This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based...This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.展开更多
To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on int...To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.展开更多
Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory v...Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.展开更多
Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in de...Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Loss of immune tolerance to central nervous system(CNS)antigens lies at the heart of multiple sclerosis(MS),the most common chronic autoimmune disease of the CNS.MS affects nearly2 million people wo rldwide and is cha...Loss of immune tolerance to central nervous system(CNS)antigens lies at the heart of multiple sclerosis(MS),the most common chronic autoimmune disease of the CNS.MS affects nearly2 million people wo rldwide and is chara cterized by focal areas of demyelination,inflammation,axonal injury,and neurodegeneration(Bronge et al.,2022;Magliozzi et al.,2023).展开更多
Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framew...Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framework that jointly designs distributed observers and local controllers to ensure robust formation control in the presence of external disturbances and sensor malfunctions.Treating the spacecraft formation as a single interconnected system,each spacecraft constructs a distributed observer that estimates the overall system state by incorporating both its own measurements and the predicted control information shared among the spacecraft.Based on the observer estimates,a local control law is synthesized to maintain the desired formation.Rigorous theoretical analysis and numerical simulations demonstrate that the proposed integrated approach effectively guarantees formation stability and resilience against sensor failures and disturbances.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning ofte...BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.展开更多
Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures...Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures.This study investigates the influence of adjoining electrode interactions on the interfacial response of a multi-electrode/TE substrate structure,including interfacial stresses and stress intensity factors at the electrode ends.To solve the corresponding boundary-value problem,the Fourier transforms are adopted to derive a governing integro-differential equation for the interfacial shear stress in multi-electrode systems,incorporating the TE effects as generalized forces on the right-hand side.The results show that both the interfacial tension and transverse stress in the electrodes are significantly affected by the presence of adjacent electrodes.The interaction between neighboring electrodes diminishes as their spacing increases or when an adhesive interlayer is introduced.Furthermore,the softer and thinner electrodes,the softer and thicker adhesive interlayer,and the smaller TE loads are found to be beneficial for improving the interfacial performance.These findings may contribute to the accurate measurement in surface sensors and layout design of multi-point health monitoring systems for TE structures.展开更多
AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patie...AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patients diagnosed with MS and a control group of 43 healthy hospital staff matched in terms of age and sex at Iran University,department of neurology and ophthalmology from March 2020 to March 2021.The ophthalmic parameters of each patient were recorded,and optical coherence tomography was used to evaluate the retinal thickness in the layers.RESULTS:This study enrolled a total of 85 participants,with a mean age of 40.44±11.52 years,including 61 females(72%).The control group consisted of 43 individuals with a mean age of 39.49±11.07 years,while the MS group comprised 42 participants with a mean age of 41.40±12.01 years.The mean disease duration in the MS group was 8.45±6.04 a.The thickness of the ganglion cell layer in the right eye was significantly lower in the MS group compared to the control group(P=0.034).In addition,except for the left nasal sector(P=0.106),the mean peripapillary neurofibrillation in all examined sectors were significantly lower in the MS group than in the control group(P<0.05).The average vessel density in both the deep and superficial capillary plexuses across all regions of both eyes was lower in the MS group than in the control group,with all comparisons for the superficial capillary plexus showing statistical significance(P<0.05 for all except the left nasal sector).CONCLUSION:The thickness of the retina of patients with MS is significantly reduced.Therefore,optical coherence tomography results can be used as a reliable tool to evaluate disease progression and prognosis in MS patients.展开更多
