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Enhancing corrosion resistance of heat-assisted forming titanium bipolar plate for proton exchange membrane fuel cell through optimized heat treatment 认领 引用
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作者 Yu Leng Junying Min +5 位作者 Daijun Yang Xinyu Lv Jian Yang Junfeng Qian Pingwen Ming Cunman Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期49-59,共11页
For heat-assisted titanium bipolar plate(BPP)with coating directly applied in the surface of retained oxide film,heating temperature and holding time are critical parameters having great influence on oxide film format... For heat-assisted titanium bipolar plate(BPP)with coating directly applied in the surface of retained oxide film,heating temperature and holding time are critical parameters having great influence on oxide film formation,as well as corrosion resistance and durability of the titanium BPP substrate in proton exchange membrane fuel cell(PEMFC).However,the effect of heating temperature and holding time on corrosion resistance of titanium BPP substrate in PEMFC working condition remains unclear.This work aims to reveal the effects as well as to obtain the optimized parameters of heat treatment process for enhanced corrosion resistance of heat-assisted forming titanium BPP substrate.First,corrosion behaviour of ultra-thin titanium specimens with heating temperature and holding time within the range of 500-600℃ and 1-30 min,respectively,were investigated by potentiodynamic test,potentiostatic test,simulated PEMFC working condition(WC)test and electrochemical impedance spectroscopy(EIS)test.In addition,surface morphology,roughness,chemical compositions and phases in the surface of the titanium specimens were studied by confocal scanning laser microscopy(CSLM),scanning electron microscopy(SEM),energy dispersive X-ray spectroscopy(EDS)and X-ray diffraction(XRD).The results indicate that corrosion resistance of titanium bipolar plate substrate can be improved significantly as compared to the original specimen when titanium specimens is heated at 600℃ for 10 min.The self-corrosion current density and passive current density in simulated PEMFC working environment will be reduced to 0.070-0.085μA·cm-2 and 0.509-0.558μA·cm-2,respectively.In addition,corrosion current densities at potentials in the range of 0.6-1.4 V(vs.Ag/AgCl)are reduced to0.011-0.040μA·cm-2.The enhanced corrosion resistance is mainly attributed to a better compactness of oxide film density,as well as higher corrosion resistance of the oxide film formed in the surface. 展开更多
关键词 Corrosion Oxidation Heat treatment Optimization Titanium bipolar plate Proton exchange membrane fuel cell
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Artificial intelligence-optimized gypsum-based composites for stealth technology:Integrating high strength performance and radar absorption 认领 引用
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作者 Sadik Alper Yildizel Abdurrahim Toktas Gökhan Kaplan 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期19-35,共17页
The demand for lightweight,high-performance materials with electromagnetic shielding capability is increasing,particularly in aerospace,defense,and infrastructure applications.This study introduces a novel gypsum-base... The demand for lightweight,high-performance materials with electromagnetic shielding capability is increasing,particularly in aerospace,defense,and infrastructure applications.This study introduces a novel gypsum-based composite system enhanced with carbon black(CB),magnetite(Fe3O4),boron nitride(BN),and hybrid glass-basalt fibers to simultaneously improve mechanical strength and electromagnetic wave absorption.A limited experimental dataset of 13 formulations was extended to 260using physics-consistent data augmentation.Multi-objective optimization was then performed using the Artificial Bee Colony(ABC)algorithm to maximize compressive,flexural,and splitting tensile strengths while minimizing return loss in the 8-12 GHz(X-band)range.The optimized formulation(85%gypsum,15%resin,6.8%CB,18.5%Fe3O4,2%BN,1%glass fiber,0.5%basalt fiber)achieved 34.41 MPa compressive strength(+6.1%),7.71 MPa flexural strength(+5.3%),and 4.45 MPa splitting tensile strength(+5.7%).Simultaneously,the minimum return loss improved from-22.5 to-24.8 dB,resulting in an enhancement of electromagnetic absorption by 10.2%at 9.72 GHz.Validation tests confirmed the model's accuracy within a 1.35%margin of error.This hybrid methodology,which combines experimental science,Al-driven data expansion,and bio-inspired optimization,offers a scalable and costeffective route to multifunctional composite design.The findings are directly relevant for radarabsorbing structures,EMI shielding in electronics,and smart civil infrastructure. 展开更多
