Transfer RNA-derived small RNA serves as potential non-invasive diagnostic marker and a novel therapeutic target for acute pancreatitis

Transfer RNA(tRNA)-derived fragments,a new type of tRNA-derived small RNA(tsRNA),can be cleaved from tRNA by enzymes to regulate target gene expression at the transcriptional and translational levels.tsRNAs are not only degradation fragments but also have biological functions,including those in immune inflammation,metabolic disorders,and cell death.tsRNA dysregulation is closely associated with multiple diseases,including various cancers and acute pancreatitis(AP).AP is a common gastrointestinal disease,and its incidence increases annually.AP development is associated with tsRNAs,which regulate cell injury and induce inflammation,especially pyroptosis and ferroptosis.Notably,serum tRF36 has the potential to serve as a non-invasive diagnostic biomarker and leads to pancreatic acinar cell ferroptosis causing inflammation to promote AP.We show the characteristics of tsRNAs and their diagnostic value and function in AP,and discuss the potential opportunities and challenges of using tsRNAs in clinical applications and research.

Brachioradialis tendon transfer and palmaris longus tendon graft for thumb avulsion:A case report and review of literature

BACKGROUND Thumb replantation following complete traumatic avulsion requires complex techniques to restore function,especially in cases of avulsion at the level of the metacarpophalangeal joint(MCP I)and avulsion of the flexor pollicis longus(FPL)at the musculotendinous junction.Possible treatments include direct tendon suture or tendon transfer,most commonly from the ring finger.To optimize function and avoid donor finger complications,we performed thumb replantation with flexion restoration using brachioradialis(BR)tendon transfer with palmaris longus(PL)tendon graft.CASE SUMMARY A 20-year-old left-handed male was admitted for a complete traumatic left thumb amputation following an accident while sliding from the top of a handrail.The patient presented with skin and bone avulsion at the MCP I,avulsion of the FPL tendon at the musculotendinous junction(zone 5),avulsion of the extensor pollicis longus tendon(zone T3),and avulsion of the thumb’s collateral arteries and nerves.The patient was treated with two stage thumb repair.The first intervention consisted of thumb replantation with MCP I arthrodesis,resection of avulsed FPL tendon and implantation of a silicone tendon prosthesis.The second intervention consisted of PL tendon graft and BR tendon transfer.Follow-up at 10 months showed good outcomes with active interphalangeal flexion of 70°,grip strength of 45 kg,key pinch strength of 15 kg and two-point discrimination threshold of 4 mm.CONCLUSION Flexion restoration after complete thumb amputation with FPL avulsion at the musculotendinous junction can be achieved using BR tendon transfer with PL tendon graft.

维生素D治疗全面性发育迟缓患儿的临床疗效研究

背景 除了某些有明确病因的代谢性疾病导致的全面性发育迟缓(GDD),康复治疗是GDD的主要治疗方式;维生素D通过影响神经营养因子在调节神经细胞的发育和分化方面发挥着重要的神经保护作用;但目前关于补充维生素D对GDD患儿临床疗效的研究开展较少。目的 探讨补充不同剂量的维生素D对GDD患儿康复治疗的临床效果。方法 于2020年9月—2022年6月选取在郑州大学第三附属医院康复医学科首次住院就诊的120例GDD患儿为研究对象,采用随机区组化的方法将其分为常规组(38例)、400 U组(37例)和1 200 U组(35例)。常规组仅进行常规康复治疗;400 U组在常规康复治疗的基础上给予口服400 U/d维生素D;1 200 U组在常规康复训练的基础上给予口服1 200 U/d维生素D。收集3组患儿的性别、就诊年龄等基本资料;于入院时(治疗前)及第3个疗程末(治疗后)行血清25羟维生素D[25(OH)D]水平检测和Gesell发育量表评估[评估适应能力、大运动能力、精细运动能力、语言能力和社交能力5个能区的发育商(DQ)];记录发生在患儿住院期间不良事件的次数,并对上述资料进行分析比较。结果 3组患儿性别、居住地、出生季节、分娩方式、就诊年龄、出生体质量、出生胎龄、主要就诊原因比较,差异均无统计学意义(P>0.05)。治疗前,3组患儿25(OH)D水平、Gesell量表各能区DQ值比较,差异均无统计学意义(P>0.05);治疗后,1 200 U组患儿血25(OH)D水平、Gesell量表大运动能力、精细运动能力、语言能力DQ值高于常规组(P<0.05)。第1、2疗程期间,3组患儿不良事件发生率比较,差异无统计学意义(P>0.05);第3疗程期间,1 200 U组患儿不良事件发生率低于常规组及400 U组(P<0.05)。结论 补充1 200 U维生素D对GDD患儿的康复疗效有益,且能减少康复期间不良事件的发生率。

