Effects of aligning pulse duration on the degree and the slope of nitrogen field-free alignment

Through theoretical analysis,we show how aligning pulse durations affect the degree and the time-rate slope of nitrogen field-free alignment at a fixed pulse intensity.It is found that both the degree and the slope first increase,then saturate,and finally decrease with the increasing pump duration.The optimal durations for the maximum degree and the maximum slope of the alignment are found to be different.Additionally,they are found to mainly depend on the molecular rotational period,and are affected by the temperature and the aligning pump intensities.The mechanism of molecular alignment is also discussed.

Carbon nanotube fibers with excellent mechanical and electrical properties by structural realigning and densification

Floating catalysis chemical vapor deposition(FCCVD)direct spinning process is an attractive method for fabrication of carbon nanotube fibers(CNTFs).However,the intrinsic structural defects,such as entanglement of the constituent carbon nanotubes(CNTs)and inter-tube gaps within the FCCVD CNTFs,hinder the enhancement of mechanical/electrical properties and the realization of practical applications of CNTFs.Therefore,achieving a comprehensive reassembly of CNTFs with both high alignment and dense packing is particularly crucial.Herein,an efficient reinforcing strategy for FCCVD CNTFs was developed,involving chlorosulfonic acid-assisted wet stretching for CNT realigning and mechanical rolling for densification.To reveal the intrinsic relationship between the microstructure and the mechanical/electrical properties of CNTFs,the microstructure evolution of the CNTFs was characterized by cross-sectional scanning electron microscopy(SEM),wide angle X-ray scattering(WAXS),polarized Raman spectroscopy and Brunauer–Emmett–Teller(BET)analysis.The results demonstrate that this strategy can improve the CNT alignment and eliminate the inter-tube voids in the CNTFs,which will lead to the decrease of mean distance between CNTs and increase of inter-tube contact area,resulting in the enhanced inter-tube van der Waals interactions.These microstructural evolutions are beneficial to the load transfer and electron transport between CNTs,and are the main cause of the significant enhancement of mechanical and electrical properties of the CNTFs.Specifically,the tensile strength,elastic modulus and electrical conductivity of the high-performance CNTFs are 7.67 GPa,230 GPa and 4.36×10^(6)S/m,respectively.It paves the way for further applications of CNTFs in high-end functional composites.

Coaxial Wet Spinning of Boron Nitride Nanosheet‑Based Composite Fibers with Enhanced Thermal Conductivity and Mechanical Strength

Hexagonal boron nitride nanosheets(BNNSs)exhibit remarkable thermal and dielectric properties.However,their self-assembly and alignment in macroscopic forms remain challenging due to the chemical inertness of boron nitride,thereby limiting their performance in applications such as thermal management.In this study,we present a coaxial wet spinning approach for the fabrication of BNNSs/polymer composite fibers with high nanosheet orientation.The composite fibers were prepared using a superacid-based solvent system and showed a layered structure comprising an aramid core and an aramid/BNNSs sheath.Notably,the coaxial fibers exhibited significantly higher BNNSs alignment compared to uniaxial aramid/BNNSs fibers,primarily due to the additional compressive forces exerted at the core-sheath interface during the hot drawing process.With a BNNSs loading of 60 wt%,the resulting coaxial fibers showed exceptional properties,including an ultrahigh Herman orientation parameter of 0.81,thermal conductivity of 17.2 W m^(-1)K^(-1),and tensile strength of 192.5 MPa.These results surpassed those of uniaxial fibers and previously reported BNNSs composite fibers,making them highly suitable for applications such as wearable thermal management textiles.Our findings present a promising strategy for fabricating high-performance composite fibers based on BNNSs.