关键词 Multifunctional composites Machine learning optimization Gypsum Artificial bee colony algorithm
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Remaining useful life prediction for bearings based on Transformer-BiLSTM network optimized by the Newton-Raphson-based optimizer 认领 引用
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作者 Wen Juan Wu You +1 位作者 Song Yang Pan Baisong 《High Technology Letters》 EI CAS 2026年第2期109-120,共12页
The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approach... The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approaches have achieved great success for bearing prognosis,most of them are not capable of mining both global and local information from the run-to-failure data.In addition,hyperparameters such as the number of hidden layer neurons,learning rate,and regularization parameters in neural networks still rely heavily on manual experience for setting.To address these issues,a novel framework for predicting the RUL of bearings based on the Transformer and the bidirectional long short-term memory(Transformer-BiLSTM)is proposed,and the Newton-Raphsonbased optimizer(NRBO)is introduced to determine the crucial parameters of the network.Firstly,degradation sensitive features are extracted and selected from the raw vibration signals,forming the input for the prediction model.Secondly,the mean absolute error(MAE)between the predicted and actual values is utilized as the fitness function of the NRBO algorithm to optimize the Transformer-BiLSTM model,searching for the optimal values of the key hyperparameters.Finally,the optimized model is used for RUL prediction,and its performance is validated on publicly available datasets.The results demonstrate that the proposed method can achieve the optimal hyperparameter combination without relying on empirical guidance.Compared with the unoptimized model,the optimized prediction model reduces the MAE and root mean squared error(RMSE)by 6.50%and 9.91%,respectively. 展开更多
关键词 Transformer bidirectional long short-term memory Newton-Raphson-based optimizer remaining useful life prediction bearing
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Clinical efficacy of optimized drug treatment for acute type A aortic dissection:insights from a multicenter retrospective cohort study 认领 引用
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作者 Shi-Pan Wang Hai-Yang Li +6 位作者 Yin-Fan Zhu Hao-Tian Wu Dong-Jie Li Yuan-Fei Zhao Lu Dai Wen-Jian Jiang Hong-Jia Zhang 《Military Medical Research》 SCIE CAS CSCD 2026年第3期507-510,共4页
Dear Editor,Acute aortic dissection(AAD)refers to the tearing of the aortic intima,with high-pressure blood flowing into the media.It can be classified into Stanford type A and type B according to whether the ascendin... Dear Editor,Acute aortic dissection(AAD)refers to the tearing of the aortic intima,with high-pressure blood flowing into the media.It can be classified into Stanford type A and type B according to whether the ascending aorta is involved.Acute type A aortic dissection(ATAAD)is a life-threatening cardiovascular disease with high mortality rates(approximately 50%and 1%–2% per hour)within the first 48 h[1]. 展开更多
关键词 Multicenter study Optimized drug treatment(ODP) Acute type A aortic dissection(ATAAD) Clinical prognosis
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Quantitative Analysis Method of Lubricant Base Oil Composition Based on Combined Optimized Characteristic Wavenumber 认领 引用
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作者 XIA Yanqiu NAY MINAUNG +1 位作者 WANG Yuxing FENG Xin 《摩擦学学报（中英文）》 EI CAS CSCD 北大核心 2026年第2期283-297,共15页
For the quantitative analysis of lube oil base oil components,three oil components,mineral oil(KN4010),hydrocarbon-based synthetic oil(PAO40),and synthetic ester(PriEco 3000)were selected as quantitative analysis obje... For the quantitative analysis of lube oil base oil components,three oil components,mineral oil(KN4010),hydrocarbon-based synthetic oil(PAO40),and synthetic ester(PriEco 3000)were selected as quantitative analysis objects,and then the mid-infrared spectral data of lube oil base oil samples formulated in different ratios were collected.The synergy interval partial least squares-binary grey wolf optimization algorithm(SiPLS-BGWO)combination optimization method was used to screen the characteristic wavenumbers in the full range to eliminate redundant invalid information and reduce the search space dimension.By optimizing the selection of characteristic wavenumbers,the SiPLS-BGWO approach not only enhanced the prediction accuracy but also demonstrated its ability to address challenges associated with overlapping spectral features in complex mixtures.The test results showed that the combined optimization model’s error indexes were significantly improved for the content prediction of mineral oil,hydrocarbonbased synthetic oil,and polyol ester.The RMSE(root mean square error)was reduced by up to 60.58% compared to using all spectral wavenumbers,and the fit indexes’R2 values were higher than 99%.The significant reduction in RMSE underscored the method’s capability to identify and eliminate irrelevant or noisy spectral information,ensuring that the predictive model focused only on relevant features.In addition,the SiPLS-BGWO method had reduced the number of characteristic wavenumbers to less than 40,significantly reducing the operational burden and effectively improving the accuracy and applicability of the quantitative analysis model for multi-matter components.The ability to reduce the number of characteristic wavenumbers to below 40 demonstrated the algorithm’s efficiency in dimensionality reduction while retaining essential predictive information.The results affirmed that the SiPLS-BGWO model was a powerful tool for predictive modeling,providing a balance between accuracy and efficiency in the quantitative analysis of multicomponent systems.And a novel framework for bridging the gap between spectral data complexity and actionable chemical insights,setting a precedent for future developments in the field. 展开更多
关键词 lubricant base oil mid-infrared spectroscopy feature wavenumber screening interval band screening combinatorial optimization model
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Enhanced production of ferulic acid and p-coumaric acid through optimized autohydrolysis pretreatment coupled with a dualenzyme system of xylanase and feruloyl esterase for efficient corncob enzymatic hydrolysis 认领 引用
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作者 Jinghao Ma Xiaoyan Liu +8 位作者 Rana Abdul Basit Lin Xiao Peng Gao Wenqi Cui Ming Chen Zhilei Fu Jinlong Yan Yuchun Liu Guangsen Fan 《Food Innovation and Advances》 2026年第1期64-75,共12页
This study aims to enhance the utilization value of corncob by efficiently extracting ferulic acid(FA)and p-coumaric acid(pCA)through environmentally sustainable methods.The research explores a novel approach that com... This study aims to enhance the utilization value of corncob by efficiently extracting ferulic acid(FA)and p-coumaric acid(pCA)through environmentally sustainable methods.The research explores a novel approach that combines autohydrolysis pretreatment with the synergistic effects of xylanase SrXyn10AR and feruloyl esterase BpFaeT132C-D143C.By conducting comprehensive optimization through single-factor analysis and response surface methodology(RSM)experiments,the optimal processing conditions were established:corncob particles of 40-60 mesh size were subjected to autohydrolysis at 165℃for 34 min with a solid-to-liquid ratio of 1:90.8,followed by enzymatic hydrolysis at 43℃,90 rpm,and pH 5.5 for 2.5 h,utilizing BpFaeT132C-D143Cand SrXyn10AR enzymes at a concentration of 1.1 U/mL each.Under these refined conditions,the yield of FA and pCA soared to 63.42%,marking a 2.42-fold increase compared to pre-optimization levels.Furthermore,this process yielded xylooligosaccharides as a valuable co-product,with a yield of 303.31 mg/g.In conclusion,this study develops an efficient and environmentally friendly strategy for extracting phenolic acids from corncob.By leveraging autohydrolysis pretreatment combined with dual-enzyme hydrolysis technology,this approach can pave a new way for the value-added utilization of corncob resources. 展开更多
关键词 autohydrolysis pretreatment synergistic effects xylanase srxyn ar pretreatment environmentally sustainable methodsthe response surface methodology rsm experimentsthe comprehensive optimization autohydrolysis
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A Hybrid MZOA-PSO Optimized Cascaded PI(1+DD)-PI-PID Controller for Frequency Stability of Interconnected Power Systems with Renewable Energy and Electric Vehicles 认领 引用
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作者 AL-Wesabi Ibrahim Hassan M.Hussein Farh +3 位作者 Jiazhu Xu Mohamad A.Alawad Ahmed Alqurashi Abdullrahman A.Al-Shamma’a 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期650-697,共48页
Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable e... Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems. 展开更多
关键词 Load frequency control cascaded PI(1+DD)-PI-PID controller hybrid MZOA-PSO algorithm interconnected power systems performance optimization
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Study on the aided PC-ASCL decoding algorithm based on optimized parity check codes 认领 引用