3D打印矩形粗糙通道内火箭煤油流动换热特性试验方法研究

为探究3D打印再生冷却通道在液体火箭发动机推力室中的替代应用特性,研制了具有不同内表面粗糙度的正弦波纹结构3D打印304不锈钢矩形通道。内截面名义尺寸为2.0 mm×2.0 mm,设计粗糙度分别为6.3、25.0、100.0μm,实际粗糙度Ra分别为11.88、12.70、17.53μm,通过将高温电阻率法和像素法相结合获得了3D打印通道的实际内径和壁厚,修正了火箭煤油流动换热的内壁温和热流密度,建立了3D打印粗糙通道内火箭煤油流动换热特性试验研究方法。试验参数如下:压力处于15~20 MPa范围、质量流速在12450~24900 kg·m^(-2)·s^(-1)之间、热流密度为5~15 MW·m^(-2)、流体温度为-150℃。研究结果表明:火箭煤油流动换热特性受到热流密度、流体温度和质量流速的影响;流体温度处于50~135℃范围内,换热系数增加约25%~33%;热流密度处于5.0~15.0 MW·m^(-2)范围内,换热系数增加了8.3%;质量流速为12450~24900 kg·m^(-2)·s^(-1)范围内,换热系数增加了60.2%。粗糙度增加对火箭煤油流动换热起到强化作用,粗糙度从11.88μm增加到17.53μm时,换热强化幅度超过20%以上。该研究可为3D打印通道在火箭发动机推力室中的替代应用提供参考。

复杂越野场景无人履带平台3D语义占据预测方法

为了理解和处理复杂越野场景中环境要素形状不规则、地形多变及路面属性复杂等问题,提出了一种基于多模态融合感知的3D语义占据预测方法.首先,基于图像和激光雷达融合网络获取初始3D语义标签;然后,对越野场景稀疏点云采用贝叶斯稠密化算法补全3D语义占据标签;最后,生成包含复杂环境要素大小、位置和语义信息的3D语义占据栅格地图.试验结果表明,该方法能够有效地提取和表示复杂越野环境中的3D信息,为复杂越野环境下无人履带平台的路径规划提供了更加准确和丰富的先验信息.

绝经后女性维生素D水平与生殖特点和运动膳食情况的关系

背景:研究证明绝经后骨质疏松症与女性生殖特点和运动膳食有关,但是鲜有研究证明女性生殖特点及运动膳食是否对绝经后女性维生素D有影响。目的:探讨北京市绝经后女性维生素D水平与女性生殖特点和运动膳食情况的关系及其影响因素。方法:选取2017年9月至2018年5月北京市多个城区的17个社区现场问卷调查的727例46-75岁绝经后女性作为研究对象,问卷内容包括受试者的基本信息和月经史、绝经史、妊娠史、运动膳食情况等。采用电化学发光免疫分析仪及其配套试剂测定25-(OH)D水平;使用双能X射线骨密度检测仪测定腰椎(L1-L4、整体)和双髋关节(股骨颈、整体)的骨密度和T值;观察维生素D与生殖特点和运动膳食的相关性,并进行维生素D的影响因素分析。结果与结论:①维生素D正常组136例(18.7%),维生素D不足组389例(53.5%),维生素D缺乏组202例(27.8%);平均维生素D水平为(15.60±5.85)ng/mL。②随着维生素D水平的升高,不同部位的骨密度值及T值也随之升高。③维生素D水平与初潮年龄、月经持续天数、绝经阶段、运动情况、饮食类型、饮食偏好均呈正相关(P<0.05);与月经周期、绝经年龄、生育次数均呈负相关(P<0.05);与初孕年龄、怀孕次数无相关性(P>0.05)。④多因素有序Logistic回归分析结果显示,喝奶制品频率对绝经后女性维生素D值存在正向相关(P<0.05),且奶制品是维生素D水平的保护因素。⑤调查结果说明,北京市绝经后女性维生素D水平普遍不足、骨密度值普遍低下,维生素D水平与部分生殖特点、运动膳食情况存在关联,且其中喝奶制品是其保护因素。因此可以通过开展相关骨质疏松的健康教育,对绝经后女性膳食情况进行调整加以干预,增加体内维生素D含量,以期增加骨密度值,减少绝经后骨质疏松患病率。