To solve the problem of in-flight actuator faults and parameter uncertainties for multiple Unmanned Aerial Vehicles(UAVs),and reduce the communication and computational resource consumption of multiple UAVs,a Fraction...To solve the problem of in-flight actuator faults and parameter uncertainties for multiple Unmanned Aerial Vehicles(UAVs),and reduce the communication and computational resource consumption of multiple UAVs,a Fraction-Order(FO)sliding-mode Fault-Tolerant Cooperative Control(FTCC)strategy is proposed for multiple UAVs based on Event-Triggered Communication Mechanism(ET-COM-M)and Event-Triggered Control Mechanism(ET-CON-M).First,by considering the limited communication bandwidth of multiple UAVs in formation,an ET-COM-M is designed to significantly reduce communication times.Then,a distributed observer is skillfully constructed to estimate the reference signals for follower UAVs.Moreover,the adaptive strategy is incorporated into the Radial Basis Function Neural Network(RBFNN)to learn the lumped unknown terms for handling bias actuator faults and parameter uncertainties.Besides,the Nussbaum method is used to deal with the loss-of-effectiveness faults.To further achieve the refined control performance against faults,FO calculus is artfully integrated into the sliding-mode control protocol with ET-CON-M.Finally,Zeno behavior is excluded by rigorous theoretical analysis and Lyapunov stability is proved to show the effectiveness of the designed FTCC strategy.Simulation results show that the designed FTCC strategy with Event-Triggered Mechanism(ETM)can guarantee the safety of multiple UAVs and simultaneously reduce the communication and control frequencies,making the developed control scheme applicable in engineering.展开更多
This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorp...This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorporates a reference command generation layer,which derives UAV attitude commands based on formation requirements,and a tracking control layer to ensure accurate execution.Collaborative variables,including trajectory position and flight speed,are defined using a three-dimensional track particle and autopilot model,enabling the development of a consensus-based formation control law.Desired attitude angles are computed through altitudehold and coordinated-turn strategies.A sliding surface is designed based on reference models derived from flight quality metrics,while an adaptive controller compensates for aerodynamic model uncertainties.To enhance learning capabilities,a prediction error mechanism based on a series-parallel estimation model is introduced,enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system.This facilitates the design of a distributed composite learning law.Lyapunov stability analysis confirms the local exponential stability of the tracking error.The simulations of a twelve-UAV formation,along with comparative analysis of two algorithms,demonstrate the system’s capability for formation maintenance and high-precision tracking control.展开更多
Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more ...Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more effective and safer treatments.In the present study,we administered Lycium barbarum glycopeptide to a mouse model of experimental autoimmune encephalomyelitis-an animal model of multiple sclerosis-and evaluated its effects on pathogenic CD4^(+)T cell activation both in vivo and in vitro.Lycium barbarum glycopeptide significantly mitigated the clinical severity of experimental autoimmune encephalomyelitis,as demonstrated by reduced demyelination and neuroinflammation.Moreover,Lycium barbarum glycopeptide treatment decreased the infiltration of peripheral leukocytes into the central nervous system and suppressed pro-inflammatory cytokine expression.Lycium barbarum glycopeptide also modulated pathogenic CD4^(+)T cell activation by inhibiting T helper 1/T helper 17 cell differentiation while promoting regulatory T cell expansion.Notably,no side effects were observed,suggesting the long-term safety and tolerability of Lycium barbarum glycopeptide.Furthermore,RNA sequencing data indicated that Lycium barbarum glycopeptide inhibits activator protein-1,an essential regulator of T cell activation and differentiation.This finding was supported by the reversal of T helper/T helper 17 cell response suppression upon AP-1 blockade.Collectively,these results highlight the potential of Lycium barbarum glycopeptide as an innovative therapeutic agent for CD4^(+)T cell-associated autoimmune or inflammatory diseases,such as multiple sclerosis.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple rol...Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple roles of mononuclear macrophages in the neuroinflammatory process. Monocytes play a significant role in neuroinflammation, and managing neuroinflammation by manipulating peripheral monocytes stands out as an effective strategy for the treatment of multiple sclerosis, leading to improved patient outcomes. This review outlines the steps involved in the entry of myeloid monocytes into the central nervous system that are targets for effective intervention: the activation of bone marrow hematopoiesis, migration of monocytes in the blood, and penetration of the blood–brain barrier by monocytes. Finally, we summarize the different monocyte subpopulations and their effects on the central nervous system based on phenotypic differences. As activated microglia resemble monocyte-derived macrophages, it is important to accurately identify the role of monocyte-derived macrophages in disease. Depending on the roles played by monocyte-derived macrophages at different stages of the disease, several of these processes can be interrupted to limit neuroinflammation and improve patient prognosis. Here, we discuss possible strategies to target monocytes in neurological diseases, focusing on three key aspects of monocyte infiltration into the central nervous system, to provide new ideas for the treatment of neurodegenerative diseases.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple pri...BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple primary cancers(MPC)has also increased.The rarity of MPC and the absence of sensitive and specific dia-gnostic markers often lead to missed or incorrect diagnoses.It is,therefore,of vital importance to improve the vigilance of clinicians and the accurate diagnosis of this disease.Patients with GI malignancies face a higher relative risk of deve-loping additional primary malignant tumors compared to those with other systemic tumors.Vigilant monitoring and follow-up are crucial,especially for high-risk groups,which include older adults,men,those with addictions to alcohol and tobacco,those with a family history of tumors,and those who have undergone radiotherapy.CASE SUMMARY In this article,we report three cases of MPC,each involving malignant tumors of the GI tract as the initial primary carcinoma,offering insights that may aid in effectively managing similar cases.CONCLUSION Patients with GI malignancies face a higher MPC risk.Developing screening and follow-up protocols may enhance detection and treatment outcomes.展开更多
