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Dual-mode All-PEDOT:PSS Organic Electrochemical Transistors:Enzyme/Metal-free Platform for Ultrasensitive Multiplexed Biosensing of Biomarkers 认领 引用 被引量：1
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作者 Xiao-Fang Liu Jia-Hui Lai +8 位作者 Cheng Liu Pei-Pei Liu Yin-Xiu Zuo Huan-Huan Qiu Rong-Ri Tan Jing Li Yu-Kou Du Jing-Kun Xu Feng-Xing Jiang 《Chinese Journal of Polymer Science》 SCIE EI CAS CSCD 2026年第2期371-380,I0009,共10页
Glucose,ascorbic acid(AA),uric acid(UA),and dopamine(DA)are vital biomarkers whose dynamic concentrations correlate with critical diseases;however,multiplexed detection remains challenging for conventional electrochem... Glucose,ascorbic acid(AA),uric acid(UA),and dopamine(DA)are vital biomarkers whose dynamic concentrations correlate with critical diseases;however,multiplexed detection remains challenging for conventional electrochemical sensors because of their limited sensitivity and selectivity.Here,we present a millimeter-scale all-poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS)organic electrochemical transistor(OECT)platform that integrates dual-mode sensing with enzyme/metal-free operation for ultrasensitive biomarker monitoring.By engineering polycrystalline PEDOT:PSS channels via H_2 SO_4 post-treatment,the device achieves record-high conductivity(about(2312.0±29.9)S·cm–1),maximum transconductance(about(2.82±0.12)mS),and on/off ratio(about 210.0±7.8),enabling signal amplification at low gate voltages.The dual-mode strategy combines the selectivity of electrochemistry with the sensitivity of OECTs,realizing simultaneous detection of glucose,AA,UA,and DA with clinical-level sensitivity:detection limits down to 8 nmol·L–1(glucose),0.5 nmol·L–1(AA),5 nmol·L–1(DA),and 0.5 nmol·L–1(UA).Validation using human urine samples yielded recovery rates of 94%–114%.This flexible sensing platform provides a new pathway for the development of wearable biosensors for precision diagnostics. 展开更多
关键词 Dual-mode Electrochemistry Organic electrochemical transistors(OECTs) Organic biosensor Post-treatment
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A red-emissive carbon dots-based fluorescence and colorimetric dual-mode probe for rapid and sensitive detection of perfluorooctanoic acid in environmental samples 认领 引用 被引量：1
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作者 Yongli Liu Guobei Ge +6 位作者 Huanjia Liu Yuxin Wang Penghui Zhou Xiaoyan Su Tianmiao Jin Guifen Zhu Qingxiang Zhou 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第6期464-472,共9页
Perfluorooctanoic acid(PFOA)is a ubiquitous persistent organic pollutant.Hence,developing effective strategies for its fast and efficient detection in environmental samples is imperative for ecological and health safe... Perfluorooctanoic acid(PFOA)is a ubiquitous persistent organic pollutant.Hence,developing effective strategies for its fast and efficient detection in environmental samples is imperative for ecological and health safety.Herein,red-emissive carbon dots(R-CDs)were innovatively fabricated and employed to construct a novel eco-friendly fluorescence and colorimetric dual-mode probe for the effective determination of PFOA.The R-CDs were sourced from meso‑tetra(4-carboxyphenyl)porphine,ethanolamine,and oxalic acid via a facile hydrothermal method.The fluorescent intensity of the R-CDs at 652 nm and the absorbance at 415 nm decreased gradually with increasing concentration of PFOA.The experimental results demonstrated the probe exhibited good linearity across PFOA concentration ranges of 1–100 ng/mL,100–900 ng/mL,and 1000–4000 ng/mL based on fluorescence quenching attributable to the static quenching.Additionally,the probe achieved high sensitivity with a detection limit as low as 0.0012 ng/mL.On the other hand,the probe could detect PFOA in the range of 1000–7000 ng/mL with a detection limit of 54.9 ng/mL according to colorimetric principle.This probe exhibited excellent reliability and satisfactory recoveries when analyzing the trace PFOA in actual water and soil samples.The proposed probe provided a simple,rapid,economical,and environmentally friendly platform for effectively,selectively,and sensitively detecting PFOA in environmental samples. 展开更多
关键词 Red-emissive CDs Perfluorooctanoic acid(PFOA) Dual-mode probe Static quenching effect Environmental samples
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Fluorophore integration strategy endowing 3D covalent organic frameworks with dual-mode white light emission 认领 引用
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作者 Jialiang Liu Chengtao Gong +5 位作者 Jinchen Qian Sen Wang Yi Liang Chenglong Guo Yongwu Peng Jun Pan 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第4期279-283,共5页