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Causal Analysis Between Rice Growth and Cadmium Accumulation and Transfer under Arbuscular Mycorrhizal Inoculation

Cadmium(Cd)contamination in rice has been a serious threat to human health.To investigate the effects of arbuscular mycorrhizal fungi(AMF)on the Cd translocation in rice,a controlled pot experiment was conducted.The results indicated that AMF significantly increased rice biomass,with an increase of up to 40.0%,particularly in root biomass by up to 68.4%.Notably,the number of prominent rice individuals also increased,and their plasticity was enhanced following AMF inoculation.AMF led to an increase in the net photosynthetic rate and antioxidant enzyme activity of rice.In the AMF treatment group,the Cd concentration in the rice roots was significantly higher(19.1%‒68.0%)compared with that in the control group.Conversely,the Cd concentration in the rice seeds was lower in the AMF treatment group,indicating that AMF facilitated the sequestration of Cd in rice roots and reduced Cd accumulation in the seeds.Path coefficients varied across different treatments,suggesting that AMF inoculation reduced the direct impact of soil Cd concentration on the total Cd accumulation in seeds.The translocation of Cd was consistently associated with simultaneous growth dilution and compensatory accumulation as a result of mycorrhizal effects.Our study quantitatively analyzed this process through path analysis and clarified the causal relationship between rice growth and Cd transfer under the influence of AMF.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Generation of mitochondrial replacement monkeys by female pronucleus transfer

Mutations in mitochondrial DNA(mtDNA)are maternally inherited and have the potential to cause severe disorders.Mitochondrial replacement therapies,including spindle,polar body,and pronuclear transfers,are promising strategies for preventing the hereditary transmission of mtDNA diseases.While pronuclear transfer has been used to generate mitochondrial replacement mouse models and human embryos,its application in non-human primates has not been previously reported.In this study,we successfully generated four healthy cynomolgus monkeys(Macaca fascicularis)via female pronuclear transfer.These individuals all survived for more than two years and exhibited minimal mtDNA carryover(3.8%–6.7%),as well as relatively stable mtDNA heteroplasmy dynamics during development.The successful establishment of this nonhuman primate model highlights the considerable potential of pronuclear transfer in reducing the risk of inherited mtDNA diseases and provides a valuable preclinical research model for advancing mitochondrial replacement therapies in humans.

Effects of projectile parameters on the momentum transfer and projectile melting during hypervelocity impact

The effects of projectile/target impedance matching and projectile shape on energy,momentum transfer and projectile melting during collisions are investigated by numerical simulation.By comparing the computation results with the experimental results,the correctness of the calculation and the statistical method of momentum transfer coefficient is verified.Different shapes of aluminum,copper and heavy tungsten alloy projectiles striking aluminum,basalt,and pumice target for impacts up to 10 km/s are simulated.The influence mechanism of the shape of the projectile and projectile/target density on the momentum transfer was obtained.With an increase in projectile density and length-diameter ratio,the energy transfer time between the projectile and targets is prolonged.The projectile decelerates slowly,resulting in a larger cratering depth.The energy consumed by the projectile in the excavation stage increased,resulting in lower mass-velocity of ejecta and momentum transfer coefficient.The numerical simulation results demonstrated that for different projectile/target combinations,the higher the wave impedance of the projectile,the higher the initial phase transition velocity and the smaller the mass of phase transition.The results can provide theoretical guidance for kinetic impactor design and material selection.

