Transcatheter arterial embolization(TAE)is the mainstay for treating advanced hepatocellular carcinoma(HCC),and the performance of the embolization material is crucial in TAE.With the development of medical imaging an...Transcatheter arterial embolization(TAE)is the mainstay for treating advanced hepatocellular carcinoma(HCC),and the performance of the embolization material is crucial in TAE.With the development of medical imaging and the birth of“X-ray-free”technologies,we designed a new dual-mode imaging material of dimethoxy tetraphenyl ethylene(DMTPE)via emulsification by mixing poly(N-isopropylacrylamide-co-acrylic acid)(PNA)with lipiodol and fluorocarbons,which was evaluated for temperature sensitivity,stability,and dual-mode visualization in vitro.Additionally,blood vessel casting embolization and renal artery imaging were assessed in healthy rabbits.In a rabbit model with a VX2 tumor,the effectiveness of TAE for treating HCC was examined,with an emphasis on evaluating long-term outcomes of embolization and its effects on tumor growth,necrosis,and proliferation through imaging techniques.In vitro experiments confirmed that the temperature-sensitive dual-oil-phase Pickering emulsion had good flow,stable contrast,and embolism when the oil-to-oil ratio and water-to-oil ratio were both 7:3(v/v)and stabilized with 8%PNA.Similarly,in vivo,arterial embolization confirmed the excellent properties of DMTPE prepared at the abovementioned ratios.It was observed that DMTPE not only has an antitumor effect but can also achieve dual imaging using X-rays and ultrasound,making it a promising excellent vascular embolization material for TAE in tumor treatment.展开更多
Selective detection of multiple analytes in a compact design with dual-modality and theranostic features presents great challenges. Herein, we wish to report a coumarin-thiazolidine masked D-penicillamine based dual-m...Selective detection of multiple analytes in a compact design with dual-modality and theranostic features presents great challenges. Herein, we wish to report a coumarin-thiazolidine masked D-penicillamine based dual-modality fluorescent probe COU-DPA-1 for selective detection, differentiation, and detoxification of multiple heavy metal ions(Ag^(+), Hg^(2+), Cu^(2+)). The probe shows divergent fluorescence(FL)/circular dichroism(CD) responses via divergent bond-cleavage cascade reactions(metal ion promoted C-S cleavage and hydrolysis at two distinctive cleavage sites): FL “turn-off” and CD “turn-on” for Ag+(no hydrolysis), FL “turn-on” and CD “turn-off” for Hg^(+)(imine hydrolysis), and FL “self-threshold ratiometric” and CD “turn-off” for excess Cu^(2+)(lactone and imine hydrolysis), providing the first example of a fluorescence/CD dual-modality probe for multiple species with complimentary responses. Moreover, the bond-cleavage cascade reactions also lead to the formation of D-penicillamine heavy metal ion complexes for potential detoxification treatments.展开更多
A biodegradable tumor targeting nano-probe based on poly（ε-caprolactone）-b-poly（ethylene glycol） block copolymer （PCL-b-PEG）micelle functionalized with a magnetic resonance imaging （MRI） contrast agent diethy...A biodegradable tumor targeting nano-probe based on poly（ε-caprolactone）-b-poly（ethylene glycol） block copolymer （PCL-b-PEG）micelle functionalized with a magnetic resonance imaging （MRI） contrast agent diethylenetriaminepentaacetic acid-gadolinium （DTPA-Gd＋） on the shell and a near-infrared （NIR） dye in the core for magnetic resonance and optical dual-modality imaging was prepared. The longitudinal relaxivity （rl） of the PCL-b-PEG- DTPA-Gd3＋ micelle was 13.4 （mmol/L）^-1s^-1, three folds of that of DTPA-Gd3＋, and higher than that of many polymeric contrast agents with similar structures. The in vivo optical imaging of a nude mouse bearing xenografied breast tumor showed that the dual-modality micelle preferentially accumulated in the tumor via the folic acid-mediated active targeting and the passive accumulation by the enhanced permeability and retention （EPR） effect. The results indicated that the dualmodality micelle is a promising nano-probe for cancer detection and diagnosis.展开更多
