantee the wind accuracy, different forms of calibration function of detectors in different count rates response range would be especially valuable. The accuracy of wind velocity iteration is improved greatly because of application of the calibration function of linearity at the ultra low light intensity especially at altitudes from 10 km to 40 km. The calibration functions of nonlinearity make the transmission of edge channel 1 and edge channel 2 increase 38.9% and 27.7% at about 1 M count rates, respectively. The dynamic range of wind field measurement may also be extended because of consideration of the response function of detectors in their all possible operating range.