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

The regulation of ferrocene-based catalysts on heat transfer in highpressure combustion of ammonium perchlorate/hydroxyl-terminated polybutadiene/aluminum composite propellants

The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.

Reliability analysis of slope stability by neural network,principal component analysis,and transfer learning techniques

The prediction of slope stability is considered as one of the critical concerns in geotechnical engineering.Conventional stochastic analysis with spatially variable slopes is time-consuming and highly computation-demanding.To assess the slope stability problems with a more desirable computational effort,many machine learning(ML)algorithms have been proposed.However,most ML-based techniques require that the training data must be in the same feature space and have the same distribution,and the model may need to be rebuilt when the spatial distribution changes.This paper presents a new ML-based algorithm,which combines the principal component analysis(PCA)-based neural network(NN)and transfer learning(TL)techniques(i.e.PCAeNNeTL)to conduct the stability analysis of slopes with different spatial distributions.The Monte Carlo coupled with finite element simulation is first conducted for data acquisition considering the spatial variability of cohesive strength or friction angle of soils from eight slopes with the same geometry.The PCA method is incorporated into the neural network algorithm(i.e.PCA-NN)to increase the computational efficiency by reducing the input variables.It is found that the PCA-NN algorithm performs well in improving the prediction of slope stability for a given slope in terms of the computational accuracy and computational effort when compared with the other two algorithms(i.e.NN and decision trees,DT).Furthermore,the PCAeNNeTL algorithm shows great potential in assessing the stability of slope even with fewer training data.

Transfer film effects induced by 3D-printed polyether-ether-ketone with excellent tribological properties for joint prosthesis

Based on the building principle of additive manufacturing,printing orientation mainly determines the tribological properties of joint prostheses.In this study,we created a polyether-ether-ketone(PEEK)joint prosthesis using fused filament fabrication and investigated the effects of printing orientation on its tribological properties using a pin-on-plate tribometer in 25% newborn calf serum.An ultrahigh molecular weight polyethylene transfer film is formed on the surface of PEEK due to the mechanical capture of wear debris by the 3D-printed groove morphology,which is significantly impacted by the printing orientation of PEEK.When the printing orientation was parallel to the sliding direction of friction,the number and size of the transfer film increased due to higher steady stress.This transfer film protected the matrix and reduced the friction coefficient and wear rate of friction pairs by 39.13%and 74.33%,respectively.Furthermore,our findings provide a novel perspective regarding the role of printing orientation in designing knee prostheses,facilitating its practical applications.

Mass Transfer-Promoted Fe^(2+)/Fe^(3+)Circulation Steered by 3D Flow-Through Co-Catalyst System Toward Sustainable Advanced Oxidation Processes

Realizing fast and continuous generation of reactive oxygen species(ROSs)via iron-based advanced oxidation processes(AOPs)is significant in the environmental and biological fields.However,current AOPs assisted by co-catalysts still suffer from the poor mass/electron transfer and non-durable promotion effect,giving rise to the sluggish Fe^(2+)/Fe^(3+)cycle and low dynamic concentration of Fe^(2+)for ROS production.Herein,we present a three-dimensional(3D)macroscale co-catalyst functionalized with molybdenum disulfide(MoS_(2))to achieve ultra-efficient Fe^(2+)regeneration(equilibrium Fe^(2+)ratio of 82.4%)and remarkable stability(more than 20 cycles)via a circulating flow-through process.Unlike the conventional batch-type reactor,experiments and computational fluid dynamics simulations demonstrate that the optimal utilization of the 3D active area under the flow-through mode,initiated by the convectionenhanced mass/charge transfer for Fe^(2+)reduction and then strengthened by MoS_(2)-induced flow rotation for sufficient reactant mixing,is crucial for oxidant activation and subsequent ROS generation.Strikingly,the flow-through co-catalytic system with superwetting capabilities can even tackle the intricate oily wastewater stabilized by different surfactants without the loss of pollutant degradation efficiency.Our findings highlight an innovative co-catalyst system design to expand the applicability of AOPs based technology,especially in large-scale complex wastewater treatment.