BACKGROUND Bile duct stones(BDSs)may cause patients to develop liver cirrhosis or even liver cancer.Currently,the success rate of surgical treatment for intrahepatic and extrahepatic BDSs is not satisfactory,and there...BACKGROUND Bile duct stones(BDSs)may cause patients to develop liver cirrhosis or even liver cancer.Currently,the success rate of surgical treatment for intrahepatic and extrahepatic BDSs is not satisfactory,and there is a risk of postoperative complic-ations.AIM To compare the clinical effects of dual-modality endoscopy(duodenoscopy and laparoscopy)with those of traditional laparotomy in the treatment of intra-and extrahepatic BDSs.METHODS Ninety-five patients with intra-and extrahepatic BDSs who sought medical services at Wuhan No.1 Hospital between August 2019 and May 2023 were selected;45 patients in the control group were treated by traditional laparotomy,and 50 patients in the research group were treated by dual-modality endoscopy.The following factors were collected for analysis:curative effects,safety(incision infection,biliary fistula,lung infection,hemobilia),surgical factors[surgery time,intraoperative blood loss(IBL)volume,gastrointestinal function recovery time,and length of hospital stay],serum inflammatory markers[tumor necrosis factor(TNF)-α,interleukin(IL)-6,and IL-8],and oxidative stress[glutathione peroxidase(GSH-Px),superoxide dismutase(SOD),malondialdehyde(MDA),and advanced protein oxidation products(AOPPs)].RESULTS The analysis revealed markedly better efficacy(an obviously higher total effective rate)in the research group than in the control group.In addition,an evidently lower postoperative complication rate,shorter surgical duration,gastrointestinal function recovery time and hospital stay,and lower IBL volume were observed in the research group.Furthermore,the posttreatment serum inflammatory marker(TNF-α,IL-6,and IL-8)levels were significantly lower in the research group than in the control group.Compared with those in the control group,the posttreatment GSH-Px,SOD,MDA and AOPPs in the research group were equivalent to the pretreatment levels;for example,the GSH-Px and SOD levels were significantly higher,while the MDA and AOPP levels were lower.CONCLUSION Dual-modality endoscopy therapy(duodenoscopy and laparoscopy)is more effective than traditional laparotomy in the treatment of intra-and extrahepatic BDSs and has a lower risk of postoperative complications;significantly shortened surgical time;shorter gastrointestinal function recovery time;shorter hospital stay;and lower intraop-erative bleeding volume,while having a significant inhibitory effect on excessive serum inflammation and causing little postoperative oxidative stress.展开更多
In this work, we report the synthesis of holmium（III）-doped carbon nanodots（Ho BCDs） as fluorescence/magnetic resonance（FL/MR） dual-modal imaging probes via a facile hydrothermal process using citrate acid（CA）...In this work, we report the synthesis of holmium（III）-doped carbon nanodots（Ho BCDs） as fluorescence/magnetic resonance（FL/MR） dual-modal imaging probes via a facile hydrothermal process using citrate acid（CA）, branched-polyethylenimine（BPEI） and diethylenetriamine pentaacetic acid hydrate holmium（III） dihydrogen salt（Ho-DTPA） as carbon source, passivating reagent and holmium source, respectively.The thus prepared Ho BCDs exhibited ultra-small particle size（~4 nm）, high water solubility and bright fluorescence with an absolute quantum yield of 8%. Additionally, grey-scaled T_1-weighted images of Ho BCDs solution appeared to be apparently brighter than that of deionized water and un-doped blue carbon nanodots（BCDs） solution. In addition, in vitro toxicity assay validated superior biocompatibility of Ho BCDs. Using He La cells as models, Ho BCDs-treated cells were observed to emit blue fluorescence located both in plasma and nucleus, and presented positive contrast enhancement in T_1-weighted images, suggesting their potentials for practical biomedical applications.展开更多
When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the feature...When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.展开更多