多头自注意力机制的Faster R-CNN目标检测算法

文中提出一种融合多头注意力机制、ROIAlign和Soft-NMS的FasterR-CNN目标检测算法,旨在解决原始Faster R-CNN目标检测网络中存在的检测精度低、漏检、误检的问题。首先,为了提高Faster R-CNN的感知能力,提取特征图中的重要特征并降低对无关特征的提取,在网络中嵌入注意力机制;接着,针对共享全连接层的降维操作导致的一些区域的细节信息被忽略,造成局部信息的丢失,采用一维卷积代替共享全连接层实现权重计算的任务,以捕捉更广泛的空间信息;然后为了提供更丰富的特征表达能力,在注意力机制中引入多头机制分别对特征的不同部分进行重要性的加权;为了减少在特征提取时原图信息的丢失,使用ROI Align替换ROI Pooling算法;最后,在算法后处理中引入Soft-NMS替换传统非极大抑制(NMS)算法以减少漏检和误检情况。实验证明,改进后的Faster R-CNN目标检测网络对感兴趣目标的定位能力得到提高,漏检和误检情况减少,平均检测精度得到显著提升。

Highly Aligned Graphene Aerogels for Multifunctional Composites

Stemming from the unique in-plane honeycomb lattice structure and the sp^(2)hybridized carbon atoms bonded by exceptionally strong carbon–carbon bonds,graphene exhibits remarkable anisotropic electrical,mechanical,and thermal properties.To maximize the utilization of graphene’s in-plane properties,pre-constructed and aligned structures,such as oriented aerogels,films,and fibers,have been designed.The unique combination of aligned structure,high surface area,excellent electrical conductivity,mechanical stability,thermal conductivity,and porous nature of highly aligned graphene aerogels allows for tailored and enhanced performance in specific directions,enabling advancements in diverse fields.This review provides a comprehensive overview of recent advances in highly aligned graphene aerogels and their composites.It highlights the fabrication methods of aligned graphene aerogels and the optimization of alignment which can be estimated both qualitatively and quantitatively.The oriented scaffolds endow graphene aerogels and their composites with anisotropic properties,showing enhanced electrical,mechanical,and thermal properties along the alignment at the sacrifice of the perpendicular direction.This review showcases remarkable properties and applications of aligned graphene aerogels and their composites,such as their suitability for electronics,environmental applications,thermal management,and energy storage.Challenges and potential opportunities are proposed to offer new insights into prospects of this material.

A likely paleo-autotetraploidization event shaped the high conservation of Nyssaceae genome

Scientific knowledge about the ancestral genome of core eudicot plant kingdom can potentially have profound impacts on both basic and applied research,including evolution,genetics,genomics,ecology,agriculture,forestry,and global climate.To investigate which plant conserves best the core eudicots common ancestor genome,we compared Arcto-Tertiary relict Nyssaceae and 30 other eudicot plant families.The genomes of Davidia involucrata(a known living fossil),Camptotheca acuminata and Nyssa sinensis,one per existent genus of Nyssaceae,were performed comparative genomic analysis.We found that Nyssaceae originated from a single Nyssaceae common tetraploidization event(NCT)-autotetraploidization 28-31 Mya after the core eudicot common hexaploidization(ECH).We identified Nyssaceae orthologous and paralogous genes,determined its chromosomal evolutionary trajectory,and reconstructed the Nyssaceae most recent ancestor genome.D.involucrata genome contained the entire seven paleochromosomes and 17 ECH-generated eudicot common ancestor chromosomes and was the slowest in mutation among the analyzed 42 species of 31 plant families.Combing both its high retention of paleochromosomes and its low mutation rate,D.involucrata provides the best case in conservation of the core eudicot paleogenome.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

P-and SV-wave dispersion and attenuation in saturated microcracked porous rock with aligned penny-shaped fractures