The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides ide...The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.展开更多
Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance th...Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance the properties of SS through high-speed dispersion,transforming its inherent hydrophilic and oleophobic characteristics into hydrophily and lipophilicity.The modification effects were innovatively assessed by observing the color changes of modified steel slag solutions following the dissolution-settlement equilibrium constant.This approach avoided human-induced errors and improved estimated accuracy in conformance with conventional methods such as oil absorption value,activation index,sedimentation volume,and lipophilicity.The hydrolysis of 3-aminopropyltriethoxysilane(KH)generated–Si(OH)_(3)structure to form hydrogen or covalent bonds with active substances(OH groups)from SS.Concurrently,SS underwent encapsulation via Si–O–Si structure resulting from the dehydration of–Si(OH)_(3).The stearic acid coupling agent(SA),aluminate coupling agent(AC),and titanate coupling agent(TN)underwent chemical reactions with Ca(OH)_(2),Al(OH)_(3),and CaCO_(3)in SS.The acidic SA primarily created stable chemical bonds and acted as a supplement due to its package,reducing surface activity and hydrophilicity while enhancing lipophilicity.Specifically,the optimal modification effect was obtained at 3 wt.%SA.Consequently,3 wt.%SA was established as the benchmark for multiple modifiers and the most effective combination was 3 wt.%SA and 3 wt.%AC.Compared with a single interface modifier,SA corroded the SS surface to provide numerous active sites for further modification by KH,AC,or TN,resulting in a more densely packed structure.In addition,more organic groups on SS prevent the proximity of other particles from agglomerating to achieve dispersion and a synergistic modification,laying a theoretical foundation of SS in a new pathway for organic composite materials.展开更多
基金Supported by the Shenzhen Key Laboratory of Control Theory and Intelligent Systems (ZDSYS20220330161800001)the National Natural Science Foundation of China (62303207)the Guangdong Basic and Applied Basic Research Foundation (2024A1515010725)。
文摘This paper studies cooperative robust parallel operation of multiple actuators over an undirected communication graph.The plant is modeled as an uncertain linear system,and the actuators are linear and identical.Based on the internal model principle,a distributed dynamic output feedback control law is proposed to achieve both robust output regulation of the closed-loop system and plant input sharing among the actuators.A practical example of five motors cooperatively driving an uncertain shaft under an external load torque is presented to show the effectiveness of the proposed control law.
基金supported in part by the International Science and Technology Project of Guangzhou Development District under Grant 2023GH08the Science and Technology Development Fund,MSAR,under Grants 0029/2022/AGJ and 0029/2023/RIA1the Program of Guangdong under Grant 2023A0505020003.
文摘To conduct marine surveys,multiple unmanned surface vessels(Multi-USV)with different capabilities perform collaborative mapping in multiple designated areas.This paper proposes a task allocation algorithm based on integer linear programming(ILP)with flow balance constraints,ensuring the fair and efficient distribution of sub-areas among USVs and maintaining strong connectivity of assigned regions.In the established gridmap,a search-based path planning algorithm is performed on the sub-areas according to the allocation scheme.It uses the greedy algorithm and the A*algorithm to achieve complete coverage of the barrier-free area and obtain an efficient trajectory of each USV.The greedy algorithm enables fast local traversal of unvisited grids,while the A*algorithm ensures navigation to escape from deadlock areas and maintains global path continuity.The comparison of task allocation results proves that the task allocation algorithm based on ILP improves the mapping efficiency and task distribution fairness.The proposed allocation method and result analysis provide a certain reference for the practical application ofMulti-USV to perform survey tasks collaboratively.
文摘Active inflammation in“inactive”progressive multiple sclerosis:Traditionally,the distinction between relapsing-remitting multiple sclerosis and progressive multiple sclerosis(PMS)has been framed as an inflammatory versus degenerative dichotomy.This was based on a broad misconception regarding essentially all neurodegenerative conditions,depicting the degenerative process as passive and immune-independent occurring as a late byproduct of active inflammation in the central nervous system(CNS),which is(solely)systemically driven.