We report on tests of combined positron emission tomography(PET)andfluorescence molecular tomography(FMT)imaging system for in vivo investigation on small animals.A nude mouse was inoculated with MD-MB-231 breast canc...We report on tests of combined positron emission tomography(PET)andfluorescence molecular tomography(FMT)imaging system for in vivo investigation on small animals.A nude mouse was inoculated with MD-MB-231 breast cancer cells which expressed redfluorescent protein(RFP).For FMT system,reflective illumination mode was adopted with full-angle data acquisition.[18F]-Fluorodeoxyglucose([18F]-FDG)was used as radioactive tracer for PET.Both data were acquired simultaneously and then reconstructed separately before fusion.Fluorescent tomography results showed exactly where the tumor was located while PET results offered more metabolic information.Results confirmed feasibility for tumor detection and showed superiority to single modality imaging.展开更多
We propose a high-speed all-optic dual-modal system that integrates spectral domain optical coherence tomography and photoacoustic microscopy(PAM).A 3*3 coupler-based interfer-ometer is used to remotely detect the sur...We propose a high-speed all-optic dual-modal system that integrates spectral domain optical coherence tomography and photoacoustic microscopy(PAM).A 3*3 coupler-based interfer-ometer is used to remotely detect the surface vibration caused by photoacoustic(PA)waves.Three outputs of the interferometer are acquired simultaneously with a multi-channel data ac-quisition card.One channel data with the highest PA signal detection sensitivity is selected for sensitivity compensation.Experiment on the phantom demonstrates that the proposed method can sucessfully compensate for the loss of intensity caused by sensitivity variation.The imaging speed of the PAM is improved compared to our previous system.The total time to image a sample with 256×256 pixels is~20s.Using the proposed system,the microvasculature in the mouse auricle is visualized and the blood flow state is accessed.展开更多
Mesoporous structured MnSiO3@Fe3O4@C nanoparticles(NPs)were prepared via a facile and efficient strategy,with negligible cytotoxicity and minor side efforts.The as-prepared MnSiO3@Fe3O4@C NPs hold great potential in s...Mesoporous structured MnSiO3@Fe3O4@C nanoparticles(NPs)were prepared via a facile and efficient strategy,with negligible cytotoxicity and minor side efforts.The as-prepared MnSiO3@Fe3O4@C NPs hold great potential in serving as pH-responsive T1-T2^*dual-modal magnetic resonance(MR)imaging contrast agents.The released Mn^2+shortened T1 relaxation time,meanwhile the superparamagnetic Fe3O4 enhanced T2 contrast imaging.The release rate of Mn ions reaches 31.66%under the condition of pH=5.0,which is similar to tumor microenvironment and organelles.Cytotoxicity assays show that MnSiO3@Fe3O4@C NPs have minor toxicity,even at high concentrations.After intravenous injection of MnSiO3@Fe3O4@C NPs,a rapid contrast enhancement in tumors was achieved with a significant enhancement of 132%after 24 h of the administration.Moreover,a significant decreasement of 53.8%was witnessed in T2 MR imaging signal.It demonstrated that MnSiO3@Fe3O4@C NPs can act as both positive and negative MR imaging contrast agents.Besides,owing to the pH-responsive degradation of mesoporous MnSiO3,MnSiO3@Fe3O4@C NPs can also be used as potential drug systems for cancer theranostics.展开更多
The study designed a polyacrylic acid(PAA)modified Fe3O4@MnO2 nanoparticles(Fe3O4@MnO2@PAA)for T1/T2 dualmode imaging.In addition,this nano-drug has pH response and anti-tumor photothermal therapy.First,using Fe3O4 as...The study designed a polyacrylic acid(PAA)modified Fe3O4@MnO2 nanoparticles(Fe3O4@MnO2@PAA)for T1/T2 dualmode imaging.In addition,this nano-drug has pH response and anti-tumor photothermal therapy.First,using Fe3O4 as the core can significantly reduce the signal of Fe3O4@MnO2@PAA nanoparticles.MnO2 nanoshells can be decomposed into paramagnetic Mn2+under the acidic environment in the tumor,which enhanced the T1 signal.The pH-responsive T1/T2 dual-mode magnetic resonance imaging(MRI)contrast agent had good sensitivity and specificity,providing more comprehensive and detailed information for tumor diagnosis.In addition,Fe3O4@MnO2@PAA nanoparticles showed excellent absorption capacity in the near-infrared region(NIR),which could be used as a good photothermal conversion material to mediate photothermal treatment of tumors.Therefore,the pHresponsive dual-mode MRI nanoparticle-mediated photothermal therapy showed good application potential in tumor treatment and diagnosis.