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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
1
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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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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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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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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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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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)
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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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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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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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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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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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 Novel Binary Classification Neural Network Optimized by the Mosquito Mating Swarm Optimization Algorithm for Predicting Microgrid Operational Modes 认领 引用
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作者 Jesús Aguila-León Carlos Vargas-Salgado +1 位作者 Dácil Díaz-Bello Fabián Lara-Vargas 《Energy Engineering》 EI 2026年第7期150-172,共23页
Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected ... Integrating renewable energy sources presents technical challenges due to their variable nature,particularly in predicting and managing microgrid operational modes.Accurate identification of grid statesinterconnected or islanded—is essential for maintaining stability and optimizing performance under fluctuating environmental conditions to meet energy demand.This work proposes a bio-inspired,optimized binary classification model based on Multi-Layer Perceptron Artificial Neural Networks(MLP-ANN),with the architecture and hyperparameters tuned using the novel Mosquito Mating Swarm Optimization(MMSO)algorithm,inspired by mosquito mating behavior and swarm dynamics.The model employs an MLP-ANN with a variable number of hidden layers and neurons per layer,configured to maximize classification accuracy by dynamically adjusting parameters,including the learning rate and regularization coefficients.Training utilizes k-fold cross-validation on experimental microgrid data.The MMSO approach is benchmarked against Particle Swarm Optimization(PSO),Genetic Algorithm(GA),and Grey Wolf Optimizer(GWO)to validate its effectiveness.Results show that the MMSO-optimized MLP-ANN achieved an 86.34%recall,98.96%precision,and 92.29%accuracy,while minimizing the Mean Squared Error to 0.0206.The MMSO-optimized MLP-ANN model achieved competitive classification performance compared to the other algorithms evaluated;although no statistically significant differences in recall were observed among the optimizers(p=0.22),the MMSO achieved the lowest MSE(0.0206).The MMSO was the only algorithm capable of discovering a four-layer architecture hidden within the same search space,evidencing superior exploration of deeper architectural regions of the solution space.These findings demonstrate the model's capacity to predict microgrid operational modes under variable conditions,highlighting the potential of integrating bio-inspired algorithms with neural networks for energy management systems.This approach could enhance the efficiency and reliability of integrating renewable energy sources into dynamic energy systems. 展开更多
关键词 Microgrids binary classification deep learning artificial neural network mosquito mating swarm optimizer operational mode prediction
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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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Observation on the clinical efficacy of comprehensive optimized nursing intervention in patients undergoing laparoscopic cholecystectomy 认领 引用
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作者 LIU Xuejun 《外文科技期刊数据库（文摘版）医药卫生》 2026年第4期022-026,共5页
Objective: To investigate the clinical efficacy of comprehensive optimized nursing care in patients undergoing laparoscopic cholecystectomy. Methods: A total of 60 patients who underwent laparoscopic cholecystectomy a... Objective: To investigate the clinical efficacy of comprehensive optimized nursing care in patients undergoing laparoscopic cholecystectomy. Methods: A total of 60 patients who underwent laparoscopic cholecystectomy at our hospital from January 2024 to January 2025 were enrolled and divided into a control group (standard nursing care) and an observation group (comprehensive nursing care) each comprising 30 cases. The observation group received comprehensive optimized nursing care, while the control group received conventional nursing care. Results: The postoperative recovery outcomes in the observation group were superior to those in the control group. Conclusion: Comprehensive optimized nursing care can effectively alleviate surgical stress responses in patients undergoing laparoscopic cholecystectomy, accelerate recovery, reduce medical costs, and holds significant clinical value for widespread application. 展开更多
关键词 laparoscopy cholecystectomy comprehensive care optimized intervention stress response
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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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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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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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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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A Novel Evolutionary Optimized Transformer-Deep Reinforcement Learning Framework for False Data Injection Detection in Industry 4.0 Smart Water Infrastructures 认领 引用
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作者 Ahmad Salehiyan Nuria Serrano +2 位作者 Francisco Hernando-Gallego Diego Martín José Vicenteálvarez-Bravo 《Computers, Materials & Continua》 SCIE EI 2026年第5期1588-1624,共37页
The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource manag... The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection(FDI)attacks,posing critical threats to operational integrity,resource management,and public safety.Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated,stealthy threats.To address these challenges,we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework(Evo-Transformer-DRL)designed for robust and adaptive FDI detection in smart water infrastructures.The proposed architecture integrates three powerful paradigms:a transformer encoder for modeling complex temporal dependencies in multivariate time series,a DRL agent for learning optimal decision policies in dynamic environments,and an evolutionary optimizer to fine-tune model hyper-parameters.This synergy enhances detection performance while maintaining adaptability across varying data distributions.Specifically,hyper-parameters of both the transformer and DRL modules are optimized using an improved grey wolf optimizer(IGWO),ensuring a balanced trade-off between detection accuracy and computational efficiency.The model is trained and evaluated on three realistic Industry 4.0 water datasets:secure water treatment(SWaT),water distribution(WADI),and battle of the attack detection algorithms(BATADAL),which capture diverse attack scenarios in smart treatment and distribution systems.Comparative analysis against state-of-the-art baselines including Transformer,DRL,bidirectional encoder representations from transformers(BERT),convolutional neural network(CNN),long short-term memory(LSTM),and support vector machines(SVM)demonstrates that our proposed Evo-Transformer-DRL framework consistently outperforms others in key metrics such as accuracy,recall,area under the curve(AUC),and execution time.Notably,it achieves a maximum detection accuracy of 99.19%,highlighting its strong generalization capability across different testbeds.These results confirm the suitability of our hybrid framework for real-world Industry 4.0 deployment,where rapid adaptation,scalability,and reliability are paramount for securing critical infrastructure systems. 展开更多
关键词 Industry 4.0 smart water systems false data injection detection cyber-physical security transformer deep reinforcement learning grey wolf optimizer
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