In recent years, Advanced Persistent Threat (APT) attacks against sensors have emerged as a prominent security concern. Due to the low level of protection provided by sensors, APT attack organizations are able to develop intrusion schemes that allow them to infiltrate, attack, lurk, spread, and steal information from the target over an extended period of time. Through extensive research on the APT attack process and current defense mechanisms, it has been found that analyzing Domain Name Server (DNS) traffic in the communication control phase is an effective way of detecting APT attacks. However, analyzing APT attacks based on traffic usually involves the detection of a vast amount of DNS traffic, and current data preprocessing methods do not scale down data effectively, leading to low detection efficiency. In previous work, most efforts have been focused on calculating the features of request messages or corresponding messages without considering the association between request messages and corresponding messages. To address these issues, we propose a sketch-based APT attack traffic detection scheme. The scheme leverages the sketch structure to count and compress network traffic, improving the efficiency of APT detection. Our work also analyzes the limitations of traditional sketches in network traffic and proposes an improved sketch scheme. In addition, we propose several effective features for detecting APT attacks. We validate and evaluate our solution using 1,088,280 DNS traffic from a lab network and APT suspicious traffic from netresec and contagio, using eight machine learning models. The experimental results show that for the ExtraTrees model, our solution has a processing time of 0.0638 s and an accuracy of 0.97920, reducing the processing time by approximately 50 times and improving detection accuracy by a small margin compared to a dataset without sketch processing. With the development of the Internet of Things (IoT), sensors are more commonly used in daily life, such as temperature sensors in cars, sound sensors for controlling lights, and pressure sensors for weighing. Despite the widespread use of sensors, they often lack robust intrusion detection systems compared to traditional hosts and are therefore susceptible to security vulnerabilities, which Advanced Persistent Threat (APT) groups can exploit to launch targeted attacks .In recent years, APT is one of the most representative attacks on the Internet. APT attacks have three main characteristics, which are advanced, persistent, and targeted ,3,4. ForAll APT attacks can be roughly divided into the following five phases: information gathering, embedded intrusion, communication control, lateral penetration, and data returning ,8. In thDue to the long period of APT attacks and the very covert behavior of each phase, we add features, such as the relationship between DNS requests and DNS responses, access frequency, and time correlation, to those about DNS domain names. As far as the current research is concerned, the huge amount of DNS traffic is a major obstacle to effectively detecting APT attacks . For exa1.Introduction. Based on the current security situation of sensors, the specific meaning and threats of APT attacks are introduced. This section summarizes the shortcomings of the current research and expounds the main work and organizational structure of this paper.2.Related work. This part mainly introduces the process of APT attacks and the current mainstream defense measures. Sketch is introduced according to the shortcomings of the above methods, and its existing work in traffic measurement and attack detection is introduced.3.APT attack detection scheme based on CK sketch and DNS traffic. The proposed scheme for APT attack detection is based on the combination of the characteristics of the APT attack communication control stage and sketch structure. The scheme is analyzed theoretically in depth, and the design ideas and implementation details of the data preprocessing, feature extraction, and the module for counting and compressing DNS traffic with an improved sketch structure, are described in a comprehensive manner.4.Experience. In the part on the attack detection algorithm, open datasets were used to test its performance. The detection algorithm was evaluated by indicators such as true-positive rate (TPR), precision, accuracy, and processing time, and compared with existing schemes of the same kind to verify its superiority.This article will be organized as four sections:The main contributions of this paper can be summarized as follows:Sketch structure is used to count and compress DNS traffic containing APT attacks, which improves the efficiency of the approach without reducing the detection accuracy.The traditional sketch structure is improved to avoid the error problem caused by the occurrence of hash conflicts in the count min (CM) sketch structure, and the mechanism of adaptively adjusting the number of hash buckets in the sketch structure is added to guarantee the accuracy rate while improving the efficiency of the algorithm for the follow-up.The characteristics of APT attack traffic are emphatically analyzed. Based on the