MECHANICAL PERTURBATION INDUCED MOLECULAR ALIGNMENTS IN A SIDE-CHAIN LIQUID CRYSTALLINE POLYACETYLENE, POLY{10-[4-(4'-METHOXYPHENOXY-CARBOML)PHENOXYCARBONYL]-1-DECYNE}

A new liquid crystalline polyacetylene containing a phenyl benzoate mesogen (5) is synthesized,whose mesomorphic properties are found to be easily 'tunab1e' by simple mechanical perturbation. Thepolymerization of 10- [ 4 - (4' -methoxyphenoxycarbonyl )phenoxycarbonyl] - 1 -decyne (4 ) in itiated by theWCl_6-Ph_4Sn/dioxane complex yields polymer 5 with a M_w of 28400. The molecular structure of 5 ischaracterized by NMR, IR, and UV spectroscopy and its liquid crystalline behavior is investigated by DSC,POM, and XRD analysis. Upon mechanical perturbation, 5 exhibits unusual agitation-induced high-strengthdisclinations, shear-induced inversion walls, and solidification-induced banded textures. Such phenomenahave been observed in the main-chain liquid crystalline polymers with rigid backbones, but have seldom beenreported for the side-chain liquid crystalline polymers with flexible backbones, suggesting that the rigidpolyacetylene backbone of 5 plays a constructive role in inducing the novel molecular alignments.

Translational,vibrational,rotational enhancements and alignments of reactions H + ClF(v = 0-5,j= 0,3,6,9) →HCl + F and HF + Cl,at E_(rel)= 0.5-20 kcal/mol

Quasi-classical trajectory calculations of the title reactions H ＋ C1F （v = 0-5, j = 0, 3,6, 9） -＋ HCl ＋ F and H ＋ C1F （v = 0-5, j =0, 3, 6, 9） → HF ＋ C1 at Erel = 0.5 kcal/mol-20 kcal/mol on ground potential energy surface DHTSN of 1 2AI [M. E Deskevich, M. Y. Hayes, K. Takahashi, R. T. Skodje and D. J. Nesbitt, J. Chem. Phys. 124, 224303 （2006）] are performed. Potential energy surfaces derived from DHTSN for the title reactions are obtained, and compared with that of DHTSN for the reaction F ＋ HC1 -＋ HF ＋ C1. Both potential energy surfaces have an early barrier pattern. Integral cross sections and alignments of product molecules HC1 and HF dependent on the internal energy states v and j of reactant molecule C1F are obtained and compared. Translational, vibrational, and rotational energy specific translational enhancements of the reactant molecule CIF of the title reactions are found. Reaction mechanisms of the title reactions according to the respective potential energy contours are further found and explained. Reasons of simultaneous translational and vibrational enhancements are clarified.

Beam alignments based on the spectrum decomposition of orbital angular momentums for acoustic-vortex communications

Given the enhanced channel capacity of wave chirality,acoustic communications based on the orbital angular momentum(OAM)of acoustic-vortex(AV)beams are of significant interest for underwater data transmissions.However,the stringent beam alignment is required for the coaxial arrangement of transceiver arrays to ensure the accuracy and reliability of OAM decoding.To avoid the required multiple measurements of the traditional orthogonality based algorithm,the beam alignment algorithm based on the OAM spectrum decomposition is proposed for AV communications by using simplified ring-arrays.Numerical studies of the single-OAM and OAM-multiplexed AV beams show that the error of the OAM spectrum increases with the translation distance and the deflection angle of the transceiver arrays.To achieve an ideal arrangement,two methods of the single-array translation alignment and the dual-array deflection alignment are developed based on the least standard deviation of the OAM spectrum(SD-OAM).By decreasing the SD-OAM towards zero using transceiver arrays of 16 transmitters and 16 receivers,accurate beam alignments are accomplished by multiple adjustments in three dimensions.The proposed method is also demonstrated by experimental measurements of the OAM dispersion and the SD-OAM for misaligned beams.The results demonstrate the feasibility of the rapid beam alignment based on the OAM spectrum decomposition by using simplified transceiver ring-arrays,and suggest more application potentials for acoustic communications.