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作者 Jianguo YUAN Deyi YANG +1 位作者 Ziliang ZHU Jinneng XIANG 《Optoelectronics Letters》 EI 2026年第2期87-91,共5页
Aiming at the poor performance of the parity check(PC) aided adaptive successive cancellation list(PC-ASCL) decoding algorithm because the PC code in the polar code can only verify odd errors, an optimized parity chec... Aiming at the poor performance of the parity check(PC) aided adaptive successive cancellation list(PC-ASCL) decoding algorithm because the PC code in the polar code can only verify odd errors, an optimized parity check(OPC) code which can verify all odd errors as well as the half even errors is proposed. The OPC code is used to improve the PC-ASCL decoding algorithm, thus an OPC aided ASCL(OPC-ASCL) decoding algorithm is proposed. In the coding stage, the algorithm divides the information sequence into multiple segments, and places an OPC code at the end of each segment to verify the current information sequence, and places a cyclic redundancy check code at the end of the entire information sequence to verify the entire information sequence. In the decoding stage, the algorithm uses the OPC-ASCL decoder to decode. Simulation results show that compared to the PC-ASCL decoding algorithm, the OPC-ASCL decoding algorithm can reduce the complexity and obtain the certain performance gain. 展开更多
关键词 pc ascl decoding pc code adaptive successive cancellation list pc ascl divides information sequence multip parity check polar code optimized parity check coding stage
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A Firefly Algorithm-Optimized CNN-BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities 认领 引用
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作者 Ishaani Priyadarshini 《Computers, Materials & Continua》 SCIE EI 2026年第3期1510-1535,共26页
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ... Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems. 展开更多
关键词 Firefly optimization algorithm(FO) marrow cell abnormalities bidirectional long short term memory(Bi-LSTM) temporal dependency modeling
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An Interpretable AI Framework for Predicting Groundwater Contamination under Atmospheric and Industrial Pollution Using Metaheuristic-Optimized Deep Learning 认领 引用
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作者 Md.Mottahir Alam Mohammed K.Al Mesfer +3 位作者 Haroonhaider Sidhwa Mohd Danish Asif Irshad Khan Tauheed Khan Mohd 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期750-783,共34页
Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially... Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios. 展开更多
关键词 Groundwater quality prediction interpretable artificial intelligence industrial and atmospheric pollution spatial-temporal-assisted Deep Belief Network Tiki-Taka Algorithm Addax Optimization Algorithm Whale Optimization Algorithm
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Data-driven and experimental synergy:Metaheuristicoptimized extreme gradient boosting modeling of self-compacting concrete compressive strength 认领 引用
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作者 Divi SAI VARDHAN Akhilendra SHARMA +1 位作者 Rahul BISWAS Akshay BURA 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第3期555-579,共25页
This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete(SCC).The predictive mode... This investigation develops a comprehensive framework that integrates machine learning modeling with experimental validation for predicting the compressive strength of self-compacting concrete(SCC).The predictive models were constructed using a consolidated data set from existing literature and validated through independent laboratory experiments on ternary blends incorporating fly ash and silica fume.Experimental results demonstrated that optimized combinations of 30%fly ash and 5%–7.5%silica fume produced superior fresh-state properties,including enhanced flowability and reduced segregation,while achieving substantial improvements in compressive,tensile,and flexural strengths across all curing ages.Advanced ensemble learning techniques were employed by coupling Extreme Gradient Boosting(XGBoost)with three metaheuristic optimization algorithms:Tuna Swarm Optimization(TSO),Coyote Optimization Algorithm,and Giant Trevally Optimization.The XGB-TSO model demonstrated superior predictive performance,achieving coefficient of determination R2=0.9690,root mean square error of 2.15 MPa,weighted mean absolute percentage error of 2.63%,and Nash-Sutcliffe efficiency of 0.9674.Model interpretability analysis using SHapley Additive exPlanations(SHAP)identified