The design of covalent organic frameworks(COFs)with strong fluorescence in both solid and solution states present a significant challenge for white-light-emitting diode(WLED)applications,primarily due to the difficult... The design of covalent organic frameworks(COFs)with strong fluorescence in both solid and solution states present a significant challenge for white-light-emitting diode(WLED)applications,primarily due to the difficulty of balancing aggregation-induced emission(AIE)and aggregation-caused quenching(ACQ).Here,we report the synthesis of a 7-fold interpenetrated three-dimensional COF(3D COF)with a pts topology,termed ZJUT-M,constructed by co-condensation of a T4-symmetric monomer with two D2h-symmetric fluorophores exhibiting distinct emission behaviors.ZJUT-M displays robust fluorescence in both solid and solution states,enabling its use in WLEDs.Mechanistic studies reveal that the dual-mode emission is driven by the synergistic integration of high-degree-of-freedom chromophores,which promote AIE emission in the solid state,and conformationally restricted fluorophores,which suppress ACQ effect in solution.These findings provide a strategic pathway for achieving multi-state emissive COFs,opening avenues for their application in advanced optoelectronic devices,including next-generation LEDs. 展开更多
关键词 Three-dimensional covalent organic frameworks Aggregation-induced emission(AIE) Aggregation-caused quenching(ACQ) Fluorophore integration strategy Dual-mode white light emission Next-generation LEDs
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Dual-Mode Sensor with Saturated Mechanochromic Structural Color Enhanced by Black Conductive Hydrogel for Interactive Rehabilitation Monitoring 认领 引用 被引量：1
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作者 Zhiyuan Sun Binhong Yu +10 位作者 Chao Dong Chengjun Yu Lianghe Sheng Zhe Cui Yaming Liu Zhenni Lu Bingda Chen Daixi Xie Zhandong Huang Songshan Zeng Qingdong Ou 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第4期153-171,共19页
Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the s... Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the structural coloration of Cyanocitta stelleri feathers,we developed a dual-mode sensor by utilizing black conductive polymer hydrogel(CPH)-enhanced structural color strategy.This sensor integrates a hydroxypropyl cellulose(HPC)-based structural color interface with a designed CPH sensing component.Highly visible light-absorbing CPH(absorption rate>88%)serves as the critical substrate for enhancing structural color performance.By absorbing incoherent scattered light and suppressing background interference,it significantly enhances the saturation of structural color,thereby achieving a high contrast index of 4.92.Unlike the faint and hardly visible structural colors on non-black substrates,the HPC on CPH displays vivid,highly perceptible colors and desirable mechanochromic behavior.Moreover,the CPH acts as a flexible sensing element,fortified by hydrogen and coordination bond networks,and exhibits exceptional electromechanical properties,including 867.1 kPa tensile strength,strain sensitivity(gauge factor of 4.24),and outstanding durability(over 4400 cycles).Compared to traditional single-mode sensors,the integrated sensor provides real-time visual and digital dual feedback,enhancing the accuracy and interactivity of rehabilitation assessments.This technology holds promise for advancing next-generation rehabilitation medicine. 展开更多
关键词 Conductive hydrogel Structural color Hydroxypropyl cellulose Dual-mode sensor Rehabilitation monitoring
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A Flexible Dual-Mode Photodetector for Human-Machine Collaborative IR Imaging 认领 引用 被引量：2
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作者 Huajing Fang Xinxing Xie +5 位作者 Kai Jing Shaojie Liu Ainong Chen Daixuan Wu Liyan Zhang He Tian 《Nano-Micro Letters》 SCIE EI CAS CSCD 2025年第10期63-75,共13页
Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetec... Photothermoelectric(PTE)photodetectors with selfpowered and uncooled advantages have attracted much interest due to the wide application prospects in the military and civilian fields.However,traditional PTE photodetectors lack of mechanical flexibility and cannot operate independently without the test instrument.Herein,we present a flexible PTE photodetector capable of dual-mode output,combining electrical and optical signal generation for enhanced functionality.Using solution processing,high-quality MXene thin films are assembled on asymmetric electrodes as the photosensitive layer.The geometrically asymmetric electrode design significantly enhances the responsivity,achieving 0.33 m A W-1under infrared illumination,twice that of the symmetrical configuration.This improvement stems from optimized photothermal conversion and an expanded temperature gradient.The PTE device maintains stable performance after 300 bending cycles,demonstrating excellent flexibility.A new energy conversion pathway has been established by coupling the photothermal conversion of MXene with thermochromic composite materials,leading to a real-time visualization of invisible infrared radiation.Leveraging this functionality,we demonstrate the first human-machine collaborative infrared imaging system,wherein the dual-mode photodetector arrays synchronously generate human-readable pattern and machine-readable pattern.Our study not only provides a new solution for functional integration of flexible photodetectors,but also sets a new benchmark for human-machine collaborative optoelectronics. 展开更多
关键词 MXene Dual-mode IR imaging Photothermoelectric Thermochromic Human-machine collaboration
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A novel PEC-EC dual-mode biosensing platform for dual target detection of miRNA-133a and cTnI 认领 引用 被引量：1
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作者 Hejie Zheng Zhili Wang +3 位作者 Guizhen Luo Cuicui Du Xiaohua Zhang Jinhua Chen 《Chinese Chemical Letters》 SCIE CAS CSCD 2025年第4期588-592,共5页
Micro RNA-133a(mi RNA-133a) and cardiac troponin I(c Tn I) are different-type crucial biomarkers of acute myocardial infarction(AMI), whose levels are great significance for AMI diagnosis and treatment. Herein,a novel... Micro RNA-133a(mi RNA-133a) and cardiac troponin I(c Tn I) are different-type crucial biomarkers of acute myocardial infarction(AMI), whose levels are great significance for AMI diagnosis and treatment. Herein,a novel photoelectrochemical-electrochemical(PEC-EC) dual-mode biosensing platform for dual-target assays of mi RNA-133a and c Tn I was developed. In which, a PEC-EC dual-mode sensing platform for mi RNA-133a was constructed based on the changes of the photocurrent inhibition effect and the electrochemical signal of Fc on the Fc-hairpin DNA probe(Fc-HP)/Zn Cd S-quantum dots(QDs)/ITO electrode. Furthermore, under magnetic separation and the specific interaction between c Tn I and its aptamer, the N-doped porous carbon-Zn O polyhedra(NPC-Zn O)-hemin-capture DNA probe hybrid(NH-CP) was obtained and introduced to the Fc-HP/Zn Cd S-QDs/ITO electrode via hybridization between NH-CP and Fc-HP. The hemin molecules encapsulated in NH-CP could effectively induce the photocurrent-polarity-switching of the FcHP/Zn Cd S-QDs/ITO electrode and generate a new electrochemical signal originating from hemin. Thus,c Tn I was assayed sensitively and selectively by the PEC-EC dual-mode biosensing platform. Here, Fc and hemin not only serve as the electrochemical indicators, but also respectively inhibit the photocurrent and switch the photocurrent polarity of Zn Cd S-QDs. Furthermore, the proposed biosensing platform could be easily expanded to the detection of other multiplex-type biomarkers via the change of the sequences of the related DNA probes, implying its significant potential in clinical diagnosis and biological analysis. 展开更多
关键词 Photoelectrochemical assay Electrochemical assay Dual-mode Dual-target miRNA-133a cTnI
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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A Novel Self-Adjusting Dual-Mode Evolutionary Framework for Multi-Task Optimization 认领 引用
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作者 Yingbo Xie Junfei Qiao +1 位作者 Ding Wang Manman Yuan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2239-2252,共14页
Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer a... Evolutionary multi-task optimization(EMTO)presents an efficient way to solve multiple tasks simultaneously.However,difficulties they face in curbing the performance degradation caused by unmatched knowledge transfer and inefficient evolutionary strategies become more severe as the number of iterations increases.Motivated by this,a novel self-adjusting dualmode evolutionary framework,which integrates variable classification evolution and knowledge dynamic transfer strategies,is designed to compensate for this deficiency.First,a dual-mode evolutionary framework is designed to meet the needs of evolution in different states.Then,a self-adjusting strategy based on spatial-temporal information is adopted to guide the selection of evolutionary modes.Second,a classification mechanism for decision variables is proposed to achieve the grouping of variables with different attributes.Then,the evolutionary algorithm with a multi-operator mechanism is employed to conduct classified evolution of decision variables.Third,an evolutionary strategy based on multi-source knowledge sharing is presented to realize the cross-domain transfer of knowledge.Then,a dynamic weighting strategy is developed for efficient utilization of knowledge.Finally,by conducting experiments and comparing the designed method with several existing algorithms,the empirical results confirm that it significantly outperforms its peers in tackling benchmark instances. 展开更多
