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.

Flexible bidirectional pulse charging regulation achieving long-life lithium-ion batteries

Typical application scenarios,such as vehicle to grid(V2G)and frequency regulation,have imposed significant long-life demands on lithium-ion batteries.Herein,we propose an advanced battery life-extension method employing bidirectional pulse charging(BPC)strategy.Unlike traditional constant current charging methods,BPC strategy not only achieves comparable charging speeds but also facilitates V2G frequency regulation simultaneously.It significantly enhances battery cycle ampere-hour throughput and demonstrates remarkable life extension capabilities.For this interesting conclusion,adopting model identification and postmortem characterization to reveal the life regulation mechanism of BPC:it mitigates battery capacity loss attributed to loss of lithium-ion inventory(LLI)in graphite anodes by intermittently regulating the overall battery voltage and anode potential using a negative charging current.Then,from the perspective of internal side reaction,the life extension mechanism is further revealed as inhibition of solid electrolyte interphase(SEI)and lithium dendrite growth by regulating voltage with a bidirectional pulse current,and a semi-empirical life degradation model combining SEI and lithium dendrite growth is developed for BPC scenarios health management,the model parameters are identified by genetic algorithm with the life simulation exhibiting an accuracy exceeding 99%.This finding indicates that under typical rate conditions,adaptable BPC strategies can extend the service life of LFP battery by approximately 123%.Consequently,the developed advanced BPC strategy offers innovative perspectives and insights for the development of long-life battery applications in the future.

Dissecting Causal Relationships Between Plasma Metabolites and Osteoporosis:A Bidirectional Mendelian Randomization Study

Objective To investigate the causal relationships between plasma metabolites and osteoporosis via Mendelian randomization(MR)analysis.Methods Bidirectional MR was used to analyze pooled data from different genome-wide association studies(GWAS)to investigate the causal relationships between plasma metabolites and osteoporosis.The causal effect of plasma metabolites on osteoporosis was estimated using the inverse variance weighted method,intersections of statistically significant metabolites obtained from different sources of osteoporosis-related GWAS aggregated data was determined,and then sensitivity analysis was performed on these metabolites.Heterogeneity between single nucleotide polymorphisms was evaluated by Cochran’s Q test.Horizontal pleiotropy was assessed through the application of the MR-Egger intercept method and the MR-PRESSO method.The causal effect of osteoporosis on plasma metabolites was also evaluated using the inverse variance weighted method.Additionally,pathway analysis was conducted to identify potential metabolic pathways involved in the regulation of osteoporosis.Results After primary analysis and a series of sensitivity analyses,77 and 61 plasma metabolites were identified as having a causal relationship with osteoporosis from the GWAS data in the GCST90038656 and GCST90044600 datasets,respectively.Five common metabolites were identified via intersection.X-13684 levels(GCST90038656:OR=0.999,95%CI,0.998-1.000,P=0.004;GCST90044600(OR=0.834,95%CI,0.700-0.993,P=0.042),and the glucose-to-maltose ratio(GCST90038656:OR=0.998,95%CI,0.997-1.000,P=0.025;GCST90044600:OR=0.752,95%CI,0.576-0.981,P=0.036)were negatively associated with osteoporosis,whereas glycoursodeoxycholate levels(GCST90038656:OR=1.002,95%CI,1.000-1.003,P=0.032;GCST90044600:OR=1.331,95%CI,1.036-1.709,P=0.025)and arachidoylcarnitine(C20)levels(GCST90038656:OR=1.001,95%CI,1.000-1.003,P=0.039;GCST90044600:OR=1.237;95%CI,1.008-1.518,P=0.042)were positively associated with osteoporosis.The relationship between X-11299 levels and osteoporosis showed contradictory results(GCST90038656:OR=0.998,95%CI,0.997-1.000,P=0.026;GCST90044600:OR=1.402,95%CI,1.071-1.834,P=0.014).Pathway analysis indicated that glycine,serine,and threonine metabolism,valine,leucine,and isoleucine biosynthesis,galactose metabolism,arginine biosynthesis,and starch and sucrose metabolism pathways were participated in the development of osteoporosis.Conclusion We found a causal relationship between plasma metabolites and osteoporosis.These results offer novel perspectives that have implications for targeted interventions focused on metabolites in the management of osteoporosis.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

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.

Bidirectional relationship between diabetes mellitus and depression:Mechanisms and epidemiology

Diabetes mellitus and depression exhibit a complex bidirectional relationship that profoundly impacts patient health and quality of life.This review explores the physiological mechanisms,including inflammation,oxidative stress,and neu-roendocrine dysregulation,that link these conditions.Psychosocial factors such as social support and lifestyle choices also contribute significantly.Epidemiological insights reveal a higher prevalence of depression among diabetics and an in-creased risk of diabetes in depressed individuals,influenced by demographic variables.Integrated management strategies combining mental health asse-ssments and personalized treatments are essential.Future research should focus on longitudinal and multi-omics studies to deepen understanding and improve therapeutic outcomes.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

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.

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.

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.

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.

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.

Gain and Noise Properties of Bidirectional EDFAs

The rate equation model is setup for the signal gain, pump absorption and output noise spectrum of bidirectional EDFA (Bi EDFA) including numbers of signals, pumps of arbitrary direction, amplified spontaneous emission (ASE) and inherent loss. The influence of erbium doped fiber length, input signal power, pump style and pump power on the gain characteristics of Bi EDFA is analyzed. Forward and backward noise figure for different pump style versus bidirectional input signal power is investigated.

Seismic performance of double- skin steel- concrete composite box piers: Part Ⅰ——Bidirectional quasi-static testing

To study the seismic performance of double-skin steelconcrete composite box（ DSCB） piers, a total of 11 DSCB pier specimens were tested under bidirectional cyclic loading. The effects of the loading pattern, the steel plate thickness, the axial load ratio, the slenderness ratio and the aspect ratio were taken into consideration. The damage evolution process and failure modes of the tested specimens are presented in detail. Test results are also discussed in terms of the hysteretic curve, skeleton curve, ductility and energy dissipation capacity of DSCB pier specimens. It can be concluded that the hysteretic performance of DSCB piers in one direction is affected and weakened by the cyclic loading in the other direction. DSCB piers under bidirectional cyclic loading exhibit good performance in terms of load carrying capacity, ductility, and energy dissipation capacity. Overall, DSCB piers can meet the basic aseismic requirements. The research results can be taken as a reference for using DSCB piers as high piers in bridges in strong earthquakeprone areas.

BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE

The multiple classifier system （MCS）, composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory （QLBAM） to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate ＂overproduce and choose＂ strategy or ＂thinning＂ algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability.