Instrumented oscillographic study on impact toughness of an axle steel DZ2 with different tempering temperatures

Compared with the conventional Charpy impact test method,the oscillographic impact test can help in the behavioral analysis of materials during the fracture process.In this study,the trade-off relationship between the strength and toughness of a DZ2 axle steel at various tempering temperatures and the cause of the improvement in impact toughness was evaluated.The tempering process dramatically influenced carbide precipitation behavior,which resulted in different aspect ratios of carbides.Impact toughness improved along with the rise in tempering temperature mainly due to the increase in energy required in impact crack propagation.The characteristics of the impact crack propagation process were studied through a comprehensive analysis of stress distribution,oscilloscopic impact statistics,fracture morphology,and carbide morphology.The poor impact toughness of low-tempering-temperature specimens was attributed to the increased number of stress concentration points caused by carbide morphology in the small plastic zone during the propagation process,which resulted in a mixed distribution of brittle and ductile fractures on the fracture surface.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

The principles and guidelines for designing long-term agronomic experiments

Many of the important questions facing farming systems in the world today require long-term studies to provide meaningful information and answers. A long-term agronomic experiment (LTAE) should (1) have long-term objectives; (2) study important soil processes or ecological processes; and (3) be related to the productivity and sustainability of systems. A well established LTAE can provide both insights into how the system operates and foresight into where the system goes. The prerequisites for setting up a LTAE are the secured land, continuous funding and dedicated scientists. A number of principles must be considered carefully when establishing a LTAE, (1) the site must be representative of large areas; (2) the treatments should be simple, but focusing on the big questions; (3) the plots should be large enough to allow subsequent modification of the experiment if this becomes necessary; (4) crop rotations should minimise, wherever possible, the risk of build-up of pests and diseases, and rotational phase should be considered in a rotational experiment; (5) a clearly defined experimental protocol should be developed to ensure data collected is scientifically valid and statistically analysable, but with flexibility to allow essential changes; (6) soil samples, possibly plant samples, should be achieved to provide better answer to the original questions when new, perhaps more accurate analytical techniques are developed, or answer new research questions that were not considered in the original design. The MASTER experiment in Australia was used as a case study to demonstrate how these principles are implemented in practice.

The Pros and Cons of Recite Strategies in English Teaching

Recitation is the basis of second language acquisition theory according to well-known American linguist Krashen[1].Recitation has a very long history in China's traditional culture and education,it’s one of the most important way of learning mother tongue and also plays a pivotal role in the English teaching.However,some scholars have found that the input of recitation focus only on formal、shallow processing and mechanical memory,but it can’t achieve the discourse of representation and enhanced language learning affection in the long-term memory.The author of this paper,talking about the pros and cons of recite strategy from multi-angle,analysis the conducive methods of recitation in English teaching of our Chinese-style high school.

Expression signatures of long non-coding RNA and mRNA in human traumatic brain injury

Long non-coding RNAs(lncRNAs) play a key role in craniocerebral disease, although their expression profiles in human traumatic brain injury are still unclear. In this regard, in this study, we examined brain injury tissue from three patients of the 101 st Hospital of the People's Liberation Army, China(specifically, a 36-year-old male, a 52-year-old female, and a 49-year-old female), who were diagnosed with traumatic brain injury and underwent brain contusion removal surgery. Tissue surrounding the brain contusion in the three patients was used as control tissue to observe expression characteristics of lncRNAs and mRNAs in human traumatic brain injury tissue. Volcano plot filtering identified 99 lncRNAs and 63 mRNAs differentially expressed in frontotemporal tissue of the two groups(P < 0.05, fold change > 1.2). Microarray analysis showed that 43 lncRNAs were up-regulated and 56 lncRNAs were down-regulated. Meanwhile, 59 mRNAs were up-regulated and 4 mRNAs were down-regulated. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses revealed 27 signaling pathways associated with target genes and, in particular, legionellosis and influenza A signaling pathways. Subsequently, a lncRNA-gene network was generated, which showed an absolute correlation coefficient value > 0.99 for 12 lncRNA-mRNA pairs. Finally, quantitative real-time polymerase chain reaction confirmed different expression of the five most up-regulated mRNAs within the two groups, which was consistent with the microarray results. In summary, our results show that expression profiles of mRNAs and lncRNAs are significantly different between human traumatic brain injury tissue and surrounding tissue, providing novel insight regarding lncRNAs' involvement in human traumatic brain injury. All participants provided informed consent. This research was registered in the Chinese Clinical Trial Registry(registration number: ChiCTR-TCC-13004002) and the protocol version number is 1.0.

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.