characteristics of the DNS domain name, eight features are proposed that can reflect the relationship between DNS request and DNS response, access frequency, and time correlation in APT attacks.Combined with eight characteristics of APT attacks, eight machine learning effects are compared. The algorithms include Logistic Regression(LR), KNN, GaussianNB, DecisionTree, Bagging, RandomForest, ExtraTrees, and AdaBoost.Different APT attacks have different attack modes but have similar stage characteristics in the attack process. All APT attacks can be roughly divided into the following five stages, which is shown in Information gatheringInformation gathering is the process by which APT attackers use multiple means to collect various information about the target network . The colEmbedded intrusionEmbedded intrusion is the stage where the APT attack formally begins, where the APT attacker writes targeted malware to adapt to the target network environment based on the information gathered about the target system network during the intelligence-gathering phase.Communication controlAfter successfully compromising the target network, the APT attacker continuously invades the internal core network devices based on the control of the internal network devices already gained, which usually requires establishing a command and control communication channel between the C&C server and the target infected host ,17,18,19Lateral penetrationLateral penetration is the stage in which an APT attack spreads to infect hosts. After an APT attacker has compromised one target network host, he continuously elevates his privileges and steals data within the scope of the privileges he has gained. However, APT attack activities are not satisfied with retrieving data on only one machine and need to continuously find new internal network hosts for spreading propagation and infiltrating the core network .Data ReturningThe data-returning phase sends the discovered data packaged and compressed back to the C&C server to complete the theft of data. The network traffic in the data returning phase is not significantly different from normal network traffic , and detZhao  proposedCho DX  proposedFu Yu , Shi YonLu Jiazhong  comparedWenlin Chu  proposedThe comparison of the existing APT detection methods is shown in With the increasing prevalence of high-speed and non-uniformly distributed network traffic, accurately measuring network properties has become a challenging task. This is due to the sheer volume of network traffic data, which can result in decreased throughput if the statistical measurement method employed has a certain degree of latency.To address this issue, the field of network traffic statistical analysis has resorted to using sketch structures. This technique involves constructing a hash function to count and compress the network traffic. A sketch structure typically consists of two components: (1) hash function, which calculates the hash value corresponding to the key in the data stream as an index for data compression, aggregation, and storage, and (2) hash bucket, which the sketch structure creator sets to a specific number for storing the required feature values of the data stream.Sketch structures have found extensive applications in a variety of areas, including traffic analysis, stream size change measurement, large stream detection, and persistent stream measurement. It has been demonstrated that the use of sketch structures can significantly improve the statistical accuracy of network traffic data while simultaneously compressing, aggregating, and storing it .The classical CM sketch, introduced by Cormode G in , has beeWhen using the CM sketch for network traffic analysis, it is crucial to designate a feature of the traffic, such as the DNS domain name or the Source IP, as the key and the rest of the features as the values to be counted. The location of the network is calculated using the n hash functions as specified in Equation .(1)lociEach network traffic is mapped to n hash buckets, and then the value in the network is added to the value in the hash bucket.In this section, we will describe the process of the proposed scheme in this paper and describe each part of the framework in detail in the subsections. This scheme is based on sketch structure and detects whether APT attacks occur by analyzing DNS traffic. It can report to the security officers quickly when it finds suspicious domains, and it can help the defenders discover APT attacks faster.The running flow of the APT attack detection scheme proposed in this paper based on Cuckoo (CK) sketch and DNS traffic is shown in Next, we selected the domain names in the DNS traffic as the key, and for the communication control phase of the APT attack, we selected eight features as parameters to outline the network state statistics, the specific types and characteristics of these features will be described in After the original dataset has been counted and compressed by sketch structure, several types of preprocessing operations are required, mainly including outlier removal, missing value filling, feature scaling, etc., to improve the dataset and enhance the classification effect and efficiency