基金partially supported by the National Natural Science Foundation of China(62075137)the Guangdong Basic and Applied Basic Research Foundation(2023A1515140161)+3 种基金the Guangxi Natural Science Foundation of China(2021JJB 110003)the Dongguan Science and Technology of Social Development Program(20231800936312)the high-level talent program of Dongguan University of Technology(No.221110080)the Sanming Project of Medicine in Shenzhen(No.SZSM202103014).
文摘Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
文摘Loss of immune tolerance to central nervous system(CNS)antigens lies at the heart of multiple sclerosis(MS),the most common chronic autoimmune disease of the CNS.MS affects nearly2 million people wo rldwide and is chara cterized by focal areas of demyelination,inflammation,axonal injury,and neurodegeneration(Bronge et al.,2022;Magliozzi et al.,2023).
基金supported by the National Natural Science Foundation of China(62088101,62522307,62273045,U2341213)Beijing Nova Program(20230484481)。
文摘Formation control of multiple spacecraft has attracted extensive research attention.However,achieving reliable performance under sensor failures remains a significant challenge.This paper develops an integrated framework that jointly designs distributed observers and local controllers to ensure robust formation control in the presence of external disturbances and sensor malfunctions.Treating the spacecraft formation as a single interconnected system,each spacecraft constructs a distributed observer that estimates the overall system state by incorporating both its own measurements and the predicted control information shared among the spacecraft.Based on the observer estimates,a local control law is synthesized to maintain the desired formation.Rigorous theoretical analysis and numerical simulations demonstrate that the proposed integrated approach effectively guarantees formation stability and resilience against sensor failures and disturbances.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
基金Supported by Chongqing Medical Scientific Research Project(Joint Project of Chongqing Health Commission and Science and Technology Bureau),No.2023MSXM060.
文摘BACKGROUND The accurate prediction of lymph node metastasis(LNM)is crucial for managing locally advanced(T3/T4)colorectal cancer(CRC).However,both traditional histopathology and standard slide-level deep learning often fail to capture the sparse and diagnostically critical features of metastatic potential.AIM To develop and validate a case-level multiple-instance learning(MIL)framework mimicking a pathologist's comprehensive review and improve T3/T4 CRC LNM prediction.METHODS The whole-slide images of 130 patients with T3/T4 CRC were retrospectively collected.A case-level MIL framework utilising the CONCH v1.5 and UNI2-h deep learning models was trained on features from all haematoxylin and eosinstained primary tumour slides for each patient.These pathological features were subsequently integrated with clinical data,and model performance was evaluated using the area under the curve(AUC).RESULTS The case-level framework demonstrated superior LNM prediction over slide-level training,with the CONCH v1.5 model achieving a mean AUC(±SD)of 0.899±0.033 vs 0.814±0.083,respectively.Integrating pathology features with clinical data further enhanced performance,yielding a top model with a mean AUC of 0.904±0.047,in sharp contrast to a clinical-only model(mean AUC 0.584±0.084).Crucially,a pathologist’s review confirmed that the model-identified high-attention regions correspond to known high-risk histopathological features.CONCLUSION A case-level MIL framework provides a superior approach for predicting LNM in advanced CRC.This method shows promise for risk stratification and therapy decisions,requiring further validation.
基金Project supported by the National Natural Science Foundation of China(Nos.12502117,12272269,11972257)the Natural Science Foundation of Ningxia of China(No.2024AAC03018)+1 种基金the Fundamental Research Funds for the Central Universitiesthe Shanghai Gaofeng Project for University Academic Program Development。
文摘Driven by the trend of device miniaturization and high-density integration,the interaction between adjacent electrodes has become a critical factor affecting the interfacial reliability of thermoelectric(TE)structures.This study investigates the influence of adjoining electrode interactions on the interfacial response of a multi-electrode/TE substrate structure,including interfacial stresses and stress intensity factors at the electrode ends.To solve the corresponding boundary-value problem,the Fourier transforms are adopted to derive a governing integro-differential equation for the interfacial shear stress in multi-electrode systems,incorporating the TE effects as generalized forces on the right-hand side.The results show that both the interfacial tension and transverse stress in the electrodes are significantly affected by the presence of adjacent electrodes.The interaction between neighboring electrodes diminishes as their spacing increases or when an adhesive interlayer is introduced.Furthermore,the softer and thinner electrodes,the softer and thicker adhesive interlayer,and the smaller TE loads are found to be beneficial for improving the interfacial performance.These findings may contribute to the accurate measurement in surface sensors and layout design of multi-point health monitoring systems for TE structures.
