Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation pred...Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry.This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period.Solar energy is the cheapest form of clean energy,and due to the intermittent nature of the energy,accurate forecasting across multiple timeframes is necessary for efficient generation and demand management.Solar radiation prediction using deep learning(DL)includes the applications of neural network methods,namely Convolutional Neural Network(CNN)or Long Short-Term Memory(LSTM)models,to forecast and model solar irradiance patterns.By leveraging meteorological variables and historical solar radiation data,DL algorithms can capture complex spatial and temporal dependencies,resulting in accurate predictions.This article presents a novel Solar Radiation Prediction model utilizing a Boosted Coyote Optimization Algorithm with Deep Learning(SRP-BCOADL).The SRP-BCOADL model initially normalizes the input data using a min-max normalization approach to improve the robust nature under different scales.Besides,the SRP-BCOADL technique uses a Deep Long Short-Term Memory Autoencoder(DLSTM-AE)system for precisely forecasting solar radiation levels.The model’s accuracy is further improved through hyperparameter optimization using the BCOA.The performance analysis of the SRP-BCOADL technique is tested using solar radiation data.Extensive experimental outcomes prove that the SRP-BCOADL method obtains better results over other techniques.The Mean Squared Error(MSE)is just 0.13 kWh/m^(2),is much lower when compared to other models.The Root Mean Squared Error(RMSE)is also reduced to 0.36 kWh/m^(2),and the Mean Absolute Error(MAE)reaches a minimal level of 0.276 kWh/m^(2).展开更多
Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern clas...Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.展开更多
We present a study about the flavor changing coupling of the top quark with the Higgs boson through the channe■at LHC.The final states considered for the such process are■.We focus on the boosted region in the phase...We present a study about the flavor changing coupling of the top quark with the Higgs boson through the channe■at LHC.The final states considered for the such process are■.We focus on the boosted region in the phase space of the Higgs boson.The backgrounds and events are simulated and analyzed.The sensitivities for the FCNH couplings are estimated.It is found that it is more sensitive for ytu than ytq at LHC.The upper limits of the FCNH couplings can be set at LHC with 3000 fb-1integrated luminosity as■95%C.L.展开更多
Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Re...Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.展开更多
This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from N...This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.展开更多
This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock pric...This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.展开更多
Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the qual...Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the network.The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes.The route identification and routing are established based on the data broadcast by the network nodes.In transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network.Furthermore,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate.In this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery rate.Initially,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing routing.The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET.Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation.Next,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency.The proposed GBEOM exhibits∼4%improved performance in MANET routing.展开更多
Dry streams filled with sand,and sun-baked soil and drought resistant mopane trees characterize vast expanse of land in the rural Chiredzi District,more than 600 km southeast of Zimbabwe’s capital Harare.Topless and ...Dry streams filled with sand,and sun-baked soil and drought resistant mopane trees characterize vast expanse of land in the rural Chiredzi District,more than 600 km southeast of Zimbabwe’s capital Harare.Topless and barefooted children make a beeline waving at modern non-governmental organization vehicles which frequent the district.展开更多