of the machine learning model.Finally, the dataset after the above processing is the dataset to be classified. In order to build the machine learning model and test its attack detection effect, we divided the dataset into a training set and test set in the ratio of three to one. The training set is marked as normal and abnormal according to the mapping relationship between the DNS domain name and grid serial number in the sketch in advance. To solve the dichotomous classification problem proposed in the last step of the algorithm, we chose eight typical classifiers for attack detection, trained and tested these eight models, recorded the results, evaluated the respective characteristics and learning effects, and selected the optimal detection model.Malicious DNS traffic from APT attacks is mixed in with normal traffic, and DNS traffic from internal network hosts can be very large, resulting in a very large size of raw data collected in the end. Therefore, it is necessary to preprocess the data, reduce the amount of data processed by the detection model, and ensure that the original data has the same characteristics compared with the processed data. While improving the detection efficiency, the detection results are not affected.The data preprocessing consists of four parts in total, DNS packet filtering, folding the domain into the second-level domain, white list filtering, and access frequency filtering. The specific algorithm flow is shown in DNS packet filteringAs can be seen from Folding the domain into the second-level domainwww.zhihu.coa and wenda.zhihu.com, which will be transformed into zhihu.com.Since second-level domains are of higher value for identifying apt malicious traffic, all domains are converted into second-level ones, such as White List FilteringAccording to the characteristics associated with APT attacks, APT attackers expect to be able to control the infected host for a long time, so they need to send commands remotely through a C&C server. Using a server with a popular domain name as a C&C server can achieve a good stealth effect, but it requires a great cost. An APT attacker has to compromise a server with a popular domain name that has a well-developed defense system installed, which undoubtedly increases the risk of exposure and is easy to detect by network defenders. Therefore, it is generally believed that popular domain names are not likely to be used for APT attacks. With such a premise, this paper proposes a white list filtering detection algorithm. The top 10,000 domain websites can be obtained from the Alexa website, and the domain white list is constructed based on the obtained domain names. If the resolved domain name belongs to the white list, no further processing is performed for the domain name and the current DNS data message is discarded directly.Access frequency filteringAPT attacks are a low-frequency attack mode in which the malware in the internal network host wants to initiate connection requests with external C&C servers by sending DNS request messages, which are not very frequent. The number of DNS requests sent in a day must be below a certain threshold, thus greatly reducing the risk of detection by the defense system. In this paper, the threshold is set to 100 times/day, and a domain name that is accessed more than 100 times in a day is considered normal, and the domain name is removed from the candidate domain name results.1.Domain LengthIn APT attacks, attackers usually use the domain generation algorithm (DGA) algorithm to generate a series of domain names, which are generally longer in length. There are two reasons for this: first, the shorter domain names may have already been registered by organizations or individuals, and the domain names generated by the algorithm may conflict with the already registered algorithm, resulting in the failure of the C&C server domain name; then, the attacker uses registered second-level domain names to reduce maintenance costs, and the overall length of the domain name is longer. Due to such a process of constructing domain names, this paper argues that the length of the domain name can be used to determine whether it is a malicious domain name.2.Number of Visits to the DomainWhether it is the initiation of communication control or the lateral penetration phase, in order to avoid detection, the attacker acts very stealthily and rarely initiates connections with external servers. So, the fewer the number of visits to the domain to be detected the more suspicious the domain is.3.Access PeriodUnder normal circumstances, the time of the host accessing the domain name should show a certain regularity related to people’s work and rest. APT attackers, in order to improve the stealth of malware connections, usually initiate DNS requests when people are sleeping, thus reducing the chance of being detected by humans. It is reasonable to assume that DNS access requests during the period of 0 a.m. to 6 a.m. each day are suspicious.4.Digital FeatureAccording to several malicious domain names with numbers that have been found, malicious domain names usually change some letters to numbers or add some numbers to the regular domain names. The