Understanding Citizens’ emotion States under the Urban Livability Environment through Social Media Data: a Case Study of Wuhan

It is recognized that a city with a livable environment can bring happiness to residents.In this study,we explored the social media users’emotional states in their current living spaces and found out the relationship between the social media users’emotions and urban livability.We adopt six urban livability indicators(including education,medical services,public facilities,leisure places,employment,and transportation)to construct city livable indices.Also,the Analytic Hierarchy Process(AHP)spatial statistic method is applied to identify and analyze the different habitable regions of Wuhan City.In terms of citizen’s emotion analysis,we use Long Short-Term Memory(LSTM)neural network to analyze the Weibo text and obtain the Weibo users’sentiment scores.The correlation analysis of residents’emotions and city livability results shows a positive correlation between the livable city areas(i.e.,the area with higher livable ranking indices)and Weibo users’sentiment scores(with a Pearson correlation coefficient of 0.881 and P-Value of 0.004).In other words,people who post Weibo in high livability areas of Wuhan express more positive emotional states.Still,emotion distribution varies in different regions,which is mainly caused by people’s distribution and the diversity of the city’s functional areas.

Expression and regulatory network of long noncoding RNA in rats after spinal cord hemisection injury

Long noncoding RNAs(lncRNAs)participate in a variety of biological processes and diseases.However,the expression and function of lncRNAs after spinal cord injury has not been extensively analyzed.In this study of right side hemisection of the spinal cord at T10,we detected the expression of lncRNAs in the proximal tissue of T10 lamina at different time points and found 445 lncRNAs and 6522 mRNA were differentially expressed.We divided the differentially expressed lncRNAs into 26 expression trends and analyzed Profile 25 and Profile 2,the two expression trends with the most significant difference.Our results showed that the expression of 68 lncRNAs in Profile 25 rose first and remained high 3 days post-injury.There were 387 mRNAs co-expressed with the 68 lncRNAs in Profile 25.The co-expression network showed that the co-expressed genes were mainly enriched in cell division,inflammatory response,FcγR-mediated cell phagocytosis signaling pathway,cell cycle and apoptosis.The expression of 56 lncRNAs in Profile2 first declined and remained low after 3 days post-injury.There were 387 mRNAs co-expressed with the 56 lncRNAs in Profile 2.The co-expression network showed that the co-expressed genes were mainly enriched in the chemical synaptic transmission process and in the signaling pathway of neuroactive ligand-receptor interaction.The results provided the expression and regulatory network of the main lncRNAs after spinal cord injury and clarified their co-expressed gene enriched biological processes and signaling pathways.These findings provide a new direction for the clinical treatment of spinal cord injury.

Fluctuations and pseudo long range dependence in network flows: A non-stationary Poisson process model

In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.

Study on the long-distance target apperception techniques for underwater vehicles

The limited physical size for autonomous underwater vehicles （AUV） or unmanned underwater vehicles （UUV） makes it difficult to acquire enough space gain for localizing long-distance targets. A new technique about long-distance target apperception with passive synthetic aperture array for underwater vehicles is presented. First, a synthetic aperture-processing algorithm based on the FFT transform in the beam space （BSSAP） is introduced. Then, the study on the flank array passive long-distance apperception techniques in the frequency scope of 11-18 kHz is implemented from the view of improving array gains, detection probability and augmenting detected range under a certain sea environment. The results show that the BSSAP algorithm can extend the aperture effectively and improve detection probability. Because of the augment of the transmission loss, the detected range has the trend of decline with the increase of frequency under the same target source level. The synthesized array could improve the space gain by nearly 7 dB and SNR is increased by about 5 dB. The detected range is enhanced to nearly 2 km under the condition of 108-118 dB of the target source level for AUV system in measurement interval of nearly 1 s.

Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Evaluation of diagnostic ratios of medium and serious weathered oils from five different oil sources

Laboratory experiments were conducted to simulate oil weathering process, a medium to long term weathering process for 210-d, using samples collected from five different oil resources. Based on relative deviation and repeatability limit analysis about indexes of these samples, the results show there had been significant changes in diagnostic ratios among the initial and weathered samples of different oils during this process. Changes of selected n-alkane diagnostic ratios of all oil samples displayed more obviously than diagnostic ratios of terpanes,steranes and PAHs in this process. Almost all selected diagnostic ratios of terpanes, steranes and PAHs can be efficiently used in tracking sources of hydrocarbon pollution, differentiating from the n-alkane diagnostic ratios.In these efficient diagnostic ratios, only four ratios maintained good stability in the weathering processes and are more suitable because their relative deviation（RSD） are lower than 5%.

