The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable deve...The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.展开更多
With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learnin...With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.展开更多
Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the cl...Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.展开更多
Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these...Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.展开更多
Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation to...Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.展开更多
An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating s...An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.展开更多
Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item ...Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.展开更多
This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction...This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.展开更多
We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by u...We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.展开更多
In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integrati...In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration ofIoT and Artificial Intelligence (AI). The amalgamation of above mentioned toolshas taken the new peak in terms of diagnosis and treatment process especially inthe pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paperproposes a novel optimized cognitive learning based BAN model based on FogIoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extractedand used to train the proposed fog based learning algorithm to achieve low energyconsumption and low-latency scheduling algorithm. To test the proposed network,Fog-IoT-BAN test bed has been developed with the battery driven MICOTTboards interfaced with the health care sensors using Micro Python programming.The extensive experimentation is carried out using the above test beds and variousparameters such as accuracy, precision, recall, F1score and specificity has beencalculated along with QoS (quality of service) parameters such as latency, energyand throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with theother state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in termsof accuracy and high False Alarm Rate (FAR), energy efficiency and latency.展开更多
The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consump...The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.展开更多
With the increasing focus on sustainable development goals,the critical role of reverse logistics in supply chains is becoming more evident.Reverse logistics not only enables resource recovery and reuse but also reduc...With the increasing focus on sustainable development goals,the critical role of reverse logistics in supply chains is becoming more evident.Reverse logistics not only enables resource recovery and reuse but also reduces environmental pollution and enhances economic efficiency.However,existing models face significant challenges related to recovery efficiency,cost control,and supply chain coordination.To address these challenges,this study proposes strategies to improve recovery and reuse efficiency,optimize logistics processes,enhance information sharing and collaboration,and encourage active participation from both businesses and consumers.These measures aim to improve the overall efficiency of reverse logistics and support the achievement of sustainable development goals.展开更多
According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are establishe...According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variati...Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy.Traditional diagnostic methods often rely on subjective visual assessments,which can lead to misdiagnosis.This study addresses these challenges by developing HybridFusionNet,a novel model that integrates Convolutional Neural Networks(CNN)with 1D feature extraction techniques to enhance diagnostic accuracy.Utilizing two extensive datasets,BCN20000 and HAM10000,the methodology includes data preprocessing,application of Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors(SMOTEENN)for data balancing,and optimization of feature selection using the Tree-based Pipeline Optimization Tool(TPOT).The results demonstrate significant performance improvements over traditional CNN models,achieving an accuracy of 0.9693 on the BCN20000 dataset and 0.9909 on the HAM10000 dataset.The HybridFusionNet model not only outperforms conventionalmethods but also effectively addresses class imbalance.To enhance transparency,it integrates post-hoc explanation techniques such as LIME,which highlight the features influencing predictions.These findings highlight the potential of HybridFusionNet to support real-world applications,including physician-assist systems,teledermatology,and large-scale skin cancer screening programs.By improving diagnostic efficiency and enabling access to expert-level analysis,the modelmay enhance patient outcomes and foster greater trust in artificial intelligence(AI)-assisted clinical decision-making.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ...Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.展开更多
Electric vehicle is a kind of new energy vehicle which uses batteries as energy supply unit.A huge gap in charging infrastructures will be created by the expansion of electric vehicles.The effectiveness and rationalit...Electric vehicle is a kind of new energy vehicle which uses batteries as energy supply unit.A huge gap in charging infrastructures will be created by the expansion of electric vehicles.The effectiveness and rationality of charging facilities will directly affect the convenience and economy of the users,as well as the safe operation of the power grid.Three types of charging facilities:charging pile,charging station and battery swap station are introduced in this paper.According to the different methods of charging infrastructure planning,the research status of the method of determining charging demand points is expounded.And the spatial distribution of charging demand points extracted by the current site selection method has a certain deviation.Then the models and algorithms of charging infrastructure optimized layout are reviewed.Currently,many researches focus on three categories optimization objectives:benefit of power company side,investment cost of charging facility and user side cost,and the genetic algorithm and particle swarm optimization are the main solving algorithms.Finally,the relative methods and development trend of the charging infrastructures optimized layout are summarized,and some suggestions on the optimized layout of electric vehicle charging infrastructures are given forward.展开更多
Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
基金supported by the National Natural Science Foundation of China (Grant No.52174065)the National Natural Science Foundation of China (Grant No.52304071)+1 种基金China University of Petroleum,Beijing (Grant No.ZX20220040)MOE Key Laboratory of Petroleum Engineering (China University of Petroleum,No.2462024PTJS002)。
文摘The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.
文摘With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.
文摘Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.
基金This work was supported by the National Nature Science Foundation of China(No.61503423,H.P.Jiang).The URL is http://www.nsfc.gov.cn/.
文摘Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.
基金The authors received funding for this study from the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFP2021-033).
文摘Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.
基金supported by the National Natural Science Foundation of China (Nos. 51178018 and 71031001)
文摘An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.
基金The Key Project of the National Ninth-Five-Year Plan No. 96-004-02-09The 48Project of Ministry of Water Resources No. 985106The Project of Chinese Academy of Sciences
文摘Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.
文摘This paper demonstrates empirical research on using convolutional neural networks(CNN)of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction.Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge.In this study,CNN architectures such as VGG-16,VGG-19,RestNet50,RestNet18 are compared,and an optimized model for feature extraction in X-ray images from various domains invol-ving several classes is proposed.An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19(Negative or Positive).Then,2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models.Among those,the optimized model architecture classifier technique achieves higher accuracy(0.97)than four other models,specifically VGG-16,VGG-19,RestNet18,and RestNet50(0.96,0.72,0.91,and 0.93,respectively).Therefore,this study will enable radiol-ogists to more efficiently and effectively classify a patient’s coronavirus disease.
文摘We put forward a chaotic estimating model, by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system, and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means. Key words chaotic time series - parameter identification - optimal prediction model - improved change ruler method CLC number TP 273 Foundation item: Supported by the National Natural Science Foundation of China (60373062)Biography: JIANG Wei-jin (1964-), male, Professor, research direction: intelligent compute and the theory methods of distributed data processing in complex system, and the theory of software.
文摘In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration ofIoT and Artificial Intelligence (AI). The amalgamation of above mentioned toolshas taken the new peak in terms of diagnosis and treatment process especially inthe pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paperproposes a novel optimized cognitive learning based BAN model based on FogIoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extractedand used to train the proposed fog based learning algorithm to achieve low energyconsumption and low-latency scheduling algorithm. To test the proposed network,Fog-IoT-BAN test bed has been developed with the battery driven MICOTTboards interfaced with the health care sensors using Micro Python programming.The extensive experimentation is carried out using the above test beds and variousparameters such as accuracy, precision, recall, F1score and specificity has beencalculated along with QoS (quality of service) parameters such as latency, energyand throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with theother state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in termsof accuracy and high False Alarm Rate (FAR), energy efficiency and latency.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375264 and 62376212).
文摘The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
文摘With the increasing focus on sustainable development goals,the critical role of reverse logistics in supply chains is becoming more evident.Reverse logistics not only enables resource recovery and reuse but also reduces environmental pollution and enhances economic efficiency.However,existing models face significant challenges related to recovery efficiency,cost control,and supply chain coordination.To address these challenges,this study proposes strategies to improve recovery and reuse efficiency,optimize logistics processes,enhance information sharing and collaboration,and encourage active participation from both businesses and consumers.These measures aim to improve the overall efficiency of reverse logistics and support the achievement of sustainable development goals.
文摘According to the principle of minimizing total cost, the three-echelon optimized medical inventory model with stochastic lead-time and two-echelon optimized medicine inventory model with fixed lead-time are established. The relationship between lead-time and inventory cost is studied by Matlab software. It shows that the variety of lead-time has an important effect on medicine inventory systems. Numerical simulation and sensitivity analysis of two models are presented by Lingo software. Based on analysis, it is concluded that the two-echelon model with lead-time results in inventory cost savings, and keeps the quality of care as reflected in service levels when compared with the three-echelon network structure.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
文摘Skin cancer is among the most common malignancies worldwide,but its mortality burden is largely driven by aggressive subtypes such as melanoma,with outcomes varying across regions and healthcare settings.These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy.Traditional diagnostic methods often rely on subjective visual assessments,which can lead to misdiagnosis.This study addresses these challenges by developing HybridFusionNet,a novel model that integrates Convolutional Neural Networks(CNN)with 1D feature extraction techniques to enhance diagnostic accuracy.Utilizing two extensive datasets,BCN20000 and HAM10000,the methodology includes data preprocessing,application of Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors(SMOTEENN)for data balancing,and optimization of feature selection using the Tree-based Pipeline Optimization Tool(TPOT).The results demonstrate significant performance improvements over traditional CNN models,achieving an accuracy of 0.9693 on the BCN20000 dataset and 0.9909 on the HAM10000 dataset.The HybridFusionNet model not only outperforms conventionalmethods but also effectively addresses class imbalance.To enhance transparency,it integrates post-hoc explanation techniques such as LIME,which highlight the features influencing predictions.These findings highlight the potential of HybridFusionNet to support real-world applications,including physician-assist systems,teledermatology,and large-scale skin cancer screening programs.By improving diagnostic efficiency and enabling access to expert-level analysis,the modelmay enhance patient outcomes and foster greater trust in artificial intelligence(AI)-assisted clinical decision-making.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number:IMSIU-DDRSP2503)。
文摘Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems.
基金Project(21805217)supported by the National Natural Science Foundation of ChinaProject(2015BAG08B02)supported by the National Key Technologies Research and Development Program of ChinaProject(2019IVB014)supported by the Fundamental Research Funds for the Central Universities,China。
文摘Electric vehicle is a kind of new energy vehicle which uses batteries as energy supply unit.A huge gap in charging infrastructures will be created by the expansion of electric vehicles.The effectiveness and rationality of charging facilities will directly affect the convenience and economy of the users,as well as the safe operation of the power grid.Three types of charging facilities:charging pile,charging station and battery swap station are introduced in this paper.According to the different methods of charging infrastructure planning,the research status of the method of determining charging demand points is expounded.And the spatial distribution of charging demand points extracted by the current site selection method has a certain deviation.Then the models and algorithms of charging infrastructure optimized layout are reviewed.Currently,many researches focus on three categories optimization objectives:benefit of power company side,investment cost of charging facility and user side cost,and the genetic algorithm and particle swarm optimization are the main solving algorithms.Finally,the relative methods and development trend of the charging infrastructures optimized layout are summarized,and some suggestions on the optimized layout of electric vehicle charging infrastructures are given forward.
文摘Based on the optimization method, a new modified GM (1,1) model is presented, which is characterized by more accuracy prediction for the grey modeling.