domain names generated by the DGA algorithm based on the domain-flux technology generally contain numbers. APT attackers also deliberately alter domain names to pretend to be normal ones, for example, the letter “o” might be replaced with a 0, and the letter “l” with a 1. Calculate the proportion of digits based on whether the domain name contains digits. As for the numerical feature of the domain name, focus on the domain name that contains digits 0 or 1.5.Packet Length CharacteristicsAccording to the characteristics of APT attack traffic analyzed above, the downstream traffic is generally the control commands sent by APT attackers, while the upstream traffic is the result of the information returned by the controlled host after the execution of the control commands. This is different from normal DNS traffic. In this section, the average length of upstream and downstream packets is calculated respectively as the feature of packet length.6.Domain Name Request and Response IntervalIn some APT attacks, the attacker-controlled DNS servers are not guaranteed to be online for a long time to increase the stealthiness, so the malware does not receive the IP address of the C&C server when it initiates a request message for the C&C server domain name. Based on such a situation, this paper proposes the time interval between DNS domain name response and request as a feature for the following reasons: in APT attacks, the DNS resolution process for the C&C server domain name is unusual, and this feature can reflect the complete resolution process; the frequency of malware-initiated DNS requests should be below a certain threshold, and the malicious domain name is not in the corresponding cache of the DNS server, and it takes more time to query the IP address corresponding to the domain name. If the time interval between the response and the request is too large, the domain name can be considered suspicious.7.Time to Live (TTL)TTL is the retention time of domain name resolution records in DNS servers, increasing the TTL value can accelerate the domain name resolution time, and decreasing the TTL value can accelerate the effective time of domain name resolution. The default TTL is currently given as 3600 s, and the value can be adjusted by itself as needed. Leyla  summariz8.Domain Access IntervalUnder normal circumstances, the time interval between two visits to the same domain by internal hosts is not too small. The attacker has a long period of latency during the lateral penetration phase, in which the infected hosts rarely visit external servers to hide their behavior, so the longer the interval between domain accesses, the more suspicious the domain is.Among the above eight features, features 1, 4, and 5 do not need to be accumulated and can be calculated directly using domain names; features 2 and 3 are accumulated using counting; features 6, 7, and 8 are calculated and measured in the form of entropy.In To address these challenges, the present study proposes an improved CK hash sketch structure. This structure consists of a multi-layered sketch architecture that employs multiple hash functions and hash buckets. The creation of the different sketch layers is guided by the principle of cuckoo hash, enabling the resolution of hash conflicts.The proposed CK sketch structure dynamically adjusts the number of hash functions and hash buckets based on the size of the network traffic data, thereby improving throughput and reducing the occurrence of hash collisions during statistical analysis. This results in a more efficient utilization of resources and improved accuracy of network traffic analysis.The Bloom filter  is a datTo attain optimal analysis of network traffic statistics, the CK sketch data structure proposed in this study is shown in 1.Hash bucket: The incoming network traffic is comprised of  pairs, where the key is represented as the DNS domain name, and the value is a set of eight feature values selected to describe APT attacks, as outlined in n + 1 is double that of layer n.The outcome of the hash function operation varies between each layer, and to comprehensively account for the heterogeneity of network traffic, the number of hash buckets in layer 2.Bloom filter: This structure encompasses the main hash function and a two-dimensional vector. The main hash function is employed to map the network traffic into the two-dimensional vector, where the key is recorded in the first line of the corresponding position in the mapping. Upon completion of data insertion, the number of layers in which the data are located will be recorded in the second row of the corresponding position in the mapping.The process of compressing the statistical DNS traffic  is shown in 1.Determine whether the current DNS traffic is already located in the hash bucket. The key in the network traffic  is calculated by the main hash function through Equation  to obtaiAccording to whether the There are no data in this position, then the element is added to the There are data in this location but the corresponding key is different from the corresponding key of the current DNS traffic, the element will be stored in the hash bucket of the multi-layer starting from the first layer.There are data in this location and the corresponding key is the same as the corresponding key of the current network traffic, the element will be stored in the corresponding layer of the multi-layer hash bucket directly according to the number of layers in the Bloom filter.2.Calculate the location of the hash bucket where the DNS traffic is stored. Put the network traffic  into the nth layer according to step 1 and Equation  to deter3.Determine whether there is a hash conflict. If the 4.n + 1 is used to calculate Formula (6) to obtain the location of layer n + 1 traffic, and insert this DNS traffic into the layer n + 1 hash bucket according to the above steps. Finally, the number of layers where the traffic resides is recorded in Bloom filter. Count and compress network traffic . If there is no hash conflict, the key in the network traffic is recorded, and the value is added to the value in the hash bucket. If a hash conflict occurs, create a different hash function 5.Record the number of hash bucket layers where the DNS traffic  is located. After counting and compressing, the number of layers where the DNS traffic is located will be recorded in the Bloom filter.The experimental dataset consists of two parts of data, which are APT malicious DNS traffic provided by netresec and contagio and normal DNS data from the lab. The malicious traffic dataset contains 15 different types of malicious network traffic from the sources shown in To verify the performance of the algorithm proposed in this section, the test program was deployed on a host computer with Intel(R) Core(TM) i7-7700HQ CPU @ 2.80 GHz 2.81 GHz, 16G RAM, Nvidia GTX 1060 GPU, Win10 OS, Python 3.6 version.TP), indicating samples that are malicious traffic and are correctly detected as attack traffic.True Positive (TN), indicating samples that are actually normal traffic and are correctly detected as normal traffic.True Negative (FP), indicating samples that are normal traffic but are incorrectly detected as attack traffic.False Positive (FN), indicating samples that are malicious traffic but are incorrectly detected as normal traffic.False Negative (1.TPRTPR) means the proportion of true positive samples to all positive samples in the current positive sample, which is the accuracy of the measurement. The accuracy of the measurement is also known as sensitivity. It is expressed by Equation (True Positive Rate (Equation .(7)TPR=2.PrecisionPrecision means the proportion of samples that are positive and correctly classified as positive to all samples classified as positive. It is expressed by Equation .(8)Prec3.AccuracyAccuracy means the percentage of all samples that are correctly detected. It is expressed by Equation .(9)Accu4.F-measureF-measure, also known as F1-Score, combines the precision and recall considerations of detection accuracy and is the summed average of the two, calculated using Equation .(10)F-m5.ThroughputIt is the number of successfully processed data per unit of time. The experimental part uses the number of packets successfully deposited into the sketch structure per second as the throughput. Higher throughput means faster and shorter processing time for network traffic and better performance.6.Processing timeOur scheme first performs feature extraction and summary statistics through CK sketch, and then includes machine learning model training and prediction after preprocessing. The data preprocessing and resampling parts contain a lot of manual offline operations, and the running time is difficult to count, so we only focus on the time consumed by the construction of the machine learning part of the classification model and test set prediction.The selected dataset is compressed and counted using the CK sketch structure after the preprocessing operation. The eight features illustrated in From This experiment compares the effect of sketch structure on the training time of various machine learning models in the proposed scheme. In the experiments, we used the same dataset to train all the models, record the time required to finish training the models, and then used the dataset counted and compressed by the sketch structure to train the models and record the time required. The experimental results are shown in This experiment compares the accuracy difference between six machine learning models trained and detected using the original dataset and the dataset compressed and counted by CK sketch. The experimental results are shown in In this experiment, the objective was to compare the processing efficiency of network traffic among different sketch structures. The experiment evaluated the throughput of CK sketch, CM sketch, and Count sketch when compressing and counting network traffic. The results of the experiment are depicted in The experimental results show that the scheme is effective in detecting APT attacks and drastically reduces the time for training multiple machine learning models. We compare the performance of CK sketch with two other sketch structures in terms of throughput and based on the results we can see that the throughput of CK sketch is much larger than the other two sketch structures. Using the sketch structure to count and compress massive DNS traffic can effectively improve the efficiency of detecting APT attacks and reduce losses.In the scheme proposed by Yan Guanghua, the author used eight DNS traffic features combined with deep learning to judge APT attacks. However, these eight features lack the consideration of APT attack access time and TLL value in DNS traffic packets, resulting in the failure to accurately detect APT attacks with time rules. Therefore, on the basis of some of the above eight features, this paper adds access time period, packet length, TTL, and other features, so as to obtain a more accurate detection effect. In the part of data preprocessing, the author mainly uses the means of domain name ranking and access times to screen the initial data, and this method has a certain effect. However, the amount of data cannot be reduced effectively, which leads to the training time of the model being too long and the decrease in detection efficiency. Therefore, this paper introduces the CK sketch structure to compress and count the DNS traffic while guaranteeing the accuracy, and significantly reduce the time for model training. However, due to data privacy, the above authors’ datasets were not available. In the comparative experiment, the dataset in Since CK sketch uses domain names as the key to compress and count DNS traffic, the diversity of domain names in datasets will affect the time superiority of the scheme proposed in this paper. Therefore, we filter the dataset in The test results show that the proposed scheme cannot greatly reduce the time of model training when the domain name in the dataset is repeated. Based on the above characteristics, we can improve the structure of sketch or select multiple features as key in future research.In this paper, we present a novel APT attack detection approach based on the combination of the CK sketch structure and DNS traffic analysis. Our work allows security personnel to quickly identify and respond to potential APT attacks based on the analysis of DNS traffic. The proposed method leverages the unique characteristics of DNS traffic to identify potential APT attacks and utilizes the CK sketch structure to efficiently compress and summarize the traffic statistics. The experimental results demonstrate the effectiveness of the proposed approach in terms of both processing time and detection accuracy. Specifically, when applied to the ExtraTrees model, the processing time was reduced by approximately 50 times to 0.0638 s while maintaining a high accuracy of 0.97920. This reduction in processing time is expected to be achievable with other machine learning models as well.It is important to note that the scope of our current work is limited to the communication control phase of APT attacks and does not take into account the characteristics of other phases. Additionally, the scheme may not provide significant time savings in cases where DNS traffic exhibits a high degree of variability in terms of domain names.In future work, our focus will be on expanding the scope of APT attack characterization to encompass not only DNS traffic but also TCP and HTTP traffic. This will provide a more comprehensive understanding of APT attacks and allow for the selection of different attributes as keys in the sketch structure, providing greater adaptability to a wider range of APT attack scenarios. Additionally, we aim to gather more information about the suspect domain names and hosts involved in APT attacks for improved security analysis.


Al20Mn5.37Ni1.31 adopts the Co2Al5 structure type in space-group type P63/mmc, replacing the Co atoms with the transition-metal atoms Mn and Ni. Structure analysis revealed that one of the two transition-metal sites is partially occupied by Ni [refined occupancy 0.342 (2)] and the other is co-occupied by Mn and Ni with a ratio of 0.895 (14):0.105 (14). The present refined chemical composition is supported by complementary energy-dispersive X-ray fluorescence (EDX) analysis and is in agreement with the previously determined Al–Mn–Ni phase diagram .The intermetallic phase with composition Al al. 2011. J. Allo It should be noted that such Co2Al5-type phases have also been found in other systems e.g. in the binary Al–Mn system the phase Al10Mn3 with unit-cell parameters a = 7.543, c = 7.898 Å  of a stoichiometric mixture with nominal composition Al71Ni24Fe5 . This phase is located within the diagram region of the φ phase determined previously (see Table S1 of the supporting information).Phases in the ternary Al–Mn–Ni alloy system are structurally complex, also including quasicrystals (QC). For example, an aperiodic diffraction pattern was observed for the alloy with composition Al2Al5 structure type , 6 h (Al2) and 12 k (Al3), one partially occupied Ni2 site [occupancy 0.342 (2)] at 2 d and one co-occupied (Mn1/Ni1) site [occupancy ratio 0.895 (14): 0.105 (14)] at 6 h. The environment of the co-occupied (Mn1/Ni1) site is shown in Fig. 2With respect to the CoNi Fig. 1. The asy−1. Finally, the sample was slowly cooled to room temperature by turning off the furnace power. Suitable pieces of single-crystal grains were selected from the products