AN INFORMATIC APPROACH TO A LONG MEMORY STATIONARY PROCESS

Long memory is an important phenomenon that arises sometimes in the analysis of time series or spatial data.Most of the definitions concerning the long memory of a stationary process are based on the second-order properties of the process.The mutual information between the past and future I_(p−f) of a stationary process represents the information stored in the history of the process which can be used to predict the future.We suggest that a stationary process can be referred to as long memory if its I_(p−f) is infinite.For a stationary process with finite block entropy,I_(p−f) is equal to the excess entropy,which is the summation of redundancies that relate the convergence rate of the conditional(differential)entropy to the entropy rate.Since the definitions of the I_(p−f) and the excess entropy of a stationary process require a very weak moment condition on the distribution of the process,it can be applied to processes whose distributions are without a bounded second moment.A significant property of I_(p−f) is that it is invariant under one-to-one transformation;this enables us to know the I_(p−f) of a stationary process from other processes.For a stationary Gaussian process,the long memory in the sense of mutual information is more strict than that in the sense of covariance.We demonstrate that the I_(p−f) of fractional Gaussian noise is infinite if and only if the Hurst parameter is H∈(1/2,1).

Simulation study on characteristics of long-range interaction in randomly asymmetric exclusion process

In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model will firstly try to hop over successive unoccupied sites with a probability q, which is different from previous exclusion process models. The probability q may represent the random access of particles. Numerical simulations for stationary particle currents, density profiles, and phase diagrams are obtained. There are three possible stationary phases： the low density （LD） phase, high density （HD） phase, and maximal current （MC） in the system, respectively. Interestingly, bulk density in the LD phase tends to zero, while the MC phase is governed by α,β, and q. The HD phase is nearly the same as the normal TASEP, determined by exit rate β. Theoretical analysis is in good agreement with simulation results. The proposed model may provide a better understanding of random interaction dynamics in complex systems.

高校创新创业教育“赛创融合”评价机制构建

普通高校创新创业教育具有综合性、实践性、创造性等显著特征,既是大学生学习效能提高的重要途径与方法,同时也是中国式现代化建设人才所应具备的核心素养和关键能力的终极性表达。“赛创融合”是提升创新创业教育实施质量的重要路径,也是构建其长效激励机制的有效手段。化结果导向型为过程激励型的评价改革,是促进这一激励机制长效化的关键。高校和教师在“赛创融合”中不仅要能够甄别大学生在创新创业教育中的学习缺陷,还要为学生提供更好的学习指导,将大学生创新创业教育落实落细落长远,最终实现学生终身学习能力的培养。通过开发设计用于过程激励型评价的奖励函数,并以问卷获得学生状态的转移概率矩阵,基于最大化学生可能获得奖励的方式进行仿真,修正和改进评价指标,能够实现创新创业教育长效激励机制严密科学可操作,提升其过程激励型评价效能。

基于双重分解和双向长短时记忆网络的中长期负荷预测模型

针对中长期电力负荷序列噪声含量高、难以直接提取序列周期规律从而影响预测精度的问题,提出了一种基于完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和奇异谱分析(singular spectrum analysis,SSA)双重分解的双向长短时记忆网络(bidirectional long and short time memory,BiLSTM)预测模型。首先,采用CEEMDAN对历史负荷进行分解,以得到若干个周期规律更为清晰的子序列;再利用多尺度熵(multiscale entropy,MSE)计算所有子序列的复杂程度,根据不同时间尺度上的样本熵值将相似的子序列重构聚合;然后,利用SSA去噪的功能,对高度复杂的新序列进行二次分解,去除序列中的噪声并提取更为主要的规律,从而进一步提高中长序列预测精度;再将得到的最终一组子序列输入BiLSTM进行预测;最后,考虑到天气、节假日等外部因素对电力负荷的影响,提出了一种误差修正技术。选取了巴拿马某地区的用电负荷进行实验,实验结果表明,经过双重分解可以将均方根误差降低87.4%;预测未来一年的负荷序列时,采用的BiLSTM模型将拟合系数最高提高2.5%;所提出的误差修正技术可将均方根误差降低9.7%。

白龙江流域大型高位滑坡成灾动力过程模拟研究

白龙江流域山高坡陡,分布大量高位滑坡,是我国高位地质灾害风险极高的地区。舟曲县立节镇北山古滑坡位于白龙江左岸,滑坡剪出口与江面高差约700 m,历史上曾发生过多次变形破坏。通过资料搜集、遥感调查与解译、现场调查等手段,查明了立节北山滑坡的地质环境与变形破坏特征,基于光滑质点流体动力学与等效流体模型,开展滑坡后破坏运动过程模拟,对远程致灾危险进行预测分析。模拟分析表明,立节北山滑坡若发生失稳剧滑,将形成高位高速远程滑坡-碎屑流灾害,滑动距离达1 600 m,最大运动速度45.7 m/s,沿途铲刮方量77.7万m~3,滑体扩容系数1.32。滑体约200 s后完全停止运动并堆积,堆积区面积2.2×10~5 m~2,覆盖坡脚立节镇一半的范围,最大堆积厚度17.8 m,最大冲击速度30 m/s。研究结果为立节北山滑坡开展风险评价与分区提供定量化数据,为白龙江流域大型高位滑坡精细调查与风险评估提供参考依据。