文摘AIM:To investigate the changes of retinal vascular parameters and retinal layer thickness in patients with multiple sclerosis(MS).METHODS:This single-centered case-control study was performed on a MS group of 42 patients diagnosed with MS and a control group of 43 healthy hospital staff matched in terms of age and sex at Iran University,department of neurology and ophthalmology from March 2020 to March 2021.The ophthalmic parameters of each patient were recorded,and optical coherence tomography was used to evaluate the retinal thickness in the layers.RESULTS:This study enrolled a total of 85 participants,with a mean age of 40.44±11.52 years,including 61 females(72%).The control group consisted of 43 individuals with a mean age of 39.49±11.07 years,while the MS group comprised 42 participants with a mean age of 41.40±12.01 years.The mean disease duration in the MS group was 8.45±6.04 a.The thickness of the ganglion cell layer in the right eye was significantly lower in the MS group compared to the control group(P=0.034).In addition,except for the left nasal sector(P=0.106),the mean peripapillary neurofibrillation in all examined sectors were significantly lower in the MS group than in the control group(P<0.05).The average vessel density in both the deep and superficial capillary plexuses across all regions of both eyes was lower in the MS group than in the control group,with all comparisons for the superficial capillary plexus showing statistical significance(P<0.05 for all except the left nasal sector).CONCLUSION:The thickness of the retina of patients with MS is significantly reduced.Therefore,optical coherence tomography results can be used as a reliable tool to evaluate disease progression and prognosis in MS patients.
基金supported in part by National Natural Science Foundation of China(Nos.62373188,62003162)the Natural Science Foundation of Jiangsu Province of China(Nos.BK20240182,BK20222012)+2 种基金the Industry-University Research Innovation Foundation for the Chinese Ministry of Education(No.2021ZYA02005)the Aeronautical Science Foundation of China(Nos.20220007052003,20200007018001)the Fundamental Research Funds for the Central Universities,China(Nos.NE2024004,NI2024001)。
文摘To solve the problem of in-flight actuator faults and parameter uncertainties for multiple Unmanned Aerial Vehicles(UAVs),and reduce the communication and computational resource consumption of multiple UAVs,a Fraction-Order(FO)sliding-mode Fault-Tolerant Cooperative Control(FTCC)strategy is proposed for multiple UAVs based on Event-Triggered Communication Mechanism(ET-COM-M)and Event-Triggered Control Mechanism(ET-CON-M).First,by considering the limited communication bandwidth of multiple UAVs in formation,an ET-COM-M is designed to significantly reduce communication times.Then,a distributed observer is skillfully constructed to estimate the reference signals for follower UAVs.Moreover,the adaptive strategy is incorporated into the Radial Basis Function Neural Network(RBFNN)to learn the lumped unknown terms for handling bias actuator faults and parameter uncertainties.Besides,the Nussbaum method is used to deal with the loss-of-effectiveness faults.To further achieve the refined control performance against faults,FO calculus is artfully integrated into the sliding-mode control protocol with ET-CON-M.Finally,Zeno behavior is excluded by rigorous theoretical analysis and Lyapunov stability is proved to show the effectiveness of the designed FTCC strategy.Simulation results show that the designed FTCC strategy with Event-Triggered Mechanism(ETM)can guarantee the safety of multiple UAVs and simultaneously reduce the communication and control frequencies,making the developed control scheme applicable in engineering.
基金co-supported in part by the National Natural Science Foundation of China(No.62403131)in part by Jiangsu Funding Program for Excellent Postdoctoral Talent,China(No.2024ZB267)in part by the Shenzhen Science and Technology Program,China(No.JCYJ20230807145500002)。
文摘This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles(UAVs)formation control within a cooperative consensus framework.The proposed strategy incorporates a reference command generation layer,which derives UAV attitude commands based on formation requirements,and a tracking control layer to ensure accurate execution.Collaborative variables,including trajectory position and flight speed,are defined using a three-dimensional track particle and autopilot model,enabling the development of a consensus-based formation control law.Desired attitude angles are computed through altitudehold and coordinated-turn strategies.A sliding surface is designed based on reference models derived from flight quality metrics,while an adaptive controller compensates for aerodynamic model uncertainties.To enhance learning capabilities,a prediction error mechanism based on a series-parallel estimation model is introduced,enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system.This facilitates the design of a distributed composite learning law.Lyapunov stability analysis confirms the local exponential stability of the tracking error.The simulations of a twelve-UAV formation,along with comparative analysis of two algorithms,demonstrate the system’s capability for formation maintenance and high-precision tracking control.
