Mechanical Performance of Bio-inspired Bidirectional Corrugated Sandwich Pressure Shell Under External Hydrostatic Pressure

This paper aims to enhance the compression capacity of underwater cylindrical shells by adopting the corrugated sandwich structure of cuttlebone.The cuttlebone suffers uniaxial external compression,while underwater cylindrical shells are in a biaxial compressive stress state.To suit the biaxial compressive stress state,a novel bidirectional corrugated sandwich structure is proposed to improve the bearing capacity of cylindrical shells.The static and buckling analysis for the sandwich shell and the unstiffened cylindrical shell with the same volume-weight ratio are studied by numerical simulation.It is indicated that the proposed sandwich shell can effectively reduce the ratio between circumferential and axial stress from 2 to 1.25 and improve the critical buckling load by about 1.63 times.Numerical simulation shows that optimizing and adjusting the structural parameters could significantly improve the advantage of the sandwich shell.Then,the hydrostatic pressure tests for shell models fabricated by 3D printing are carried out.According to the experimental results,the overall failure position of the sandwich shell is at the center part of the sandwich shell.It has been found the average critical load of the proposed sandwich shell models exceeds two times that of the unstiffened shell models.Hence,the proposed bio-inspired bidirectional corrugated sandwich structure can significantly enhance the pressure resistance capability of cylindrical shells.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

Bidirectional rectifier with gate voltage control based on Bi_(2)O_(2)Se/WSe_(2)heterojunction

Two-dimensional(2D)WSe_(2)has received increasing attention due to its unique optical properties and bipolar behavior.Several WSe_(2)-based heterojunctions exhibit bidirectional rectification characteristics,but most devices have a lower rectification ratio.In this work,the Bi_(2)O_(2)Se/WSe_(2)heterojunction prepared by us has a typeⅡband alignment,which can vastly suppress the channel current through the interface barrier so that the Bi_(2)O_(2)Se/WSe_(2)heterojunction device has a large rectification ratio of about 10^(5).Meanwhile,under different gate voltage modulation,the current on/off ratio of the device changes by nearly five orders of magnitude,and the maximum current on/off ratio is expected to be achieved 106.The photocurrent measurement reveals the behavior of recombination and space charge confinement,further verifying the bidirectional rectification behavior of heterojunctions,and it also exhibits excellent performance in light response.In the future,Bi_(2)O_(2)Se/WSe_(2)heterojunction field-effect transistors have great potential to reduce the volume of integrated circuits as a bidirectional controlled switching device.

Bidirectional visible light absorber based on nanodisk arrays

A perfect bidirectional broadband visible light absorber composed of titanium nitride and tungsten nanodisk arrays is proposed.The average absorption of the absorber exceeds 89%at 400 nm–800 nm when light is normally incident on the front-side.Illumination from the opposite direction(back-side)results in absorption of more than 75%.Through the theoretical analysis of the electric and magnetic fields,the physical mechanism of the broadband perfect absorption is attributed to the synergy of localized surface plasmons,propagating surface plasmons,and plasmonic resonant cavity modes.Furthermore,the absorber also exhibits excellent polarization-independence performance and a high angular tolerance of~30°for both front-and back-side incidence.The designed bidirectional broadband visible light absorber here has wide application prospects in the fields of solar cells and ink-free printing.

Demulsification Behavior, Characteristics, and Performance of Surfactant Stabilized Oil-in-Water Emulsion under Bidirectional Pulsed Electric Field

As a novel electric demulsification method,bidirectional pulsed electric field(BPEF)was employed to demulsify the surfactant stabilized oil-in-water(SSO/W)emulsion for oil/water separation in this work.The demulsification behavior,characteristics,and stages under BPEF were explored.It was discovered that BPEF drove SSO/W emulsion to move and form vortexes,during which the oil droplets aggregated and accumulated to generate an oil droplet layer(ODL).ODL subsequently transformed into a continuous oil layer(COL)leading to the demulsification and separation of SSO/W emulsion.The conversion rate of ODL to COL was defined and used to evaluate the demulsification process and reflect the coalescence ability and transformation efficiency of dispersed oil droplets into COL.Furthermore,the effects of BPEF voltage,frequency,duty cycle,ratio of pulse output time,and surfactant type and content on the demulsification performance were examined.The optimal values of BPEF parameters for demulsification operation were 400 V,25 Hz,50%,and 4:1.O/W emulsion containing anionic surfactant was apt to be demulsified by BPEF,nonionic surfactant took the second place and cationic surfactant was the most difficult.A high surfactant content was not conducive to the BPEF demulsification.This work is anticipated to provide useful guidance for oil/water separation and oil recovery from actual emulsified oily wastewater by BPEF.

