Dynamic modelling and chaos control for a thin plate oscillator using Bubnov–Galerkin integral method

The utilization of thin plate systems based on acoustic vibration holds significant importance in micro-nano manipulation and the exploration of nonlinear science. This paper focuses on the analysis of an actual thin plate system driven by acoustic wave signals. By combining the mechanical analysis of thin plate microelements with the Bubnov–Galerkin integral method, the governing equation for the forced vibration of a square thin plate is derived. Notably,the reaction force of the thin plate vibration system is defined as f=α|w|, resembling Hooke’s law. The energy function and energy level curve of the system are also analyzed. Subsequently, the amplitude–frequency response function of the thin plate oscillator is solved using the harmonic balance method. Through numerical simulations, the amplitude–frequency curves are analyzed for different vibration modes under the influence of various parameters. Furthermore, the paper demonstrates the occurrence of conservative chaotic motions in the thin plate oscillator using theoretical and numerical methods. Dynamics maps illustrating the system’s states are presented to reveal the evolution laws of the system. By exploring the effects of force fields and system energy, the underlying mechanism of chaos is interpreted. Additionally, the phenomenon of chaos in the oscillator can be controlled through the method of velocity and displacement states feedback, which holds significance for engineering applications.

Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability

Memristor chaotic systems have aroused great attention in recent years with their potentials expected in engineering applications.In this paper,a five-dimension(5D)double-memristor hyperchaotic system(DMHS)is modeled by introducing two active magnetron memristor models into the Kolmogorov-type formula.The boundness condition of the proposed hyperchaotic system is proved.Coexisting bifurcation diagram and numerical verification explain the bistability.The rich dynamics of the system are demonstrated by the dynamic evolution map and the basin.The simulation results reveal the existence of transient hyperchaos and hidden extreme multistability in the presented DMHS.The NIST tests show that the generated signal sequence is highly random,which is feasible for encryption purposes.Furthermore,the system is implemented based on a FPGA experimental platform,which benefits the further applications of the proposed hyperchaos.

Current Status and Perspectives of Dual-Atom Catalysts Towards Sustainable Energy Utilization

The exploration of sustainable energy utilization requires the imple-mentation of advanced electrochemical devices for efficient energy conversion and storage,which are enabled by the usage of cost-effective,high-performance electro-catalysts.Currently,heterogeneous atomically dispersed catalysts are considered as potential candidates for a wide range of applications.Compared to conventional cata-lysts,atomically dispersed metal atoms in carbon-based catalysts have more unsatu-rated coordination sites,quantum size effect,and strong metal-support interactions,resulting in exceptional catalytic activity.Of these,dual-atomic catalysts(DACs)have attracted extensive attention due to the additional synergistic effect between two adja-cent metal atoms.DACs have the advantages of full active site exposure,high selectiv-ity,theoretical 100%atom utilization,and the ability to break the scaling relationship of adsorption free energy on active sites.In this review,we summarize recent research advancement of DACs,which includes(1)the comprehensive understanding of the synergy between atomic pairs;(2)the synthesis of DACs;(3)characterization meth-ods,especially aberration-corrected scanning transmission electron microscopy and synchrotron spectroscopy;and(4)electrochemical energy-related applications.The last part focuses on great potential for the electrochemical catalysis of energy-related small molecules,such as oxygen reduction reaction,CO_(2) reduction reaction,hydrogen evolution reaction,and N_(2) reduction reaction.The future research challenges and opportunities are also raised in prospective section.

维奈克拉在急性淋巴细胞白血病中的应用和研究进展

维奈克拉是全球首个获批的高选择性口服B细胞淋巴瘤-2(B-cell lymphoma 2,Bcl-2)蛋白抑制剂,具有高亲和性的靶向肿瘤细胞凋亡的独特作用机制,目前已被美国食品药品监督管理局批准应用于治疗慢性淋巴细胞白血病(chronic lymphocytic leukemia,CLL)/小淋巴细胞白血病(small lymphocytic leukemia,SLL)以及不适合接受强诱导化疗或年龄>75岁的新诊断急性髓系白血病(acute myeloid leukemia,AML)。近年来,维奈克拉在治疗急性淋巴细胞白血病(acute lymphoblastic leukemia,ALL)上也显示出其独特的优越性。对于常规治疗方案疗效不佳的特殊亚型ALL,如急性早期前体T淋巴细胞白血病(early T-cell precursor acute lymphoblastic leukemia,ETP-ALL)和ETP-ALL之外的其他复发难治性ALL(relapse/refractory ALL,R/R ALL)经维奈克拉联合化疗或其他靶向药物相较于传统疗法可获得更高的完全缓解(complete response,CR)率和长期生存率,且在费城染色体阳性的ALL(Philadelphia-chromosome positive acute lymphoblastic leukemia,Ph^(+)ALL)中联合酪氨酸激酶抑制剂(tyrosine kinase inhibitor,TKI)也取得了良好的效果,表明维奈克拉在ALL治疗上具有较大前景。本文就维奈克拉在ALL治疗领域的作用及研究进展展开综述。

A Novel Trajectory Tracking Control of AGV Based on Udwadia-Kalaba Approach

Dear Editor,This letter is about an automated guided vehicle(AGV)trajectory tracking control method based on Udwadia-Kalaba(U-K)approach.This method provides a novel,concise and explicit motion equation for constrained mechanical systems with holonomic and/or nonholonomic constraints as well as constraints that may be ideal or nonideal.In this letter.

Research advances in enhanced coal seam gas extraction by controllable shock wave fracturing

With the continuous increase of mining in depth,the gas extraction faces the challenges of low permeability,great ground stress,high temperature and large gas pressure in coal seam.The controllable shock wave(CSW),as a new method for enhancing permeability of coal seam to improve gas extraction,features in the advantages of high efficiency,eco-friendly,and low cost.In order to better utilize the CSW into gas extraction in coal mine,the mechanism and feasibility of CSW enhanced extraction need to be studied.In this paper,the basic principles,the experimental tests,the mathematical models,and the on-site tests of CSW fracturing coal seams are reviewed,thereby its future research directions are provided.Based on the different media between electrodes,the CSW can be divided into three categories:hydraulic effect,wire explosion and excitation of energetic materials by detonating wire.During the process of propagation and attenuation of the high-energy shock wave in coal,the shock wave and bubble pulsation work together to produce an enhanced permeability effect on the coal seam.The stronger the strength of the CSW is,the more cracks created in the coal is,and the greater the length,width and area of the cracks being.The repeated shock on the coal seam is conducive to the formation of complex network fracture system as well as the reduction of coal seam strength,but excessive shock frequency will also damage the coal structure,resulting in the limited effect of the enhanced gas extraction.Under the influence of ground stress,the crack propagation in coal seam will be restrained.The difference of horizontal principal stress has a significant impact on the shape,propagation direction and connectivity of the CSW induced cracks.The permeability enhancement effect of CSW is affected by the breakage degree of coal seam.The shock wave is absorbed by the broken coal,which may hinder the propagation of CSW,resulting in a poor effect of permeability enhancement.When arranging two adjacent boreholes for CSW permeability enhancement test,the spacing of boreholes should not be too close,which may lead to negative pressure mutual pulling in the early stage of drainage.At present,the accurate method for effectively predicting the CSW permeability enhanced range should be further investigated.

Seasonal changes in gut microbiota of sea cucumber over natural aestivation cycle

Sea cucumber Apostichopus japonicus is an ideal model organism for marine invertebrate aestivation;it annually enters a“sleeping state”for nearly 3 months when ambient water temperature range is 25–30℃.The natural fasting is accompanied by remodeling the intestinal biota and function,which is a part of host biology and could affect the gut microbiota.We investigatesd the impact of annual aestivation on gut microbiota using high-throughput sequencing of 16S rRNA amplicons.Results reveal a notable alteration in the composition of gut bacteria during aestivation during which various indigenous families and genera that exhibit a preference for dietary glycans(e.g.,family Rhodobacteraceae and Flavobacteriaceae)decreased,while the taxa capable of utilizing substrates derived from the host(e.g.,genus Akkermansia and Prevotella)increased,and so did certain opportunistic pathogenic bacteria.Moreover,the investigation delved into the gut morphology and immunity response of A.japonicus and reveal that the intestine of A.japonicus undergoes substantial atrophy and degeneration during aestivation.However,there was an observed augmentation in the levels of acid and neutral mucin within mucous cells,as well as an enhanced immune defense response(as evidenced by increased gene expression of AjTLR3,LITAF,Ajp105,and LYZ).These results imply that the composition of sea cucumber gut microbiota differed between aestivation and active periods,which potentially affects the intestinal functions of the host and the symbiotic relationship between host and its microbiota over the activeaestivation cycle.

Hyperbranched polymer hollow-fiber-composite membranes for pervaporation separation of aromatic/aliphatic hydrocarbon mixtures

The separation of aromatic/aliphatic hydrocarbon mixtures is crucial in the petrochemical industry.Pervaporation is regarded as a promising approach for the separation of aromatic compounds from alkanes. Developing membrane materials with efficient separation performance is still the main task since the membrane should provide chemical stability, high permeation flux, and selectivity. In this study, the hyperbranched polymer(HBP) was deposited on the outer surface of a polyvinylidene fluoride(PVDF)hollow-fiber ultrafiltration membrane by a facile dip-coating method. The dip-coating rate, HBP concentration, and thermal cross-linking temperature were regulated to optimize the membrane structure.The obtained HBP/PVDF hollow-fiber-composite membrane had a good separation performance for aromatic/aliphatic hydrocarbon mixtures. For the 50%/50%(mass) toluene/n-heptane mixture, the permeation flux of optimized composite membranes could reach 1766 g·m^(-2)·h^(-1), with a separation factor of 4.1 at 60℃. Therefore, the HBP/PVDF hollow-fiber-composite membrane has great application prospects in the pervaporation separation of aromatic/aliphatic hydrocarbon mixtures.

A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting fromcyber-attacks.Firstly,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data restoration.Secondly,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on HF.Finally,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available capacity.Experiments show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

Enhancing Li-S battery performance via functional polymer binders for polysulfide inhibition

The commercialization of lithium-sulfur(Li-S)batteries faces several challenges,including poor conductivity,unexpected volume expansion,and continuous sulfur loss from the cathode due to redox shuttling.In this study,we introduce a novel polymer via a simple cross-linking between poly(ether-thioureas)(PETU)and poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)as a bifunctio nal binder for Li-S batteries(devotes as"PPTU").Compared to polyvinylidene fluoride(PVDF),as-prepared PPTU exhibits significantly higher electrical conductivity,facilitating electrochemical reactions.Additionally,PPTU demonstrates effective adsorption of lithium polysulfides,leading to improved cycling stability by suppressing the shuttling effect.We investigate this behavior by monitoring morphological changes at the cell interface using synchrotron X-ray tomography.Cells with PPTU binders exhibit remarkable rate performance,desired reversibility,and excellent cycling stability even under stringent bending and twisting conditions.Our work represents promising progress in functional polymer binder development for Li-S batteries.

The value of toxicological analysis in acute poisoning patients with uncertain exposure histories:a retrospective and descriptive study from an institute of poisoning

BACKGROUND:In clinical practice,some patients might not be able or unwilling to provide a thorough history of medication and poison exposure.The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective.METHODS:This was a retrospective and descriptive study from an institute of poisoning.Patient registration information and test reports spanning the period from April 1,2020 to March 31,2022,were obtained.Patients with uncertain exposure histories and who underwent toxicological analysis were included.Clinical manifestations and categories of toxics were analyzed.RESULTS:Among the 195 patients with positive toxicological analysis results,the main causes of uncertain exposure history was disturbance of consciousness(62.6%),unawareness(23.6%)and unwillingness or lack of cooperation(13.8%).The predominant clinical manifestations were disturbed consciousness(62.6%),followed by vomiting and nausea(14.4%)and liver function abnormalities(8.7%).A comparison of clinical manifestations between patients with positive and negative(n=99)toxicological analyses results revealed significantly different proportions of disturbances in consciousness(63%vs.21%),dizziness(1.5%vs.5.1%),multi-organ failure(1.5%vs.7.1%),and local pain(0 vs 4%).The main categories of substances involved were psychiatric medications(23.1%),sedatives(20.5%),insecticides(13.8%),and herbicides(12.8%).CONCLUSION:The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific,and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.