关键词 Evolutionary algorithms evolutionary multitasking knowledge transfer optimization problem
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：2
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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Dual-modal trajectory planning method for compound-wing UAV leveraging differential flatness in urban environments 认领 引用
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作者 Teng Long Zhenlin Zhou +3 位作者 Jingliang Sun Junzhi Li Zihan Wang Dawei Liu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第6期50-63,共14页
The rise of the low-altitude economy highlights the importance of compound-wing UAVs.However,achieving seamless and optimal trajectories across different flight modes remains a significant challenge due to inherent hi... The rise of the low-altitude economy highlights the importance of compound-wing UAVs.However,achieving seamless and optimal trajectories across different flight modes remains a significant challenge due to inherent high-order discontinuities during mode transitions.To address this limitation,the Spatial-Temporal Adaptive Dual-modal Trajectory Planning(STA-DTP)method for compound-wing UAVs is proposed.By leveraging the differential flatness characteristics of the compound-wing UAV's dual-modal dynamics,a Dynamic collocation-point-based Minimum Control Effort Polynomial(DMINCO)trajectory parameterization model is developed.Its polynomial design ensures high-order continuity and allows for decoupled spatial-temporal representation of dual-modal trajectories,significantly reducing optimization complexity.An adaptive collocation-point decision mechanism for dual-modal transition is designed to address the dependence of trajectory optimality on transition timing.Integrated with an Anytime framework,this mechanism facilitates the real-time generation of feasible dual-modal flight trajectories.Transition collocation points are adaptively assigned based on conflicts between trajectory states and dynamic constraints.Under available computational resources,trajectory optimality is progressively enhanced through the densification collocation-point strategy.Compared with typical trajectory planning algorithms(i.e.,STA-DTP,SFC-SCP,and GPOPS-II)using fixed transition-point strategies,simulation results demonstrate that the proposed method achieves improvements of 1–2 orders of magnitude in planning efficiency and reduces trajectory flight durations as well.Consequently,this work provides an efficient and optimal framework for trajectory planning of compound-wing UAVs in urban environments. 展开更多
关键词 Compound-wing UAV Dual-modal trajectory planning Differential flatness Anytime framework
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Multifunctional FA-PDA@ICG-Bi2Se3 nanoplatform for targeted NIR and X-ray dual-modal imaging and photothermal therapy of cervical cancer 认领 引用
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作者 Zhong Du Li-Jun Zhu +9 位作者 Jia-Bao Xiong Yu-Xiang Gao Ya-Qi Cui Maierhaba Aili Chen-Yang Chu Chi Zhang Gao-Fei Huang Xue-Liang Zhang Nuernisha Alifu Biao Dong 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第4期716-720,共5页
Cervical cancer remains a leading cause of cancer-related mortality in women,underscoring the urgent need for advanced diagnostic and therapeutic strategies.Current imaging techniques face significant limitations,incl... Cervical cancer remains a leading cause of cancer-related mortality in women,underscoring the urgent need for advanced diagnostic and therapeutic strategies.Current imaging techniques face significant limitations,including radiation exposure,high costs,and inadequate sensitivity for detecting early metastases,particularly in imaging and diagnosing tumor metastatic lesions.To overcome these challenges,we developed a multimodal imaging strategy that combined near-infrared fluorescence(NIRF,750-1700 nm)imaging with X-ray imaging,with assistance of innovative nanomaterial to achieve precise tumor targeting and comprehensive diagnostic.Specifically,we