Magmatism-Related Thermal Simulation of Volcanic Arcs in the Molucca Sea Bidirectional Subduction System

The bidirectional subduction system,island arc magmatic activities,and thermal structure of the forearc basin in the Molucca Sea are taken into consideration in this study.The active volcanic arcs on both sides of the bidirectional subduction zone in the Molucca Sea are undergoing arc-arc collisions.We applied a finite element thermal simulation method to reconstruct the thermal evolution history of the Molucca Sea Plate based on geophysical data.Then,we analyzed the thermodynamic characteristics of island arc volcanism on both sides of the bidirectional subduction zone.The results showed that at 10Myr,the oceanic ridge of the Molucca Sea Plate was asymmetrically biased to the west,causing this bidirectional subduction to be deeper in the west than in the east.Furthermore,the oceanic ridge subducted under the Sangihe arc at 5.5Myr,causing intermittent cessation of volcanic activities.Due to the convergence of bidirectional subduction,the geothermal gradient in the top 3km depth of the forearc area between the Sangihe and Halmahera arcs decreased from about 60℃km^(−1) at 4Myr to about 38℃km^(−1) today.Finally,within the 45–100 km depth range of the sliding surface of the subduction,anomalously high-temperature zones formed due to shear friction during the bidirectional subduction.

3D Model Encryption Algorithm by Parallel Bidirectional Diffusion and 1D Map with Sin and Logistic Coupling

3D models are essential in virtual reality,game development,architecture design,engineering drawing,medicine,and more.Compared to digital images,3D models can provide more realistic visual effects.In recent years,significant progress has been made in the field of digital image encryption,and researchers have developed new algorithms that are more secure and efficient.However,there needs to be more research on 3D model encryption.This paper proposes a new 3D model encryption algorithm,called the 1D map with sin and logistic coupling(1D-MWSLC),because existing digital image encryption algorithms cannot be directly applied to 3D models.Firstly,this paper introduce 1D-MWSLC,which has a wider range of parameters compared to traditional 1D chaotic systems.When the parameter exceeds a specific range,the chaotic phenomenon does not weaken.Additionally,1D-MWSLC has two control parameters,which increases the cryptosystem’s parameter space.Next,1D-MWSLC generates keystreams for confusion and diffusion.In the confusion stage,this paper use random confusion,and the keystream generates an index matrix that confuses the integer and decimal parts of the 3D model simultaneously.In the diffusion stage,this paper use parallel bidirectional diffusion to simultaneously diffuse the integer parts of the three coordinates of the 3D model.Finally,this paper verify the proposed algorithm through statistical analysis,and experimental results demonstrate that the proposed 3D model encryption algorithm has robust security.

Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According to the literature,social networks and particularly Twitter are effective platforms for gathering public opinions.Moreover,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including healthcare.The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus.The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022.The tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors.The authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine classifier.This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT.The deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.

Exploration on the mechanism of therapeutic and toxic bidirectional effects of Haizao Yuhu decoction based on machine learning and data mining