Application of a microscopic optical potential of chiral effective field theory in (p, d) transfer reactions

The microscopic global nucleon–nucleus optical model potential(OMP)proposed by Whitehead,Lim,and Holt,the WLH potential(Whitehead et al.,Phys Rev Lett 127:182502,2021),which was constructed in the framework of many-body per-turbation theory with state-of-the-art nuclear interactions from chiral effective field theory(EFT),was tested with(p,d)transfer reactions calculated using adiabatic wave approximation.The target nuclei included both stable and unstable nuclei,and the incident energies reached 200 MeV.The results were compared with experimental data and predictions using the phenomenological global optical potential of Koning and Delaroche,the KD02 potential.Overall,we found that the micro-scopic WLH potential described the(p,d)reaction angular distributions similarly to the phenomenological KD02 potential;however,the former was slightly better than the latter for radioactive targets.On average,the obtained spectroscopic factors(SFs)using both microscopic and phenomenological potentials were similar when the incident energies were below approxi-mately 120 MeV.However,their difference tended to increase at higher incident energies,which was particularly apparent for the doubly magic target nucleus 40Ca.

Embryo Transfer Strategies for Women with Recurrent Implantation Failure During the Frozen-thawed Embryo Transfer Cycles:Sequential Embryo Transfer or Double-blastocyst Transfer?

Objective Both sequential embryo transfer(SeET)and double-blastocyst transfer(DBT)can serve as embryo transfer strategies for women with recurrent implantation failure(RIF).This study aims to compare the effects of SeET and DBT on pregnancy outcomes.Methods Totally,261 frozen-thawed embryo transfer cycles of 243 RIF women were included in this multicenter retrospective analysis.According to different embryo quality and transfer strategies,they were divided into four groups:group A,good-quality SeET(GQ-SeET,n=38 cycles);group B,poor-quality or mixed-quality SeET(PQ/MQ-SeET,n=31 cycles);group C,good-quality DBT(GQ-DBT,n=121 cycles);and group D,poor-quality or mixed-quality DBT(PQ/MQ-DBT,n=71 cycles).The main outcome,clinical pregnancy rate,was compared,and the generalized estimating equation(GEE)model was used to correct potential confounders that might impact pregnancy outcomes.Results GQ-DBT achieved a significantly higher clinical pregnancy rate(aOR 2.588,95%CI 1.267–5.284,P=0.009)and live birth rate(aOR 3.082,95%CI 1.482–6.412,P=0.003)than PQ/MQ-DBT.Similarly,the clinical pregnancy rate was significantly higher in GQ-SeET than in PQ/MQ-SeET(aOR 4.047,95%CI 1.218–13.450,P=0.023).The pregnancy outcomes of GQ-SeET were not significantly different from those of GQ-DBT,and the same results were found between PQ/MQ-SeET and PQ/MQ-DBT.Conclusion SeET relative to DBT did not seem to improve pregnancy outcomes for RIF patients if the embryo quality was comparable between the two groups.Better clinical pregnancy outcomes could be obtained by transferring good-quality embryos,no matter whether in SeET or DBT.Embryo quality plays a more important role in pregnancy outcomes for RIF patients.

Tunneling Electrons Triggered Energy Transfer between Coherently Coupled Donor-Acceptor Molecules

Energy transfer is ubiquitous in natural and artificial lightharvesting systems,and coherent energy transfer,a highly efficient energy transfer process,has been accepted to play a vital role in such systems.However,the energy oscillation of coherent energy transfer is exceedingly difficult to capture because of its evanescence due to the interaction with a thermal environment.Here a microscopic quantum model is used to study the time evolution of electrons triggered energy transfer between coherently coupled donoracceptor molecules in scanning tunneling microscope(STM).A series of topics in the plasmonic nanocavity(PNC)coupled donor-acceptor molecules system are discussed,including resonant and nonresonant coherent energy transfer,dephasing assisted energy transfer,PNC coupling strength dependent energy transfer,Fano resonance of coherently coupled donor-acceptor molecules,and polariton-mediated energy transfer.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.