concrete age and cement content as the most influential parameters,providing transparent insights into strength development mechanisms.Taylor diagram analysis confirmed statistical robustness through high correlation coefficients and minimal centered root mean square error.A graphical user interface was developed to enable real-time strength prediction from standard mix parameters,facilitating practical implementation.Experimental validation using laboratory-produced SCC specimens demonstrated excellent agreement with model predictions,achieving validation R2=0.9698 and mean absolute error of 1.83 MPa.The strong correlation between predicted and measured values validates the framework’s reliability for engineering applications.This research advances concrete technology by providing a validated,interpretable,and deployable tool that combines data-driven modeling with experimental verification,enabling intelligent mix design and informed decision-making in sustainable concrete construction. 展开更多
关键词 SCC XGBoost metaheuristic optimization TSO SHAP analysis graphical user interface
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Vibration reduction of propulsion shaft system by optimized bearing parameters 认领 引用
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作者 LIU Jian-yu GENG Xiao-ming +2 位作者 LI Xin-bin LIU Jing XU Ya-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2808-2828,共21页
The vibrations of the propulsion shaft systems have a critical impact on the performance and noise control of underwater vehicles.Bearings are essential for the system's dynamic performance as the support componen... The vibrations of the propulsion shaft systems have a critical impact on the performance and noise control of underwater vehicles.Bearings are essential for the system's dynamic performance as the support components.Existing studies have primarily focused on the impacts of the individual bearing parameters on system vibrations,while the effects of bearing designation,clearance,tolerance,and bearing arrangement on the multi-bearing propulsion shaft system dynamics remain unclear.There is a lack of optimized design for multi-bearing parameters in propulsion shaft systems.A comprehensive dynamic model of the multi-bearing propulsion shaft system is developed in this study,which includes key components such as support bearings and the propeller.The effects of different bearings at different positions and bearing parameters on key vibration indicators(such as acceleration and displacement)are revealed through dynamic simulations.Based on the simulation results,a vibration optimization model for a multi-bearing propulsion shaft system is proposed,which can select effective bearing parameters.The optimal bearing parameters of the propulsion shaft system can be obtained through the optimization model.The findings not only provide quantitative criteria for low-vibration design of underwater propulsion systems,but also provide a theoretical reference for modeling and vibration control of complex multi-support rotating machinery. 展开更多
关键词 vibration reduction propulsion shaft systems bearing parameter optimization dynamic modeling
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An Optimized Multiclass Support Vector Machine for Recognizing EEG Signals based on a Lower-Limb Exoskeleton 认领 引用
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作者 Kamel K.Mohammed Heba M.Afify Aboul Ella Hassanien 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1605-1620,共16页
The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper... The lower-limb prosthesis is used to assist patients with dysfunction of motor dysfunction or aging through Brain-Machine Interface(BMI)based on Electroencephalography(EEG)signals to control cognitive tasks.This paper presents a remarkable model to improve the estimation of the EEG signal and further help improve the control performance for the lower-limb prosthesis,and then improve the rehabilitation.It is based on an optimized Multiclass Support Vector Machine(MSVM)using Snake Optimizer(SO)to get the best possible parameter tuning for classifying different cognitive tasks to control of lower-limb exoskeleton.A public EEG dataset for a lower-limb exoskeleton using Motor Imagery(MI)during the control of the prosthesis and attention to gait(Att)on two surfaces,including flat(Experience)and non-flat(Slopes),has been used as benchmark data sets for this work.The results of the proposed model revealed the superiority of this technique in accuracy,compared with two optimization methods,including Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By comparing the outcomes of SO-MSVM with state of the arts,it achieved an accuracy of more than 85%for MI and Att metric,demonstrating intriguing results for solving the rehabilitation challenge.The devised technique could help people with neurological conditions who have trouble using manual controls. 展开更多