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the e...BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
基金supported by the Hubei Province Nature Science Foundation of China(Grant No.:2023AFB1077)the National Natural Science Foundation of China(Grant No.:82003308)+2 种基金the Doctoral Start-up Fund Project of Hubei University of Science and Technology,China(Grant No.:BK202118)the Innovation team and Medical research program of Hubei University of Science and Technology,China(Grant Nos.:2023T10 and 2022YKY05)the Hubei Province Key R&D Plan Big Health Local Special Project,China(Grant No.:2022BCE042).
文摘Transcatheter arterial embolization(TAE)is the mainstay for treating advanced hepatocellular carcinoma(HCC),and the performance of the embolization material is crucial in TAE.With the development of medical imaging and the birth of“X-ray-free”technologies,we designed a new dual-mode imaging material of dimethoxy tetraphenyl ethylene(DMTPE)via emulsification by mixing poly(N-isopropylacrylamide-co-acrylic acid)(PNA)with lipiodol and fluorocarbons,which was evaluated for temperature sensitivity,stability,and dual-mode visualization in vitro.Additionally,blood vessel casting embolization and renal artery imaging were assessed in healthy rabbits.In a rabbit model with a VX2 tumor,the effectiveness of TAE for treating HCC was examined,with an emphasis on evaluating long-term outcomes of embolization and its effects on tumor growth,necrosis,and proliferation through imaging techniques.In vitro experiments confirmed that the temperature-sensitive dual-oil-phase Pickering emulsion had good flow,stable contrast,and embolism when the oil-to-oil ratio and water-to-oil ratio were both 7:3(v/v)and stabilized with 8%PNA.Similarly,in vivo,arterial embolization confirmed the excellent properties of DMTPE prepared at the abovementioned ratios.It was observed that DMTPE not only has an antitumor effect but can also achieve dual imaging using X-rays and ultrasound,making it a promising excellent vascular embolization material for TAE in tumor treatment.
基金supported by the National Natural Science Foundation of China (Nos. 21577037 and 21738002)the State Key Laboratory of Bioreactor Engineering, Shanghai Natural Science Fund (No. 20ZR1414700)+2 种基金Shanghai Sailing Program (No. 19YF1412500)Natural Science Basic Research Program of Shaanxi (No. 2019JQ-924)Key Breeding Program by Collaborative Innovation Center of Green Manufacturing Technology for Traditional Chinese Medicine in Shaanxi Province (No. 2019XT-1-03)。
文摘Selective detection of multiple analytes in a compact design with dual-modality and theranostic features presents great challenges. Herein, we wish to report a coumarin-thiazolidine masked D-penicillamine based dual-modality fluorescent probe COU-DPA-1 for selective detection, differentiation, and detoxification of multiple heavy metal ions(Ag^(+), Hg^(2+), Cu^(2+)). The probe shows divergent fluorescence(FL)/circular dichroism(CD) responses via divergent bond-cleavage cascade reactions(metal ion promoted C-S cleavage and hydrolysis at two distinctive cleavage sites): FL “turn-off” and CD “turn-on” for Ag+(no hydrolysis), FL “turn-on” and CD “turn-off” for Hg^(+)(imine hydrolysis), and FL “self-threshold ratiometric” and CD “turn-off” for excess Cu^(2+)(lactone and imine hydrolysis), providing the first example of a fluorescence/CD dual-modality probe for multiple species with complimentary responses. Moreover, the bond-cleavage cascade reactions also lead to the formation of D-penicillamine heavy metal ion complexes for potential detoxification treatments.