for single-crystal X-ray diffraction experiments.The high-purity elements Al , Mn  and Ni  were mixed in the molar ratio 60:7:3 and ground in an agate mortar. The blended powders were placed into a cemented carbide grinding mound of 9.6 mm diameter and pressed at 4 MPa for about 5 min. The obtained cylindrical block was crushed and a sample with a weight of 50.32 mg was selected and subsequently loaded into a Netzsch STA449C simultaneous thermal analysis apparatus. The sample was heated up to 1373 K for 10 min with a heating rate of 20 K min2Al5 structure type, the labelling scheme and atomic coordinates were adapted from Co2Al5  I. DOI: 10.1107/S2414314621009810/wm4153Isup2.hklStructure factors: contains datablock(s) I. DOI: Click here for additional data file.10.1107/S2414314621009810/wm4153sup3.docxSupporting figures and tables. DOI: 2110961CCDC reference:  crystallographic information; 3D view; checkCIF reportAdditional supporting information:


The organization of the genome into domains plays a central role in gene expression and other cellular activities. Researchers identify genomic domains mainly through two views: 1D functional assays such as ChIP-seq, and chromatin conformation assays such as Hi-C. Fully understanding domains requires integrative modeling that combines these two views. However, the predominant form of integrative modeling uses segmentation and genome annotation (SAGA) along with the rigid assumption that loci in contact are more likely to share the same domain type, which is not necessarily true for epigenomic domain types and genome-wide chromatin interactions.Here, we present an integrative approach that annotates domains using both 1D functional genomic signals and Hi-C measurements of genome-wide 3D interactions without the use of a pairwise prior. We do so by using a graph embedding to learn structural features corresponding to each genomic region, then inputting learned structural features along with functional genomic signals to a SAGA algorithm. We show that our domain types recapitulate well-known subcompartments with an additional granularity that distinguishes a combination of the spatial and functional states of the genomic regions. In particular, we identified a division of the previously identified A2 subcompartment such that the divided domain types have significantly varying expression levels.https://github.com/nedashokraneh/IChDA.Bioinformatics online. Chromosomes are segmented into different domain types that are identified through analysis of genomic data  algorithms have emerged as the predominant tool for integrative modeling of multiple 1D genomic signals  , and DNase-seq, as 1D functional genomic signals. We used Hi-C data to learn genomic structural features, and add spatial constraints to existing integrative models , such that pairwise distances in the embedding space are representative of the proximity of pairs of nodes in the input graph. They have four main components, (i) encoder: to map the graph structure into a vector space, (ii) proximity definition: to define a proximity measure between pairs of nodes based on the input graph structure, (iii) decoder: to map the embeddings of a pair of nodes to a reconstructed proximity measure and (iv) loss function: to penalize the difference between the reconstructed and true proximity measures.V is the set of nodes E is the set of edges attributed with a ijw representing the weight of the edge between nodes iv and jv, the first-order proximity of a pair  is equal to the weight ijw of the edge connecting that pair. A greater edge weight between nodes indicates more first-order proximity between nodes.The most important component to define is the proximity measure which is based on the graph structure that we aim to embed. Given a weighted graph iv with all other nodes is defined as i, j) is defined as the similarity between A greater similarity between nodes’ interaction patterns with the rest of the nodes in a graph indicates a higher second-order proximity between the two nodes. More formally, a first-order proximity of a node LINE can be optimized based on either first-order or second-order proximity loss functions. Since we aim to capture the global chromatin structural features, we will use LINE embeddings based on the second-order proximity. We also show that second-order embeddings capture more meaningful global chromatin structural patterns compared to first-order embeddings in the iv are defined, the embedding of the node, iu, and the context of the node, iv as a distribution over all the nodes that determine the probability that the context of each node has been generated using embedding of node iv.To learn the embeddings that capture the second-order proximity between nodes, two vectors corresponding with each node iλ is given to each node i equal to its degree. The full objective function is defined asTo preserve the second-order proximity, We use LINE’s . The optOur approach involves applying existing segmentation and genome annotation (SAGA) methods to integrate 1D and 3D genomic features. Unsupervised segmentation and genome annotation (SAGA) algorithms cluster the genomic positions based on their