FL-EASGD:Federated Learning Privacy Security Method Based on Homomorphic Encryption

Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data.However,there is still a potential risk of privacy leakage,for example,attackers can obtain the original data through model inference attacks.Therefore,safeguarding the privacy of model parameters becomes crucial.One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process.However,the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes.To solve the above problems,this paper proposes a privacy protection scheme named Federated Learning-Elastic Averaging Stochastic Gradient Descent(FL-EASGD)based on a fully homomorphic encryption algorithm.First,this paper introduces the homomorphic encryption algorithm into the FL-EASGD scheme to preventmodel plaintext leakage and realize privacy security in the process ofmodel aggregation.Second,this paper designs a robust model aggregation algorithm by adding time variables and constraint coefficients,which ensures the accuracy of model prediction while solving performance differences such as computation speed and node anomalies such as downtime of each participant.In addition,the scheme in this paper preserves the independent exploration of the local model by the nodes of each party,making the model more applicable to the local data distribution.Finally,experimental analysis shows that when there are abnormalities in the participants,the efficiency and accuracy of the whole protocol are not significantly affected.

A Novel Approach for Trajectory Tracking Control of an Under-Actuated Quad-Rotor UAV

Dear Editor,This letter presents a novel Udwadia-Kalaba(U-K)approach for the trajectory tracking control of a quad-rotor unmanned aerial vehicle(UAV).Compared to conventional control approaches,the desired trajectories are treated as trajectory tracking constraints in this approach.

Event-Triggered Bipartite Consensus Tracking and Vibration Control of Flexible Timoshenko Manipulators Under Time-Varying Actuator Faults

For bipartite angle consensus tracking and vibration suppression of multiple Timoshenko manipulator systems with time-varying actuator faults,parameter and modeling uncertainties,and unknown disturbances,a novel distributed boundary event-triggered control strategy is proposed in this work.In contrast to the earlier findings,time-varying consensus tracking and actuator defects are taken into account simultaneously.In addition,the constructed event-triggered control mechanism can achieve a more flexible design because it is not required to satisfy the input-to-state condition.To achieve the control objectives,some new integral control variables are given by using back-stepping technique and boundary control.Moreover,adaptive neural networks are applied to estimate system uncertainties.With the proposed event-triggered scheme,control inputs can reduce unnecessary updates.Besides,tracking errors and vibration states of the closed-looped network can be exponentially convergent into some small fields,and Zeno behaviors can be excluded.At last,some simulation examples are given to state the effectiveness of the control algorithms.

UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality rate.SAH lesions are generally diffusely distributed,showing a variety of scales with irregular edges.The complex characteristics of lesions make SAH segmentation a challenging task.To cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH segmentation.Specifically,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale features.Second,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features.Third,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed UDT.Extensive experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

Surrounding Rock Failure Mechanism and Control Technology of Gob-Side Entry with Triangle Coal Pillar at Island Longwall Panel in 15 m Extra-Thick Coal Seams

Taking the return air roadway of Tashan 8204 isolated island working face as the background, the evolution law of the stress field in the surrounding rock of the widened coal pillar area roadway during the mining period of the isolated island working face is obtained through numerical simulation. The hazardous area of strong mine pressure under different coal pillar widths is determined. Through simulation, it is known that when the width of the coal pillar is less than 20 m, there is large bearing capacity on the coal side of the roadway entity. The force on the side of the coal pillar is relatively small. When the width of the coal pillar ranges from 25 m to 45 m, the vertical stress on the roadway and surrounding areas is relatively high. Pressure relief measures need to be taken during mining to reduce surrounding rock stress. When the width of the coal pillar is greater than 45 m, the peak stress of the coal pillar is located in the deep part of the surrounding rock, but it still has a certain impact on the roadway. It is necessary to take pressure relief measures to transfer the stress to a deeper depth to ensure the stability of the triangular coal pillar during the safe mining period of the working face. This provides guidance for ensuring the stability of the triangular coal pillar during the safe mining period of the working face.