P-and SV-wave dispersion and attenuation have been extensively investigated in saturated poroelastic media with aligned fractures.However,there are few existing models that incorporate the multiple wave attenuation mechanisms from the microscopic scale to the macroscopic scale.Hence,in this work,we developed a unified model to incorporate the wave attenuation mechanisms at different scales,which includes the microscopic squirt flow between the microcracks and pores,the mesoscopic wave-induced fluid flow between fractures and background(FB-WIFF),and the macroscopic Biot's global flow and elastic scattering(ES)from the fractures.Using Tang's modified Biot's theory and the mixed-boundary conditions,we derived the exact frequency-dependent solutions of the scattering problem for a single penny-shaped fracture with oblique incident P-and SV-waves.We then developed theoretical models for a set of aligned fractures and randomly oriented fractures using the Foldy approximation.The results indicated that microcrack squirt flow considerably influences the dispersion and attenuation of P-and SV-wave velocities.The coupling effects of microcrack squirt flow with the FB-WIFF and ES of fractures cause much higher velocity dispersion and attenuation for P waves than for SV waves.Randomly oriented fractures substantially reduce the attenuation caused by the FB-WIFF and ES,particularly for the ES attenuation of SV waves.Through a comparison with existing models in the limiting cases and previous experimental measurements,we validated our model.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion

With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

通智测试——基于动态具身物理社会交互环境的通用人工智能测试

The release of the generative pre-trained transformer(GPT)series has brought artificial general intelligence(AGI)to the forefront of the artificial intelligence(AI)field once again.However,the questions of how to define and evaluate AGI remain unclear.This perspective article proposes that the evaluation of AGI should be rooted in dynamic embodied physical and social interactions(DEPSI).More specifically,we propose five critical characteristics to be considered as AGI benchmarks and suggest the Tong test as an AGI evaluation system.The Tong test describes a value-and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI,allowing for infinite task generation.We contrast the Tong test with classical AI testing systems in terms of various aspects and propose a systematic evaluation system to promote standardized,quantitative,and objective benchmarks and evaluation of AGI.

Transforming Adolescent Smiles:Correcting Skeletal Mandibular Retrusion and Bimaxillary Protrusion with Clear Aligners

Maxillary protrusion combined with mandibular retraction is a highly prevalent but extremely complex maxillofacial deformity that can have a serious negative impact on patients’facial aesthetics and mental health.The traditional orthodontic treatment strategy often involves extracting 4 first premolars and conventional fixed techniques,combined with mini-implant screws,to retract the anterior teeth and improve facial protrusion.In recent years,an invisible orthodontic technique,without brackets,has become increasingly popular.However,while an invisible aligner has been used in some cases with reasonable results,there remain significant challenges in achieving a perfect outcome.This case report presents an adolescent patient with bimaxillary protrusion and mandibular retrognathia.Based on the characteristics of the invisible aligners and the growth characteristics of the adolescent’s teeth and jawbone,we designed precise three-dimensional tooth movement and corresponding resistance/over-correction for each tooth,while utilizing the patient’s jawbone growth potential to promote rapid development of the mandible,accurately and efficiently correcting bimaxillary protrusion and skeletal mandibular retrognathia.The patient’s facial aesthetics,especially the lateral morphology,have been greatly improved,and various aesthetic indicators have also shown significant changes,and to the patient’s great benefit,invasive mini-implant screws were not used during the treatment.This case highlights the advantages of using invisible aligners in adolescent maxillary protrusion combined with mandibular retraction patients.Furthermore,comprehensive and accurate design combined with good application of growth potential can also enable invisible orthodontic technology to achieve perfect treatment effects in tooth extractions,providing clinical guidance for orthodontists.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection meth-ods,which are caused by the characteristics of many kinds,complex shapes,and different scales of strip surface defects,a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed.Firstly,the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module,and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect.To improve the attention of the network to defects in the image,a CBAM-BiFPN network module is designed,and then the backbone network is combined with the CBAM-BiFPN network to realize the de-tection and fusion of multi-scale features.The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect loca-tion.Finally,Soft NMS is used to achieve non-maximum suppression and remove redundant boxes.In the comparative experiment on the NEU-DET dataset,the improved algorithm improves the mean average precision by 4.2%compared with the Faster R-CNN algorithm,and also improves the average precision by 6.1%and 6.7%for crazing defect and rolled-in scale defect,which are difficult to detect with the Faster R-CNN algorithm.The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.