Anomaly detection is crucial for data-driven applications in integrated energy systems.Traditional anomaly detection methods primarily focus on one single energy load,often neglecting potential spatial correlations be...Anomaly detection is crucial for data-driven applications in integrated energy systems.Traditional anomaly detection methods primarily focus on one single energy load,often neglecting potential spatial correlations between multivariate energy time series.Meanwhile,addressing the imbalanced nature of user-level multi-en-ergy load data remains a significant challenge.In this paper,we propose EGBAD,an Ensemble Graph-Boosted Anomaly Detection framework for user-level multi-energy load that leverages the advantages of graph relational analysis and ensemble learning.First,a dynamic graph construction method based on multidimensional scaling(MDS)is proposed to transform multi-energy load data into graph representations.These graphs are subse-quently processed using graph convolutional network(GCN)to capture the spatiotemporal correlations between multi-energy load time series.In addition,to improve detection robustness under class imbalance,the entire training process is embedded within a Boosting ensemble learning framework,where the weight assigned to the minority class is progressively increased at each boosting stage.Experimental results on publicly real-world datasets demonstrate that the proposed model achieves superior anomaly detection accuracy compared to most baseline methods.Notably,it performs especially well in scenarios characterized by extreme data imbal-ance,achieving the highest recall and F1-score for anomaly detection.展开更多
Correction to:Signal Transduction and Targeted Therapy https://doi.org/10.1038/s41392-021-00474-x,published online 29 January 2021 In the process of collating the raw data,the authors noticed an inadvertent mistakes o...Correction to:Signal Transduction and Targeted Therapy https://doi.org/10.1038/s41392-021-00474-x,published online 29 January 2021 In the process of collating the raw data,the authors noticed an inadvertent mistakes occurred in Supplementary Fig.11c that need to be corrected after online publication of the article.1 Due to our negligence in extracting and processing a large amount of experimental data,duplicate images were inadvertently used for the MPTP group and the MPTP+lovastatin group of mice with dopaminergic neurons deficient in SHP2(SHP2TH-/-).The correct data are provided as follows.The keyfindings of the article are not affected by these corrections.展开更多
精确评估电池的荷电状态(state of charge,SOC)是实现高效储能电池管理的前提。当前利用阻抗估计电池SOC多基于非原位电化学阻抗谱,由于此技术要求充分的静置时间,使得通过阻抗动态估计电池SOC困难。考虑到储能电站的实际运行情况,实时...精确评估电池的荷电状态(state of charge,SOC)是实现高效储能电池管理的前提。当前利用阻抗估计电池SOC多基于非原位电化学阻抗谱,由于此技术要求充分的静置时间,使得通过阻抗动态估计电池SOC困难。考虑到储能电站的实际运行情况,实时快速地获取阻抗数据成为关键。然而工况下受到直流偏置影响,电池电压的非线性变化会导致中低频阻抗产生偏移,影响阻抗测量准确性。针对以上问题,该研究采用离散间隔二进制序列设计了一种动态电化学阻抗谱测量方法,结合电池充放电工况下的阻抗特性,引入Categorical Boosting算法构建了电池SOC估计模型。针对4块商用18650锂电池,在不同温度和充放电倍率下每隔1%SOC重复测量了电池在充放电过程中的动态阻抗。实验结果表明,在不同实验条件下,电池充放电工况下SOC估计的最大平均绝对误差和均方根误差分别为2.98%和3.59%,证明了所提方法的可靠性和鲁棒性。展开更多
Engineering of enzyme microenvironment can surprisingly boost the apparent activity.However,the underlying regulation mechanism is not well-studied at a molecular level so far.Here,we present a modulation of two model...Engineering of enzyme microenvironment can surprisingly boost the apparent activity.However,the underlying regulation mechanism is not well-studied at a molecular level so far.Here,we present a modulation of two model enzymes of cytochrome c(Cty C)and D-amino acid oxidase(DAAO)with opposite pH-activity profiles using ionic polymers.The operational pH of poly(acrylic acid)modified Cyt C and polyallylamine modified DAAO was extended to 3-7 and 2-10 where the enzyme activity was larger than that at their optimum pH of 4.5 and 8.5 by 106%and 28%,respectively.The cascade reaction catalyzed by two modified enzymes reveals a 1.37-fold enhancement in catalytic efficiency compared with their native counterparts.The enzyme activity boosting is understood by performing the UV-vis/CD spectroscopy and molecular dynamics simulations in the atomistic level.The increased activity is ascribed to the favorable microenvironment in support of preserving enzyme native structures nearby cofactor under external perturbations.展开更多
A continuous-wave(CW)π-polarized 1084 nm laser based on Nd:MgO:LiNbO_(3)under 888 nm thermally boosted pumping is reported.According to the absorption spectrum and energy level structure of Nd:MgO:LiNbO_(3),the 888 n...A continuous-wave(CW)π-polarized 1084 nm laser based on Nd:MgO:LiNbO_(3)under 888 nm thermally boosted pumping is reported.According to the absorption spectrum and energy level structure of Nd:MgO:LiNbO_(3),the 888 nm laser diode(LD)is used for thermally boosted pumping.This pumping method eliminates the quantum defect caused by the nonradiative transition in Nd:MgO:LiNbO_(3)under the traditional 813 nm pumping and effectively improves the serious thermal effect of the crystal.The unmatched polarized 1093 nm laser is completely suppressed,and theπ-polarized laser output of1084 nm in the whole pump range is realized by the 888 nm thermally boosted pumping.In the present work,we achieved the CWπ-polarized 1084 nm laser with a maximum output power of 7.53 W and a slope efficiency of about 46.1%.展开更多
A new 11 T SRAM cell with write-assist is proposed to improve operation at low supply voltage. In this technique, a negative bit-line voltage is applied to one of the write bit-lines, while a boosted voltage is applie...A new 11 T SRAM cell with write-assist is proposed to improve operation at low supply voltage. In this technique, a negative bit-line voltage is applied to one of the write bit-lines, while a boosted voltage is applied to the other write bit-line where transmission gate access is used in proposed 11 T cell. Supply voltage to one of the inverters is interrupted to weaken the feedback. Improved write feature is attributed to strengthened write access devices and weakened feedback loop of cell at the same time. Amount of boosting required for write performance improvement is also reduced due to feedback weakening, solving the persistent problem of half-selected cells and reliability reduction of access devices with the other suggested boosted and negative bit-line techniques. The proposed design improves write time by 79%, 63% and slower by 52% with respect to LP 10 T, WRE 8 T and 6 T cells respectively. It is found that write margin for the proposed cell is improved by about 4×, 2.4× and 5.37× compared to WRE8 T, LP10 T and 6 T respectively. The proposed cell with boosted negative bit line（BNBL） provides47%, 31%, and 68.4% improvement in write margin with respect to no write-assist, negative bit line（NBL） and boosted bit line（BBL） write-assist respectively. Also, new sensing circuit with replica bit-line is proposed to give a more precise timing of applying boosted voltages for improved results. All simulations are done on TSMC 45 nm CMOS technology.展开更多
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah Bint Abdulrahman University,through the Program of Research Project Funding after Publication,grant No.(RPFAP-79-1445).
文摘Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles,surface radiation balance,weather and climate changes,and vegetation photosynthesis.Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry.This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period.Solar energy is the cheapest form of clean energy,and due to the intermittent nature of the energy,accurate forecasting across multiple timeframes is necessary for efficient generation and demand management.Solar radiation prediction using deep learning(DL)includes the applications of neural network methods,namely Convolutional Neural Network(CNN)or Long Short-Term Memory(LSTM)models,to forecast and model solar irradiance patterns.By leveraging meteorological variables and historical solar radiation data,DL algorithms can capture complex spatial and temporal dependencies,resulting in accurate predictions.This article presents a novel Solar Radiation Prediction model utilizing a Boosted Coyote Optimization Algorithm with Deep Learning(SRP-BCOADL).The SRP-BCOADL model initially normalizes the input data using a min-max normalization approach to improve the robust nature under different scales.Besides,the SRP-BCOADL technique uses a Deep Long Short-Term Memory Autoencoder(DLSTM-AE)system for precisely forecasting solar radiation levels.The model’s accuracy is further improved through hyperparameter optimization using the BCOA.The performance analysis of the SRP-BCOADL technique is tested using solar radiation data.Extensive experimental outcomes prove that the SRP-BCOADL method obtains better results over other techniques.The Mean Squared Error(MSE)is just 0.13 kWh/m^(2),is much lower when compared to other models.The Root Mean Squared Error(RMSE)is also reduced to 0.36 kWh/m^(2),and the Mean Absolute Error(MAE)reaches a minimal level of 0.276 kWh/m^(2).
基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A5A8033165)the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and was granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20214000000200).