基金supported by the National Natural Science Foundational of China,Nos.U24A20692(to CJZ),82371355(to CJZ),and 82101414(to MH)National NaturalScience Foundational of China for Excellent Young Scholars,No.82022019(to CJZ)+5 种基金Sichuan Special Fund for Distinguished Young Scholars,No.24NSFJQ0052(to CJZ)The Innovationand Entrepreneurial Team of Sichuan Tianfu Emei Program,No.CZ2024018(to CJZ)Funding for Distinguished Young Scholars of Sichuan Provincial People’sHospital,No.30420230005Funding for Distinguished Young Scholars of University of Electronic Science and Technology of China,No.A1098531023601381(toCJZ)Sichuan Science and Technology Support Project,No.2023YFS0212(to BH)Project of Sichuan Provincial Health Commission,No.19PJ265(to LD).
文摘Multiple sclerosis is a severe autoimmune disorder that is mainly mediated by pathogenic cluster of CD4^(+)T cell subsets.Despite advancements in the management of multiple sclerosis,there is a critical need for more effective and safer treatments.In the present study,we administered Lycium barbarum glycopeptide to a mouse model of experimental autoimmune encephalomyelitis-an animal model of multiple sclerosis-and evaluated its effects on pathogenic CD4^(+)T cell activation both in vivo and in vitro.Lycium barbarum glycopeptide significantly mitigated the clinical severity of experimental autoimmune encephalomyelitis,as demonstrated by reduced demyelination and neuroinflammation.Moreover,Lycium barbarum glycopeptide treatment decreased the infiltration of peripheral leukocytes into the central nervous system and suppressed pro-inflammatory cytokine expression.Lycium barbarum glycopeptide also modulated pathogenic CD4^(+)T cell activation by inhibiting T helper 1/T helper 17 cell differentiation while promoting regulatory T cell expansion.Notably,no side effects were observed,suggesting the long-term safety and tolerability of Lycium barbarum glycopeptide.Furthermore,RNA sequencing data indicated that Lycium barbarum glycopeptide inhibits activator protein-1,an essential regulator of T cell activation and differentiation.This finding was supported by the reversal of T helper/T helper 17 cell response suppression upon AP-1 blockade.Collectively,these results highlight the potential of Lycium barbarum glycopeptide as an innovative therapeutic agent for CD4^(+)T cell-associated autoimmune or inflammatory diseases,such as multiple sclerosis.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Natural Science Foundation of China,Nos.82060219,82271234the Natural Science Foundation of Jiangxi Province,Nos.20212ACB216009,20212BAB216048+1 种基金Jiangxi Province Thousands of Plans,No.jxsq2019201023Youth Team Project of the Second Affiliated Hospital of Nanchang University,No.2019YNTD12003(all to FH)。
文摘Mononuclear macrophage infiltration in the central nervous system is a prominent feature of neuroinflammation. Recent studies on the pathogenesis and progression of multiple sclerosis have highlighted the multiple roles of mononuclear macrophages in the neuroinflammatory process. Monocytes play a significant role in neuroinflammation, and managing neuroinflammation by manipulating peripheral monocytes stands out as an effective strategy for the treatment of multiple sclerosis, leading to improved patient outcomes. This review outlines the steps involved in the entry of myeloid monocytes into the central nervous system that are targets for effective intervention: the activation of bone marrow hematopoiesis, migration of monocytes in the blood, and penetration of the blood–brain barrier by monocytes. Finally, we summarize the different monocyte subpopulations and their effects on the central nervous system based on phenotypic differences. As activated microglia resemble monocyte-derived macrophages, it is important to accurately identify the role of monocyte-derived macrophages in disease. Depending on the roles played by monocyte-derived macrophages at different stages of the disease, several of these processes can be interrupted to limit neuroinflammation and improve patient prognosis. Here, we discuss possible strategies to target monocytes in neurological diseases, focusing on three key aspects of monocyte infiltration into the central nervous system, to provide new ideas for the treatment of neurodegenerative diseases.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金Supported by Gansu Provincial Natural Science Foundation,No.21JR1RA010In-Hospital Research Fund of Gansu Provincial Hospital,No.23GSSYD-5.