关键词 Electroencephalography(EEG) Brain-Machine Interface(BMI) Multiclass Support Vector Machine(MSVM) Genetic Algorithm(GA) Particle Swarm Optimization(PSO) and Snake Optimization(SO)
A clustering-guided feature enhancement and Bayesian-optimized LSTM approach for LC temporal anomaly detection 认领 引用
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作者 Yating Lei He Zhang +2 位作者 Haibo Liang Yihui Han Jialing Zou 《Petroleum》 EI CAS CSCD 2026年第3期509-522,共14页
Lost circulation(LC),as one of the high-risk accidents in drilling,usually occurs when the wellbore pressure is greater than the formation pressure,causing a large amount of drilling fluid to seep into the formation a... Lost circulation(LC),as one of the high-risk accidents in drilling,usually occurs when the wellbore pressure is greater than the formation pressure,causing a large amount of drilling fluid to seep into the formation and resulting in a significant decrease in the flow rate at the wellhead.Currently,existing LC prediction studies mainly rely on logging parameters for time series prediction,ignoring the problem of the inherent imbalance in the ratio of positive and negative samples in the LC dataset and geological information.To address these issues,this study proposes a clustering-guided feature enhancement and Bayesian optimization Long Short-Term Memory(BO-LSTM)method for LC time series anomaly detection.This method,based on the mud logging and geological structural information of faults and lithology risks,leverages clustering algorithms to uncover the latent structural information within samples to construct enhanced features,which combine with the selected key features,forming a joint representation that is fed into the LSTM model.At the same time,the BO algorithm is introduced to adaptively optimize the LSTM hyperparameters,and finally outputs the LC occurrence's probability.The results show that the proposed method performs well in LC prediction,with an average false negative rate of 0.825%,an average false positive rate of 1.525%,and an average lead time of 4.825 min for effective early warnings,significantly improving the accuracy and timeliness of early LC identification. 展开更多
关键词 LC early warning Long short-term memory network Bayesian optimization Clustering Feature enhancement
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Fed-HOER: Federated Hybrid-Optimized Emotion Recognition Framework Using DBO-FLA Metaheuristic Optimization 认领 引用
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作者 Mohammed Shukur Alfaras Oguz Karan +1 位作者 Sefer Kurnaz Ayca Kurnaz Turkben 《Computers, Materials & Continua》 SCIE EI 2026年第8期1673-1699,共27页
Despite deep learning’s high precision in emotion identification,centralized training is associated with privacy and scalability concerns.The privacy-preserving federated learning model,Federated Hybrid-Optimized Emo... Despite deep learning’s high precision in emotion identification,centralized training is associated with privacy and scalability concerns.The privacy-preserving federated learning model,Federated Hybrid-Optimized Emotion Recognition(Fed-HOER),introduced in this paper is an auto-tuning hyperparameters optimizer based on a hybrid Dung Beetle Optimizer-Fick’s Law Algorithm(DBO-FLA)optimizer.The global and local searches are optimized at two levels,and validation loss is minimized by 22%–24%without sharing raw data.The experiments on Extended Cohn–Kanade(CK+),Japanese Female Facial Expressions(JAFFE),and Karolinska Directed Emotional Faces(KDEF)exhibit a high generalization rate with a mean accuracy of 98.14.The findings demonstrate that Fed-HOER is statistically significantly better than baseline configurations.The results show that the suggested framework offers a favorable trade-off between predictive accuracy and privacy protection,which is why it can be used in the healthcare,educational,and other emotion-related fields. 展开更多
关键词 Emotion recognition affective computing federated learning Dung Beetle Optimizer(DBO) Fick’s Law Algorithm(FLA) hybrid metaheuristic optimization privacy preservation convolutional neural networks(CNN)
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Study on Heat Transfer in Double-Layer Topologically Optimized Microchannels under Non-Uniform Heat Sources in 2.5D Chips 认领 引用
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作者 Yechao Qin Zezhong Hou +2 位作者 Xiaojun Dai Zhenqian Chen Bo Xu 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第3期21-42,共22页