Objective:To explore the bidirectional mechanism of Haizao Yuhu decoction(HYD)on goiter and drug-induced liver injury(DILI)based on machine learning and data mining.Methods:Firstly,compounds of HYD were selected from the TCMSP,TCMIP,and BATMAN databases,then the TCMSP was used to acquire the targets of compounds.Targets of goiter and DILI were obtained from the GeneCards database.Secondly,common targets of“HYD-goiter”and“HYD-DILI”as well as related compounds were used to construct the networks and perform Random Walk with Restart(RWR)algorithm and network stability test.Finally,core targets in the“HYD-goiter”and“HYD-DILI”networks were used for molecular docking with core compounds and searched for validation on PubChem,and the relevant experimental data of our group were quoted to verify the analysis results.Results:There were 22 intersection targets of HYD and DILI,326 of HYD and goiter.RWR analysis showed that MAPK1,MAPK3,AKT1,etc.may be the core targets of HYD treating goiter,RELA,TNF,IL4,etc.may be the core targets of the bidirectional effect,and eckol may be the core compound in bidirectional effect.Network stability test indicated that the HYD had a high stability on treating goiter and playing a bidirectional effect.The core targets and core compounds docked well,and 37.3%of targets had been confirmed by experiments and 29.8%core targets had been confirmed.Our previous experimental result confirmed that the HYD could treat goiter usefully by reducing the expression levels of PI3K and AKT mRNA,and down-regulating the expression of Cyclin D1 and Bcl-2 mRNA.Conclusion:HYD containing“sargassum-liquorice”combination may have a bidirectional effect on treating goiter and causing DILI.We offered a new way for more explorations on the therapeutic and toxic bidirectional mechanisms based on machine learning and data mining.

“人类智能”与“人工智能”双向耦合:智能时代的教育取向

以大数据、人工智能、虚拟现实为核心的智能技术对教育发展影响巨大而深远。人工智能在辅助师生在教育认知、教育思维、深度学习等方面发挥着积极的“亲人类”“为人类”“利人类”功用,促进了教育全方位的高阶发展。然而,面对日新月异的人工智能发展态势,人类必须最大限度地规避风险,“拥抱机遇、趋利避害”,努力地选择它、使用它、导航它、超越它。而超越“类人性”的人工智能,教育应当自觉增强人类的主体地位,强化“人性本真”的“自然智能”,强调人类自然智能的自觉性、道德性、主体性和意志性。人类智能不是对人工智能的抵制和防御,而是要通过积极的努力,达成人类智能与人工智能高度契合、相得益彰、双向建构、耦合共生的格局,促进教育发展在人性与智能之间构建人机共强的良好生态。

一种可实现双向故障闭锁的双极Y型模块化多电平直流变换器

直流电网互联通常采用隔离型高压大功率直流变换器,但其存在体积大、成本高及传输效率较低等问题。该文提出一种双极Y型模块化多电平DC/DC变换器,其避免使用中间变压器,实现直流功率双向传输,并且可以有效闭锁双向直流故障。首先分析了变换器拓扑结构及故障闭锁工作原理,并根据桥臂内电势等效原理建立了数学模型。基于变换器臂间、相间能量平衡约束,在闭环控制的基础上引入桥臂电流补偿控制,提升变换器暂态性能。最后,在Matlab/Simulink搭建了采用模块化多电平DC/DC变换器(modularmultilevelDC/DCconverter,DC-MMC)的直流输电系统仿真模型,通过对端口电流、桥臂电流及桥臂电容电压等动态指标的分析,验证了所提DC-MMC双向功率传输控制及双向故障闭锁能力的可行性和有效性。

多模态特征的越南语语音识别文本标点恢复

越南语语音识别系统输出的文本序列缺少标点符号,恢复识别文本标点有助于消除歧义,更易于阅读和理解。越南语语音识别文本中常出现破坏语义的错误音节,基于文本模态的标点恢复模型在识别带噪文本时存在标点预测不准确的问题。利用越南语语音中的语气停顿及声调变化指导模型对带噪文本作出正确的标点预测,提出多模态特征的越南语语音识别文本标点恢复方法,利用梅尔倒谱系数(MFCC)提取语音特征,利用预训练语言模型提取文本上下文特征,基于标签注意力机制实现语音与文本多模态特征融合,增强模型对越南语带噪文本上下文信息的学习能力。实验结果表明,相较于基于Transformer和BERT提取文本单一模态特征的标点恢复模型,所提方法在越南语数据集上精确率、召回率和F1值均至少提高10个百分点,验证了融合语音与文本特征对提升越南语语音识别带噪文本标点预测精确率的有效性。

基于二次分解双向门控单元新型电力系统超短期负荷预测

在新型电力系统中,电力负荷随机性和波动性较强,现有预测方法难以对其实现高精度预测。为此,提出一种基于二次分解和双向门控循环单元的超短期负荷预测模型。首先,针对电力负荷的强随机性和强波动性,利用自适应噪声完备经验模态分解对电力负荷历史序列进行初步分解,使负荷序列更加平稳。随后,对初步分解得到的强非平稳分量运用连续变分模态分解进行二次分解,降低其预测难度。最后,为充分学习电力负荷的时序特征,在预测过程构建基于双向门控循环单元的超短期电力负荷预测模型。实验结果表明,该模型相较于现有优秀预测模型有更高的预测精度。