文摘BACKGROUND The incidence of malignant gastrointestinal(GI)tumors is increasing,and advancements in medical care have significantly improved patient survival rates.As a result,the number of cases involving multiple primary cancers(MPC)has also increased.The rarity of MPC and the absence of sensitive and specific dia-gnostic markers often lead to missed or incorrect diagnoses.It is,therefore,of vital importance to improve the vigilance of clinicians and the accurate diagnosis of this disease.Patients with GI malignancies face a higher relative risk of deve-loping additional primary malignant tumors compared to those with other systemic tumors.Vigilant monitoring and follow-up are crucial,especially for high-risk groups,which include older adults,men,those with addictions to alcohol and tobacco,those with a family history of tumors,and those who have undergone radiotherapy.CASE SUMMARY In this article,we report three cases of MPC,each involving malignant tumors of the GI tract as the initial primary carcinoma,offering insights that may aid in effectively managing similar cases.CONCLUSION Patients with GI malignancies face a higher MPC risk.Developing screening and follow-up protocols may enhance detection and treatment outcomes.
文摘The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching.This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers.First,the gamma spectra of 12 target nuclides(including the background case)were measured to create training datasets.The characteristic energies,obtained through energy calibration and full-energy peak addresses,are utilized as input features for a neural network.A large number of single-and multiple-nuclide training datasets are generated using random combinations and small-range drifting.Subsequently,a multi-label classification neural network based on a binary cross-entropy loss function is applied to export the existence probability of certain nuclides.The designed algorithm effectively reduces the computation time and storage space required by the neural network and has been successfully implemented in a portable gamma spectrometer with a running time of t_(r)<2 s.Results show that,in both validation and actual tests,the identification accuracy of the designed algorithm reaches 94.8%,for gamma spectra with a dose rate of d≈0.5μSv∕h and a measurement time t_(m)=60 s.This improves the ability to perform rapid on-site nuclide identification at important sites.
基金supported by the National Natural Science Foundation of China(U23A20605)Anhui Graduate Innovation and Entrepreneurship Practice Project(2022cxcysj090)+2 种基金China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202202)the University Synergy Innovation Program of Anhui Province(GXXT-2020-072)the Outstanding Youth Fund of Anhui Province(2208085J19).
文摘Steel slag(SS)accumulates unavoidably due to its complex and unstable composition,high production volumes,and limited value-added resource utilization.Single or multiple interface modifiers were proposed to enhance the properties of SS through high-speed dispersion,transforming its inherent hydrophilic and oleophobic characteristics into hydrophily and lipophilicity.The modification effects were innovatively assessed by observing the color changes of modified steel slag solutions following the dissolution-settlement equilibrium constant.This approach avoided human-induced errors and improved estimated accuracy in conformance with conventional methods such as oil absorption value,activation index,sedimentation volume,and lipophilicity.The hydrolysis of 3-aminopropyltriethoxysilane(KH)generated–Si(OH)_(3)structure to form hydrogen or covalent bonds with active substances(OH groups)from SS.Concurrently,SS underwent encapsulation via Si–O–Si structure resulting from the dehydration of–Si(OH)_(3).The stearic acid coupling agent(SA),aluminate coupling agent(AC),and titanate coupling agent(TN)underwent chemical reactions with Ca(OH)_(2),Al(OH)_(3),and CaCO_(3)in SS.The acidic SA primarily created stable chemical bonds and acted as a supplement due to its package,reducing surface activity and hydrophilicity while enhancing lipophilicity.Specifically,the optimal modification effect was obtained at 3 wt.%SA.Consequently,3 wt.%SA was established as the benchmark for multiple modifiers and the most effective combination was 3 wt.%SA and 3 wt.%AC.Compared with a single interface modifier,SA corroded the SS surface to provide numerous active sites for further modification by KH,AC,or TN,resulting in a more densely packed structure.In addition,more organic groups on SS prevent the proximity of other particles from agglomerating to achieve dispersion and a synergistic modification,laying a theoretical foundation of SS in a new pathway for organic composite materials.