To address the increasing thermal management demands of electronic devices,this study proposes a single-inlet,single-outlet counterflow double-layer microchannel structure optimized for non-uniform heat sources and co... To address the increasing thermal management demands of electronic devices,this study proposes a single-inlet,single-outlet counterflow double-layer microchannel structure optimized for non-uniform heat sources and conducts a numerical analysis.The research focuses on designing a double-layer microchannel structure through topology optimization under 2.5D chip non-uniform heat source conditions.Its heat dissipation performance is compared with traditional linear microchannels to analyze the advantages of the topology-optimized microchannel.In a simulated 2.5D heat source scenario comprising a 240 W logic chip and four 40 W memory chips,the average temperature at the bottom of the topologically optimized microchannel heat sink decreased by 2 to 4 K compared to the conventional design when the inlet Reynolds number ranged from 500 to 2300.Concurrently,the system pressure drop was significantly reduced by 32%to 42%.These results demonstrate that this innovative design offers significant advantages in enhancing cooling efficiency and reducing energy consumption.Furthermore,due to its unique counter-current heat transfer mechanism,the structure also exhibits markedly improved temperature uniformity.This study provides an efficient design solution for advanced electronic thermal management scenarios characterized by constrained design space and stringent requirements for thermal stress management and integration density. 展开更多
关键词 Heat exchange maximization liquid cooled heat sink objective function topology optimization
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Optimized fiber allocation for enhanced impact resistance in composites through damage mode suppression 认领 引用
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作者 Noha M.Hassan Zied Bahroun +2 位作者 Mahmoud I.Awad Rami As'ad El-Cheikh Amer Kaiss 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第1期316-329,共14页
Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may... Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may be affected by a different failure mode,the optimal fiber volume fraction to suppress damage initiation and evolution is different across the layers.This research examines how re-allocating the fibers layer-wise enhances the composites'impact resistance.In this study,constant stiffness panels with the same fiber volume fraction throughout the layers are compared to variable stiffness ones by varying volume fraction layer-wise.A method is established that utilizes numerical analysis coupled with optimization techniques to determine the optimal fiber volume fraction in both scenarios.Three different reinforcement fibers(Kevlar,carbon,and glass)embedded in epoxy resin were studied.Panels were manufactured and tested under various loading conditions to validate results.Kevlar reinforcement revealed the highest tensile toughness,followed by carbon and then glass fibers.Varying reinforcement volume fraction significantly influences failure modes.Higher fractions lead to matrix cracking and debonding,while lower fractions result in more fiber breakage.The optimal volume fraction for maximizing fiber breakage energy is around 45%,whereas it is about 90%for matrix cracking and debonding.A drop tower test was used to examine the composite structure's behavior under lowvelocity impact,confirming the superiority of Kevlar-reinforced composites with variable stiffness.Conversely,glass-reinforced composites with constant stiffness revealed the lowest performance with the highest deflection.Across all reinforcement materials,the variable stiffness structure consistently outperformed its constant stiffness counterpart. 展开更多
关键词 Sandwich panel Fiber reinforced plastic composites Finite element analysis Variable stiffness Impact resistance Regression analysis Process optimization
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Optimized Sustainable Hybridization Through Holistic Multi-Platform Simulation:Enhancing Dynamic Response in Solar-Wind-Battery Energy Systems 认领 引用
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作者 Riad Mollik Babu Md Shafiul Alam +3 位作者 Md.Hasibur Rahman Mohammad Ali Md.Alamgir Hossain Md.Arifuzzaman 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期593-634,共42页