ORPA:a fast and efficient phylogenetic analysis method for constructing genome-wide alignments of organelle genomes

Creating a multi-gene alignment matrix for phylogenetic analysis using organelle genomes involves aligning single-gene datasets manually,a process that can be time-consuming and prone to errors.The HomBlocks pipeline has been created to eliminate the inaccuracies arising from manual operations.The processing of a large number of sequences,however,remains a time-consuming task.To conquer this challenge,we develop a speedy and efficient method called Organelle Genomes for Phylogenetic Analysis(ORPA).ORPA can quickly generate multiple sequence alignments for whole-genome comparisons by parsing the result files of NCBI BLAST,completing the task just in 1 min.With increasing data volume,the efficiency of ORPA is even more pronounced,over 300 times faster than HomBlocks in aligning 60 high-plant chloroplast genomes.The phylogenetic tree outputs from ORPA are equivalent to HomBlocks,indicating its outstanding efficiency.Due to its speed and accuracy,ORPA can identify species-level evolutionary conflicts,providing valuable insights into evolutionary cognition.

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。

Alignments in the nobelium isotopes

Total-Routhian-Surface calculations have been performed to investigate the deformation and alignment properties of the No isotopes. It is found that normal deformed and superdeformed states in these nuclei can coexist at low excitation energies. In neutron-deficient No isotopes, the superdeformed shapes can even become the ground states. Moreover, we plotted the kinematic moments of inertia of the No isotopes, which follow very nicely available experimental data. It is noted that, as the rotational frequency increases, alignments develop at hω = 0.2- 0.3 MeV. Our calculations show that the occupation of the νj15/2 orbital plays an important role in the alignments of the No isotopes.

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.

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.

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.

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.

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.

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.

FASMA:A Service to Format and Analyze Sequences in Multiple Alignments

Multiple sequence alignments are successfully applied in many studies for understanding the structural and functional relations among single nucleic acids and protein sequences as well as whole families. Because of the rapid growth of sequence databases, multiple sequence alignments can often be very large and difficult to visualize and analyze. We offer a new service aimed to visualize and analyze the multiple alignments obtained with different external algorithms, with new features useful for the comparison of the aligned sequences as well as for the creation of a final image of the alignment. The service is named FASMA and is available at http： / /bioinformatica.isa.cnr.it /FASMA /.

The effect of hadronic scatterings on the measurement of vector meson spin alignments in heavy-ion collisions

Spin alignments of vector mesons and hyperons in relativistic heavy-ion collisions have been proposed as signals of global polarization.The STAR experiment first observed the ∧ polarization.Recently,the ALICE collaboration measured the transverse momentum {PT) and the collision centrality dependence of K*,and Φ spin alignments during Pb-Pb collisions at 1/2sNN=2.76 TeV.A large signal is observed in the low pT region of mid-central collisions for K*,while the signal is much smaller for Φ,and these have not been understood yet.Since vector mesons have different lifetimes and their decay products have different scattering cross sections,they suffer from different hadronic effects.In this paper,we study the effect of hadronic interactions on the spin alignment of K*,Φ,and p mesons in relativistic heavy-ion collisions with a multi-phase transport model.We find that hadronic scatterings lead to a deviation of the observed spin alignment matrix element p00 away from the true value for p and K*mesons(with a bigger effect on p) while the effect is negligible for the Φ meson.The effect depends on the kinematic acceptance:the observed p00 value is lower than the true value when the pseudorapidity(η) coverage is small,while there is little effect when the η coverage is large.Hence,this study provides valuable information to understand the vector meson spin alignment signals observed during the experiments.

基于改进Faster R-CNN的轴承表面缺陷检测

针对轴承表面缺陷小、几何形状多变以及低对比度的特点,提出了一种改进的Faster R-CNN算法,对轴承表面缺陷进行检测。首先,以ResNet-50结合特征金字塔网络对轴承表面缺陷进行特征提取;其次,在改进的特征提取网络中引入可变形卷积,通过卷积学习偏移量自适应调整感受野,提高了缺陷的提取能力;最后,针对ROI Pooling因二次量化而导致的区域不匹配问题,采用基于双线插值的ROI Align改进ROI Pooling。实验结果表明,在采集的轴承表面缺陷数据集上,改进的Faster R-CNN平均精度均值为97.6%,与改进前相比,提高了11.76%,可以实现对轴承表面各类缺陷更为准确的检测,具有较强的实用性。