文摘Recently,machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductormanufacturing.The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features.This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns.First,the number of defects during the actual process may be limited.Therefore,insufficient data are generated using convolutional auto-encoder(CAE),and the expanded data are verified using the evaluation technique of structural similarity index measure(SSIM).After extracting handcrafted features,a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction.Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns,the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.
基金Supported by the National Science Foundation of China under Grant No.11405095
文摘We present a study about the flavor changing coupling of the top quark with the Higgs boson through the channe■at LHC.The final states considered for the such process are■.We focus on the boosted region in the phase space of the Higgs boson.The backgrounds and events are simulated and analyzed.The sensitivities for the FCNH couplings are estimated.It is found that it is more sensitive for ytu than ytq at LHC.The upper limits of the FCNH couplings can be set at LHC with 3000 fb-1integrated luminosity as■95%C.L.
文摘Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications.
文摘This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering.
文摘This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.
基金Deanship of Scientific Research at Umm Al-Qura University,Grant Code,funds this research:22UQU4281768DSR08。
文摘Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the network.The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes.The route identification and routing are established based on the data broadcast by the network nodes.In transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network.Furthermore,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate.In this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery rate.Initially,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing routing.The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET.Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation.Next,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency.The proposed GBEOM exhibits∼4%improved performance in MANET routing.
文摘Dry streams filled with sand,and sun-baked soil and drought resistant mopane trees characterize vast expanse of land in the rural Chiredzi District,more than 600 km southeast of Zimbabwe’s capital Harare.Topless and barefooted children make a beeline waving at modern non-governmental organization vehicles which frequent the district.
基金supported by National Natural Science Foundation of China(U24B6010).
文摘Anomaly detection is crucial for data-driven applications in integrated energy systems.Traditional anomaly detection methods primarily focus on one single energy load,often neglecting potential spatial correlations between multivariate energy time series.Meanwhile,addressing the imbalanced nature of user-level multi-en-ergy load data remains a significant challenge.In this paper,we propose EGBAD,an Ensemble Graph-Boosted Anomaly Detection framework for user-level multi-energy load that leverages the advantages of graph relational analysis and ensemble learning.First,a dynamic graph construction method based on multidimensional scaling(MDS)is proposed to transform multi-energy load data into graph representations.These graphs are subse-quently processed using graph convolutional network(GCN)to capture the spatiotemporal correlations between multi-energy load time series.In addition,to improve detection robustness under class imbalance,the entire training process is embedded within a Boosting ensemble learning framework,where the weight assigned to the minority class is progressively increased at each boosting stage.Experimental results on publicly real-world datasets demonstrate that the proposed model achieves superior anomaly detection accuracy compared to most baseline methods.Notably,it performs especially well in scenarios characterized by extreme data imbal-ance,achieving the highest recall and F1-score for anomaly detection.
文摘Correction to:Signal Transduction and Targeted Therapy https://doi.org/10.1038/s41392-021-00474-x,published online 29 January 2021 In the process of collating the raw data,the authors noticed an inadvertent mistakes occurred in Supplementary Fig.11c that need to be corrected after online publication of the article.1 Due to our negligence in extracting and processing a large amount of experimental data,duplicate images were inadvertently used for the MPTP group and the MPTP+lovastatin group of mice with dopaminergic neurons deficient in SHP2(SHP2TH-/-).The correct data are provided as follows.The keyfindings of the article are not affected by these corrections.
文摘精确评估电池的荷电状态(state of charge,SOC)是实现高效储能电池管理的前提。当前利用阻抗估计电池SOC多基于非原位电化学阻抗谱,由于此技术要求充分的静置时间,使得通过阻抗动态估计电池SOC困难。考虑到储能电站的实际运行情况,实时快速地获取阻抗数据成为关键。然而工况下受到直流偏置影响,电池电压的非线性变化会导致中低频阻抗产生偏移,影响阻抗测量准确性。针对以上问题,该研究采用离散间隔二进制序列设计了一种动态电化学阻抗谱测量方法,结合电池充放电工况下的阻抗特性,引入Categorical Boosting算法构建了电池SOC估计模型。针对4块商用18650锂电池,在不同温度和充放电倍率下每隔1%SOC重复测量了电池在充放电过程中的动态阻抗。实验结果表明,在不同实验条件下,电池充放电工况下SOC估计的最大平均绝对误差和均方根误差分别为2.98%和3.59%,证明了所提方法的可靠性和鲁棒性。
基金financial supports from the National Natural Science Foundation of China(31961133004,21977013,21903045)the National Key R&D Program of China(2018YFA0902200)+1 种基金China Post-doctoral Science Foundation(2019M661842)the Fundamental Research Funds for the Cornell University(PT1917,buctrc201,30920021122)。
文摘Engineering of enzyme microenvironment can surprisingly boost the apparent activity.However,the underlying regulation mechanism is not well-studied at a molecular level so far.Here,we present a modulation of two model enzymes of cytochrome c(Cty C)and D-amino acid oxidase(DAAO)with opposite pH-activity profiles using ionic polymers.The operational pH of poly(acrylic acid)modified Cyt C and polyallylamine modified DAAO was extended to 3-7 and 2-10 where the enzyme activity was larger than that at their optimum pH of 4.5 and 8.5 by 106%and 28%,respectively.The cascade reaction catalyzed by two modified enzymes reveals a 1.37-fold enhancement in catalytic efficiency compared with their native counterparts.The enzyme activity boosting is understood by performing the UV-vis/CD spectroscopy and molecular dynamics simulations in the atomistic level.The increased activity is ascribed to the favorable microenvironment in support of preserving enzyme native structures nearby cofactor under external perturbations.
基金supported by the National Natural Science Foundation of China(Nos.U20A20214 and 11974060)the Natural Science Foundation of Jilin Province (No.20210101154JC)。
文摘A continuous-wave(CW)π-polarized 1084 nm laser based on Nd:MgO:LiNbO_(3)under 888 nm thermally boosted pumping is reported.According to the absorption spectrum and energy level structure of Nd:MgO:LiNbO_(3),the 888 nm laser diode(LD)is used for thermally boosted pumping.This pumping method eliminates the quantum defect caused by the nonradiative transition in Nd:MgO:LiNbO_(3)under the traditional 813 nm pumping and effectively improves the serious thermal effect of the crystal.The unmatched polarized 1093 nm laser is completely suppressed,and theπ-polarized laser output of1084 nm in the whole pump range is realized by the 888 nm thermally boosted pumping.In the present work,we achieved the CWπ-polarized 1084 nm laser with a maximum output power of 7.53 W and a slope efficiency of about 46.1%.
文摘A new 11 T SRAM cell with write-assist is proposed to improve operation at low supply voltage. In this technique, a negative bit-line voltage is applied to one of the write bit-lines, while a boosted voltage is applied to the other write bit-line where transmission gate access is used in proposed 11 T cell. Supply voltage to one of the inverters is interrupted to weaken the feedback. Improved write feature is attributed to strengthened write access devices and weakened feedback loop of cell at the same time. Amount of boosting required for write performance improvement is also reduced due to feedback weakening, solving the persistent problem of half-selected cells and reliability reduction of access devices with the other suggested boosted and negative bit-line techniques. The proposed design improves write time by 79%, 63% and slower by 52% with respect to LP 10 T, WRE 8 T and 6 T cells respectively. It is found that write margin for the proposed cell is improved by about 4×, 2.4× and 5.37× compared to WRE8 T, LP10 T and 6 T respectively. The proposed cell with boosted negative bit line（BNBL） provides47%, 31%, and 68.4% improvement in write margin with respect to no write-assist, negative bit line（NBL） and boosted bit line（BBL） write-assist respectively. Also, new sensing circuit with replica bit-line is proposed to give a more precise timing of applying boosted voltages for improved results. All simulations are done on TSMC 45 nm CMOS technology.