The increasing penetration of solar photovoltaic(PV)systems into power grids poses challenges due to their inherent intermittency and variability,which can compromise grid stability and reliability.Hybridizing solar P... The increasing penetration of solar photovoltaic(PV)systems into power grids poses challenges due to their inherent intermittency and variability,which can compromise grid stability and reliability.Hybridizing solar PV with wind energy and battery energy storage system(BESS)offers a promising solution by leveraging resource complementarity and providing fast frequency response.This study presents a techno-economic and environmental assessment of a hybrid renewable energy system.Wind turbines and a BESS are integrated with the existing 7.5 MW Sirajganj Solar PV Power Plant in Bangladesh.The proposed hybrid configuration is evaluated using real-world operational data and site-specific environmental parameters in a multi-platform simulation framework.MATLAB Simulink and DIgSILENT PowerFactory assess dynamic control and steady state stability,and HOMER Pro and openLCA perform techno-economic optimization and environmental impact analysis.Six system configurations were analyzed to determine the most technically reliable and cost-effective solution.The selected configuration includes 7.5 MW of solar PV,6.5 MW of wind,and an 8 MWh BESS.The setup achieves a levelized cost of electricity(LCOE)of$0.l23/kWh,a net present cost(NPC)of$34.5 million,and a 7.4-year payback period.Dynamic simulations show grid compliant operation under disturbances.Frequency remains within(European Network of Transmission System Operators for Electricity)ENTSO-E limits,rate of change of frequency(RoCoF)is reduced relative to the standalone PV plant,and harmonic distortion is mitigated through filtering.PVsyst validates the energy output obtained from HOMER Pro and provides performance ratio and detailed system loss breakdowns for the PV system.A Monte Carlobased uncertainty analysis validates the robustness of the results.Life cycle assessment(LCA)shows the hybrid system's total global warming potential(GWP)was about 20 times lower than coal and 1l times lower than gas generation.The hybrid solution offers a sustainable model for enhancing renewable energy infrastructure in Bangladesh and similar resource-constrained regions. 展开更多
关键词 Hybrid solar-wind-BESS system grid stability and frequency regulation Monte Carlo simulation lifecycle emissions assessment renewable energy optimization
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Enhancing Bridge Vibration Control through Optimized Quasi-Zero-Stiffness Supports under Moving Mass 认领 引用
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作者 Hamed Saber Antonio Zippo +1 位作者 Farhad S.Samani Francesco Pellicano 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期586-615,共30页
Lightweight bridges are increasingly used in modern infrastructure due to their structural efficiency;however,their relatively low stiffness and damping lead to a high sensitivity to vibration excitation induced by mo... Lightweight bridges are increasingly used in modern infrastructure due to their structural efficiency;however,their relatively low stiffness and damping lead to a high sensitivity to vibration excitation induced by moving loads such as pedestrians and vehicles.Conventional vibration mitigation strategies are often insufficient to suppress low-frequency responses,which has caused the development of advanced nonlinear isolation mechanisms.This paper investigates the effectiveness of nonlinear quasi-zero stiffness supports(QZSS)in suppressing vertical vibrations of lightweight bridges.Such structures are highly susceptible to vibrations induced by moving loads because of low stiffness and dissipation,with consequent high amplification near the resonances.The bridge excitation is a moving mass,and its structure is modelled as an Euler-Bernoulli beam.The partial differential equation(PDE)is analyzed through the Bubnov-Galerkin approach after expanding the displacement field using a multimode eigenfunction series,the resulting ordinary differential equations are numerically solved using the Gauss-Kronrod algorithm.The optimal parameters of the QZSS are identified based on the criterion of maximum footbridge deflection.Comparative analyses demonstrate the superior performance of optimized nonlinear QZSS over conventional linear elastic supports.The results indicate that an optimally designed QZsS incorporated with a dashpot can reduce the maximum vibration amplitude by up to 67%under moving loads,demonstrating its effectiveness as a vibration mitigation strategy for lightweight bridges.Moreover,reductions of up to 9o%can be achieved across the remaining frequency range. 展开更多
关键词 Quasi-zero stiffness support viscous damper large span bridge vibrations numerical optimization moving load vibration reduction
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