一维管网与二维地表双向耦合的城市暴雨内涝模拟

为快速、准确模拟城市暴雨内涝演化过程,提出一种排水管网与上覆地表动态水力交互方法,构建了一维管网模型(SWMM)与二维水动力(LISFLOOD-FP)双向耦合的模型,解决了一维管网和二维地表的双向流量交换和时间同步难题。以上海外高桥地区为例,采用两次短历时降雨过程对耦合模型进行校准和验证,比较分析了单向、双向耦合的淹没范围与水深变化。结果表明:双向耦合模拟精度较高,在研究区具有良好的适用性;对于占比80%以上的轻度(<0.2m)积水区,单向、双向耦合的模拟积水面积比为1.21;对于中等(0.2~0.3 m)和重度(>0.3 m)积水区,单向耦合模拟结果趋于严重,该比值分别增至1.88和2.1。所构建的双向耦合模型能够揭示城区内涝积水、扩散及消退的全过程,可用于城市暴雨内涝推演,为内涝治理和灾害防御提供科学依据。

基于YOLOv5s和超声图像的儿童肠套叠特征检测模型

为帮助医生快速寻找到儿童腹部超声中肠套叠的病变特征并实现肠套叠超声诊后数据的快速质检,文章将目标检测算法应用于儿童腹部超声图像检测肠套叠“同心圆”征.首先探索了基于YOLOv5s的儿童肠套叠检测模型,发现该模型检测肠套叠“同心圆”征的精确度、召回率、F 1分数、mAP@0.5、FPS以及参数量等方面均优于Faster RCNN.进一步,为解决肉眼难以观察的“同心圆”征的检测问题,使用双向特征金字塔网络,并将注意力机制加入YOLOv5s网络,形成基于YOLOv5s_BiFPN_SE框架的儿童肠套叠“同心圆”征检测模型.该模型检测的精确率、召回率、F 1分数、mAP@0.5分别达到了91.33%、90.73%、91.03%、88.77%,性能更优于YOLOv5s.

基于双向稀疏Transformer的多变量时序分类模型

针对多变量时序(Multivariate Time Series,MTS)分类中长序列数据难以捕捉时序特征的问题,提出一种基于双向稀疏Transformer的时序分类模型BST(Bidirectional Sparse Transformer),提高了MTS分类任务的准确度.BST模型使用Transformer框架,构建了一种基于活跃度得分的双向稀疏注意力机制.基于KL散度构建活跃度评价函数,并将评价函数的非对称问题转变为对称权重问题.据此,对原有查询矩阵、键值矩阵进行双向稀疏化,从而降低原Transformer模型中自注意力机制运算的时间复杂度.实验结果显示,BST模型在9个长序列数据集上取得最高平均排名,在临界差异图中领先第2名35.7%,对于具有强时序性的乙醇浓度数据集(Ethanol Concentration,EC),分类准确率提高30.9%.

1LF-5型液压翻转犁的设计与试验

为推动玉米秸秆还田保护性耕作农艺的发展,设计了一种集土壤耕作技术、玉米秸秆还田技术于一体的1LF-5型液压翻转犁,一次作业既翻转了土层、松碎了土壤,又可将土壤表层的玉米秸秆与作物残茬翻入土层下,形成隔层进行还田;同时,犁体受到突发外力冲击时,安全装置自动生效,使犁柱绕固定轴栓旋转,从而保护犁体与犁柱的安全。田间试验表明:1LF-5型液压翻转犁的平均耕作深度稳定为33.45cm,平整度为20mm,变异系数在3.166%~3.535%之间;在第1工况下,翻转犁的耕宽平均值为220.75cm,耕宽的变异系数范围为1.21%~1.71%;在第2工况下,翻转犁的耕宽平均值170.1cm,耕作宽度的变异系数范围为1.26%~1.68%;植被覆盖率平均值为89.97%,土垡破碎率为72.27%。机具满足国家标准GB/T14225对于耕作质量的各项要求,也满足翻耕农艺要求。