synthesized a folate acid-functionalized polydopamine-modified ICG-Bi2Se3 nanocomposite(FA-PDA@ICG-Bi2Se3,FPBI),which integrated the complementary advantages of NIRF,X-ray,and computerized tomography imaging.The FPBI nanocomposite leveraged the targeting capability of folate acid for specific identification of cervical cancer lesions and metastatic lymph node.Furthermore,it demonstrated robust photothermal therapeutic efficacy under near-infrared(808 nm)excitation,achieving significant tumor ablation effects.This work provides an innovative nanoplatform based strategy for multimodal imaging,precise diagnosis,and targeted therapy of cervical cancer,paving the way for improved detection and management of metastatic lesions. 展开更多
关键词 NIR fluorescent imaging Active targeting Dual-modal imaging Metastasis Photothermal therapy
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Optimization of a self-tuning force control system for the milling process using a dynamic enhanced genetic algorithm 认领 引用
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作者 Yao Li Zhengcai Zhao +3 位作者 Ning Qian Lei Zhang Wenfeng Ding Yucan Fu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期33-43,共11页
When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longev... When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longevity,developing a simple,reliable,and easy-to-implement force control system for milling is essential,which is an important step toward advancing intelligent manufacturing.This paper explores the use of genetic algorithms(GA) for powerful optimization capabilities in developing self-tuning milling force controllers.A comprehensive framework for optimizing a fuzzy logic controller using an enhanced GA is specifically designed for the milling process.The optimization integrates the GA with a simulation model,fine-tuning membership functions and optimizing fuzzy rule selection.The enhanced GA incorporates the Integral of Time-weighted Absolute Error(ITAE) as the fitness criterion to improve the robustness and responsiveness of the controller.The optimized fuzzy logic controller is implemented within a computer numerical control system,adjusting feed rates in real-time to control milling forces.The performance of the proposed controller is validated through step and slope milling tests,demonstrating an average control accuracy of 95.52%.Comparative evaluations with other controllers show that the proposed system offers a significant improvement,achieving up to 4.58% better control accuracy in step milling tests. 展开更多
关键词 Optimization Self-tuning Force control system Milling process Genetic algorithm
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asx9puq5uvpvfc6k05.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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An algorithm-assisted high-resolution D-TOF imaging system with reconfigurable macropixel-based SPAD image sensor 认领 引用
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作者 Zhe Wang Jia-xing Song +8 位作者 Na Tian Xing-jia Ni Xu Yang Run-jiang Dou Peng Feng Jian Liu Nan-jian Wu Li-yuan Liu Shuang-ming Yu 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期61-71,共11页
Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TO... Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TOF imaging system that combines a reconfigurable macro-pixel sensor architecture with a lightweight depth completion algorithm to achieve long-range depth imaging with enhanced spatial resolution under low optical power.The proposed sensor adopts a back-side illuminated(BSI)3D-stacked architecture with programmable macro-pixels that enhance detection sensitivity and enable flexible sensitivity–resolution trade-offs.An injection-locked ring-oscillator-based time-to-digital converter(RO-TDC)array achieves a time resolution of 152.5 ps,enabling accurate TOF measurement at an optical power of 10 mW.To compensate for macropixel-induced resolution loss,a probabilistic normalized convolutional neural network(pNCNN)is employed for depth completion using sparse depth inputs only.Experimental results demonstrate that up to 30×effective resolution enhancement of the system can be achieved via the depth completion algorithm without changing the physical resolution of the sensor.Additionally,the proposed system achieves a maximum ranging distance of 90 m and a range-to-power figure-of-merit(FOM)of9 m/mW,which validates the effectiveness of the system. 展开更多
关键词 SPAD reconfigurable macro-pixel time-to-digital converter(TDC) depth completion algorithm
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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