基金supported by the National Natural Science Foundation of China(No.20904046)the National Basic Research Program(973 Program)(No.2009CB526403) of China+1 种基金the Doctoral Fund of Ministry of Education of China(No.20090101120159)the Qianjiang Talent Program of Zhejiang Province,China(No.2010R10050)
文摘A biodegradable tumor targeting nano-probe based on poly（ε-caprolactone）-b-poly（ethylene glycol） block copolymer （PCL-b-PEG）micelle functionalized with a magnetic resonance imaging （MRI） contrast agent diethylenetriaminepentaacetic acid-gadolinium （DTPA-Gd＋） on the shell and a near-infrared （NIR） dye in the core for magnetic resonance and optical dual-modality imaging was prepared. The longitudinal relaxivity （rl） of the PCL-b-PEG- DTPA-Gd3＋ micelle was 13.4 （mmol/L）^-1s^-1, three folds of that of DTPA-Gd3＋, and higher than that of many polymeric contrast agents with similar structures. The in vivo optical imaging of a nude mouse bearing xenografied breast tumor showed that the dual-modality micelle preferentially accumulated in the tumor via the folic acid-mediated active targeting and the passive accumulation by the enhanced permeability and retention （EPR） effect. The results indicated that the dualmodality micelle is a promising nano-probe for cancer detection and diagnosis.
基金Supported by 2021 Municipal Health Commission Scientific Research Project,No.WX21D482021 Municipal Health Commission Project,No.WZ21Q112022 Hubei Provincial Department of Science and Technology Project,No.2022CFB980.
文摘BACKGROUND Bile duct stones(BDSs)may cause patients to develop liver cirrhosis or even liver cancer.Currently,the success rate of surgical treatment for intrahepatic and extrahepatic BDSs is not satisfactory,and there is a risk of postoperative complic-ations.AIM To compare the clinical effects of dual-modality endoscopy(duodenoscopy and laparoscopy)with those of traditional laparotomy in the treatment of intra-and extrahepatic BDSs.METHODS Ninety-five patients with intra-and extrahepatic BDSs who sought medical services at Wuhan No.1 Hospital between August 2019 and May 2023 were selected;45 patients in the control group were treated by traditional laparotomy,and 50 patients in the research group were treated by dual-modality endoscopy.The following factors were collected for analysis:curative effects,safety(incision infection,biliary fistula,lung infection,hemobilia),surgical factors[surgery time,intraoperative blood loss(IBL)volume,gastrointestinal function recovery time,and length of hospital stay],serum inflammatory markers[tumor necrosis factor(TNF)-α,interleukin(IL)-6,and IL-8],and oxidative stress[glutathione peroxidase(GSH-Px),superoxide dismutase(SOD),malondialdehyde(MDA),and advanced protein oxidation products(AOPPs)].RESULTS The analysis revealed markedly better efficacy(an obviously higher total effective rate)in the research group than in the control group.In addition,an evidently lower postoperative complication rate,shorter surgical duration,gastrointestinal function recovery time and hospital stay,and lower IBL volume were observed in the research group.Furthermore,the posttreatment serum inflammatory marker(TNF-α,IL-6,and IL-8)levels were significantly lower in the research group than in the control group.Compared with those in the control group,the posttreatment GSH-Px,SOD,MDA and AOPPs in the research group were equivalent to the pretreatment levels;for example,the GSH-Px and SOD levels were significantly higher,while the MDA and AOPP levels were lower.CONCLUSION Dual-modality endoscopy therapy(duodenoscopy and laparoscopy)is more effective than traditional laparotomy in the treatment of intra-and extrahepatic BDSs and has a lower risk of postoperative complications;significantly shortened surgical time;shorter gastrointestinal function recovery time;shorter hospital stay;and lower intraop-erative bleeding volume,while having a significant inhibitory effect on excessive serum inflammation and causing little postoperative oxidative stress.