Unicompartmental knee arthroplasty vs high tibial osteotomy for knee osteoarthritis:A comparison of clinical and radiological outcomes

BACKGROUND Unicompartmental knee arthroplasty(UKA)and high tibial osteotomy(HTO)are well-established operative interventions in the treatment of knee osteoarthritis.However,which intervention is more beneficial to patients with knee osteoarthritis remains unknown and a topic of much debate.Simultaneously,there is a paucity of research assessing the relationship between radiographic parameters of knee joint alignment and patient-reported clinical outcomes,preoperatively and following HTO or UKA.AIM To compare UKAs and HTOs as interventions for medial-compartment knee osteoarthritis:Examining differences in clinical outcome and investigating the relationship of joint alignment with respect to this.METHODS This longitudinal observational study assessed a total of 42 patients that had undergone UKA(n=23)and HTO(n=19)to treat medial compartment knee osteoarthritis.Patient-reported outcome measures(PROMs)were collected to evaluate clinical outcome.These included two disease-specific(Knee Injury and Osteoarthritis Outcome Score,Oxford Knee Score)and two generic(EQ-5D-5L,Short Form-12)PROMs.The radiographic parameters of knee alignment assessed were the:Hip-knee-ankle angle,mechanical axis deviation and angle of Mikulicz line.RESULTS Statistical analyses demonstrated significant(P<0.001),preoperative to postoperative,improvements in the PROM scores of both groups.There were,however,no significant inter-group differences in the postoperative PROM scores of the UKA and HTO group.Several significant correlations associated a more distolaterally angled Mikulicz line with worse knee function and overall health preoperatively(P<0.05).Postoperatively,two clusters of significant correlations were observed between the disease-specific PROM scores and knee joint alignment parameters(hip-knee-ankle angle,mechanical axis deviation)within the HTO group;yet no such associations were observed within the UKA group.CONCLUSION UKAs and HTOs are both efficacious operations that provide a comparable degree of clinical benefit to patients with medial compartment knee osteoarthritis.Clinical outcome has a limited association with radiographic parameters of knee joint alignment postoperatively;however,a more distolaterally angled Mikulicz line appears associated with worse knee function/health-related quality of life preoperatively.

Detection of Water Meter Digits Based on Improved Faster R-CNN

In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was proposed. The improved Faster-RCNN algorithm uses ResNet50 combined with FPN (Feature Pyramid Network) structure instead of the original ResNet50 as the feature extraction network, which can enhance the accuracy of the model for small-sized digit recognition;the use of ROI Align instead of ROI Pooling can eliminate the error caused by the quantization process of the ROI Pooling twice, so that the candidate region is more accurately mapped to the feature map, and the accuracy of the model is further enhanced. The experiment proves that the improved Faster-RCNN algorithm can reach 91.8% recognition accuracy on the test set of homemade dataset, which meets the accuracy requirements of automatic meter reading technology for water meter digital recognition, which is of great significance for solving the problem of automatic meter reading of mechanical water meters and promoting the intelligent development of water meters.

A holistic approach to aligning geospatial data with multidimensional similarity measuring

Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching methods.However,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD alignment.To solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)simultaneously.Spatial,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval voting.The presented approach is validated with real geographical semantic webs,Geonames and OpenStreetMap.Compared with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric aggregation.It reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.

Aligning Crystal Orientation of Poly-Crystalline Bi-Te With a High Magnetic Field

We investigated the effect of aligning crystal orientation in the microstructures containing sub micro-sized grains on the thermoelectric properties for polycrystalline Bi-Te materials.Bi-Te powder,prepared through the conventional pulverization process,was sufficiently dispersed in an appropriate solvent,and then was formed into c-axis aligned green bodies under a designated high magnetic field.The green bodies were sintered with spark-plasma-sintering machine.The degree of crystal alignment of sintered bodies was examined with the electron-back-scatter-diffraction SEM and the X-ray diffraction patterns.It was observed that for both p and n type thermoelectric Bi-Te materials,aligning crystal orientation properly made electrical resistivity decreased with keeping Seebeck coefficient and thermal conductivity remained unchanged.As a typical result,the aligned Bi-Te material with the magnetic field of 10 tesla showed 30%enhancement of the thermoelectric performance.