Verifying the Authorship of the Yasunari Kawabata Novel The Sound of the Mountain

Yasunari Kawabata was a famous Japanese novelist and the winner of the 1968 Nobel Prize for Literature. However, considerable debate has persisted concerning the authorship of one of his novels, The Sound of the Mountain, with some claiming that it was in fact written by another celebrated author, Yukio Mishima. In this research, we attempt to resolve this issue by applying character bigrams, part-of-speech bigrams, and phrase patterns as stylometric features, and principal component analysis, hierarchical cluster analysis, and random forests as authorship attribution methods. As a result, we obtained compelling evidence to show that Yukio Mishima was not the author of The Sound of the Mountain.

Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

Currently,accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited.A novel multi‐scale CNN‐LSTM neural network(MSCNN‐LSTMNet)is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis.First,a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised.Considering the essential characteristics of measured vibration signals at different scales,a multi‐scale convolutional NN(CNN)block is designed to realize multi‐scale feature extraction.Specifically,multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features,enhancing the robustness of the model.On this basis,the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisa-tion performances.The effectiveness of MSCNN‐LSTMNet is validated by experi-mental results of both one‐and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions.The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods.Under strong noise conditions(−10 dB signal‐to‐noise ratio)for four datasets,MSCNN‐LSTMNet obtained 97.561%average accuracy,while average accuracy for random forest,deep neural network,CNN and MSCNNNet were 73.828%,79.544%,82.247%,and 89.741%,respectively.Moreover,for 11 noise generalisation tasks between different noise levels,MSCNN‐LSTMNet obtained at least 96.679%,97.849%,98.892%,and 94.010%accuracy on the four datasets,which are much higher than those of the existing methods.

Porous heterostructure of graphene/hexagonal boron nitride as an efficient electrocatalyst for hydrogen peroxide generation

Compared with the traditional heteroatom doping,employing heterostructure is a new modulating approach for carbon-based electrocatalysts.Herein,a facile ball milling-assisted route is proposed to synthesize porous carbon materials composed of abundant graphene/hexagonal boron nitride(G/h-BN)heterostructures.Metal Ni powder and nanoscale h-BN sheets are used as a catalytic substrate/hard template and“nucleation seed”for the formation of the heterostructure,respectively.As-prepared G/h-BN heterostructures exhibit enhanced electrocatalytic activity toward H_(2)O_(2) generation with 86%-95%selectivity at the range of 0.45-0.75 V versus reversible hydrogen electrode(RHE)and a positive onset potential of 0.79 versus RHE(defined at a ring current density of 0.3 mA cm^(-2))in the alkaline solution.In a flow cell,G/h-BN heterostructured electrocatalyst has a H_(2)O_(2) production rate of up to 762 mmol g_(catalyst)^(-1) h^(-1) and Faradaic efficiency of over 75%during 12 h testing,superior to the reported carbon-based electrocatalysts.The density functional theory simulation suggests that the B atoms at the interface of the G/h-BN heterostructure are the key active sites.This research provides a new route to activate carbon catalysts toward highly active and selective O_(2)-to-H_(2)O_(2) conversion.

High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning

Single atomic catalysts(SACs),especially metal-nitrogen doped carbon(M-NC)catalysts,have been extensively explored for the electrochemical oxygen reduction reaction(ORR),owing to their high activity and atomic utilization efficiency.However,there is still a lack of systematic screening and optimization of local structures surrounding active centers of SACs for ORR as the local coordination has an essential impact on their electronic structures and catalytic performance.Herein,we systematic study the ORR catalytic performance of M-NC SACs with different central metals and environmental atoms in the first and second coordination sphere by using density functional theory(DFT)calculation and machine learning(ML).The geometric and electronic informed overpotential model(GEIOM)based on random forest algorithm showed the highest accuracy,and its R^(2) and root mean square errors(RMSE)were 0.96 and 0.21,respectively.30 potential high-performance catalysts were screened out by GEIOM,and the RMSE of the predicted result was only 0.12 V.This work not only helps us fast screen high-performance catalysts,but also provides a low-cost way to improve the accuracy of ML models.