基金supported by grants from Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2014TQO1R417)the Fundamental Research Funds for the Central Universities (No. 171gjc09)Shenzhen Basic Research Program(No.JCYJ20170307140752183)
文摘In this work, we report the synthesis of holmium（III）-doped carbon nanodots（Ho BCDs） as fluorescence/magnetic resonance（FL/MR） dual-modal imaging probes via a facile hydrothermal process using citrate acid（CA）, branched-polyethylenimine（BPEI） and diethylenetriamine pentaacetic acid hydrate holmium（III） dihydrogen salt（Ho-DTPA） as carbon source, passivating reagent and holmium source, respectively.The thus prepared Ho BCDs exhibited ultra-small particle size（~4 nm）, high water solubility and bright fluorescence with an absolute quantum yield of 8%. Additionally, grey-scaled T_1-weighted images of Ho BCDs solution appeared to be apparently brighter than that of deionized water and un-doped blue carbon nanodots（BCDs） solution. In addition, in vitro toxicity assay validated superior biocompatibility of Ho BCDs. Using He La cells as models, Ho BCDs-treated cells were observed to emit blue fluorescence located both in plasma and nucleus, and presented positive contrast enhancement in T_1-weighted images, suggesting their potentials for practical biomedical applications.
基金National Natural Science Foundation of China(Nos.61662070,61363059)Youth Science Fund Project of Lanzhou Jiaotong University(No.2018036)。
文摘When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.
基金The authors would like to thank X.Zhang,faculty of XinAoMDT Technology Co.,Ltd.,for the work of system software development.This work is supported by the National Natural Science Foundation of China under Grant Nos.81071191,60831003,30930092,30872633the Tsinghua-Yue-Yuen Medical Science Foundationthe National Basic Research Program of China(973)under Grant No.2011CB707701.
文摘We report on tests of combined positron emission tomography(PET)andfluorescence molecular tomography(FMT)imaging system for in vivo investigation on small animals.A nude mouse was inoculated with MD-MB-231 breast cancer cells which expressed redfluorescent protein(RFP).For FMT system,reflective illumination mode was adopted with full-angle data acquisition.[18F]-Fluorodeoxyglucose([18F]-FDG)was used as radioactive tracer for PET.Both data were acquired simultaneously and then reconstructed separately before fusion.Fluorescent tomography results showed exactly where the tumor was located while PET results offered more metabolic information.Results confirmed feasibility for tumor detection and showed superiority to single modality imaging.
基金This work was supported in part by the National Natural Science Foundation of China(Grant Nos.61771119,61901100 and 62075037)the Natural Science Foundation of Hebei Province(Grant Nos.H2019501010,F2019501132,E2020501029 and F2020501040).
文摘We propose a high-speed all-optic dual-modal system that integrates spectral domain optical coherence tomography and photoacoustic microscopy(PAM).A 3*3 coupler-based interfer-ometer is used to remotely detect the surface vibration caused by photoacoustic(PA)waves.Three outputs of the interferometer are acquired simultaneously with a multi-channel data ac-quisition card.One channel data with the highest PA signal detection sensitivity is selected for sensitivity compensation.Experiment on the phantom demonstrates that the proposed method can sucessfully compensate for the loss of intensity caused by sensitivity variation.The imaging speed of the PAM is improved compared to our previous system.The total time to image a sample with 256×256 pixels is~20s.Using the proposed system,the microvasculature in the mouse auricle is visualized and the blood flow state is accessed.
基金supported by the National Natural Science Foundation of China(No.21571168)
文摘Mesoporous structured MnSiO3@Fe3O4@C nanoparticles(NPs)were prepared via a facile and efficient strategy,with negligible cytotoxicity and minor side efforts.The as-prepared MnSiO3@Fe3O4@C NPs hold great potential in serving as pH-responsive T1-T2^*dual-modal magnetic resonance(MR)imaging contrast agents.The released Mn^2+shortened T1 relaxation time,meanwhile the superparamagnetic Fe3O4 enhanced T2 contrast imaging.The release rate of Mn ions reaches 31.66%under the condition of pH=5.0,which is similar to tumor microenvironment and organelles.Cytotoxicity assays show that MnSiO3@Fe3O4@C NPs have minor toxicity,even at high concentrations.After intravenous injection of MnSiO3@Fe3O4@C NPs,a rapid contrast enhancement in tumors was achieved with a significant enhancement of 132%after 24 h of the administration.Moreover,a significant decreasement of 53.8%was witnessed in T2 MR imaging signal.It demonstrated that MnSiO3@Fe3O4@C NPs can act as both positive and negative MR imaging contrast agents.Besides,owing to the pH-responsive degradation of mesoporous MnSiO3,MnSiO3@Fe3O4@C NPs can also be used as potential drug systems for cancer theranostics.
文摘The study designed a polyacrylic acid(PAA)modified Fe3O4@MnO2 nanoparticles(Fe3O4@MnO2@PAA)for T1/T2 dualmode imaging.In addition,this nano-drug has pH response and anti-tumor photothermal therapy.First,using Fe3O4 as the core can significantly reduce the signal of Fe3O4@MnO2@PAA nanoparticles.MnO2 nanoshells can be decomposed into paramagnetic Mn2+under the acidic environment in the tumor,which enhanced the T1 signal.The pH-responsive T1/T2 dual-mode magnetic resonance imaging(MRI)contrast agent had good sensitivity and specificity,providing more comprehensive and detailed information for tumor diagnosis.In addition,Fe3O4@MnO2@PAA nanoparticles showed excellent absorption capacity in the near-infrared region(NIR),which could be used as a good photothermal conversion material to mediate photothermal treatment of tumors.Therefore,the pHresponsive dual-mode MRI nanoparticle-mediated photothermal therapy showed good application potential in tumor treatment and diagnosis.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金Supported by Beijing Hospitals Authority Youth Programme,No.QML20200505.
文摘BACKGROUND Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer.Many molecular genetic changes are associated with its occurrence.Raman spectroscopy has become a new method for the early diagnosis of tumors because it can reflect the structures of substances and their changes at the molecular level.AIM To detect alterations in Raman spectral information across different stages of esophageal neoplasia.METHODS Different grades of esophageal lesions were collected,and a total of 360 groups of Raman spectrum data were collected.A 1D-transformer network model was proposed to handle the task of classifying the spectral data of esophageal squamous cell carcinoma.In addition,a deep learning model was applied to visualize the Raman spectral data and interpret their molecular characteristics.RESULTS A comparison among Raman spectral data with different pathological grades and a visual analysis revealed that the Raman peaks with significant differences were concentrated mainly at 1095 cm^(-1)(DNA,symmetric PO,and stretching vibration),1132 cm^(-1)(cytochrome c),1171 cm^(-1)(acetoacetate),1216 cm^(-1)(amide III),and 1315 cm^(-1)(glycerol).A comparison among the training results of different models revealed that the 1Dtransformer network performed best.A 93.30%accuracy value,a 96.65%specificity value,a 93.30%sensitivity value,and a 93.17%F1 score were achieved.CONCLUSION Raman spectroscopy revealed significantly different waveforms for the different stages of esophageal neoplasia.The combination of Raman spectroscopy and deep learning methods could significantly improve the accuracy of classification.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