observed functional or chemical genomic signals  Gaussian Mixture model (GMM), the most simple SAGA method that only models the dependency between latent labels and observed genomic signals, (ii) Hidden Markov model with observations modeled as GMM (GMM-HMM) that model the dependency between neighbor latent labels, (iii) Segway .By applying a node embedding model to genome-wide Hi-C data, we map each genomic bin into a vector space. A vector space with dimension 8 is enough to capture the structural information in whole Hi-C data .Then, we input the learned 8 structural features with 12 functional genomic signals to HMM, which we showed that it is a proper choice for the domain-scale annotation (Section 3.1), and infer domain types that we call HMM_combined. We also input the learned structural features and functional genomic signals to HMM separately to identify HMM_structural and HMM_functional domain types separately as baselines.2.5.1K is the number of labels) and a signal vector k is calculated asThe percentage of variance explained (VE) is used to measure the consistency between different epigenomic signals, such as histone modifications and gene expressions, with annotated domains. The VE is a fraction of signal variance that is explainable using an annotation prediction. Given a genome annotation So, the predicted signal vector is VE is bounded by the range 2.5.2The most important function of domain structures is transcription regulation, and we expect that domain states have different transcriptional patterns. We acquired RNA-seq measurements of gene expression, measured in RPKM . We anno2.5.3ip in a phase i.The order with which DNA is replicated in S phase is correlated with many genomic phenomena including domain regulation First, we change the resolution of RT signals from 1 to 100 kb resolution to match the annotation’s resolution. Then we calculate the VE for each phase separately and calculated a mean of VE among 6 phases as RT VE.2.5.4L is a number of labels, ijO is a number of loops with one end belonging to the label i, and the other belonging to the label j. Given a normalized coverage of labels i and j is We also assessed the domains with respect to higher-order chromatin structures, including loops from CTCF and RNAPII  on functional genomic signals, and identify six domain types for GM12878. The average length of Segway domains (0.4 Mb) is longer than HMM domains (0.25 Mb) and HMM domains are also longer than GMM domains (0.2 Mb) due to the HMM and Segway duration models . We compTo identify domains based on structural features, we learned genomic structural signals using the LINE embedding model for all genomic bins in a shared space (Section 2.2), applied an HMM on the learned structural signals, and identified six domain types .We named HMM_functional and HMM_structural domain types based on their overlap with subcompartments from HF1 to HF6 and HS1 to HS6, respectively, such that the overlap with inactive subcompartments increases by increasing the domain type’s number .We compared HMM_functional, HMM_structural and subcompartment annotations for GM12878 cell type in terms of their domain sizes, the overlap of their domain types, and explanation of other chromatin features including gene expression and replication timing profiles and loops from ChIA-PET data . We obseThe chromatin features that are regulated in domain-scale such as replication timing and ChIA-PET loops are better explained by structural domain types . We calcP-values In contrast, a more local feature, gene expression, is better explained by HMM_functional annotation . There iDue to the availability of structural genomic signals, LaminA and SON TSA-seq . The proThese observations show that the separation of active functional domain types is based on their local enrichment of functional activities such as histone modifications, while the separation of active structural domain types is mostly based on their positioning within the nucleus as measured indirectly by Hi-C. Therefore, functional and structural compartmentalization of chromatin are different chromatin organization layers and complementary to each other, and thus an integrative approach is needed to model both patterns together.To infer domain types that jointly capture chromatin functional and structural compartmentalization, we input functional and learned structural genomic signals to the HMM (Section 2.4); we term the resulting combinatorial annotation HMM_combined.We compared the HMM_combined annotation to existing methods that utilize both measurements of 1D genomic signals and 2D Hi-C data, Segway-GBR and SPIN. The graph-based posterior regularization (GBR) can be added to any type of probabilistic model, and we use the GMM_GBR version using GMM as a base probabilistic model to be consistent with two alternatives, SPIN and HMM_combined, that utilize GMM as their basic component as well. Note that by SPIN annotations, we mean the result of applying HMRF, the approach used for SPIN, to our datasets.k = 6. Other labels have very low coverage and are not interpretable, therefore, we made SPIN annotation with two labels HMRF1 and HMRF2 , where HMRF1 is active and HMRF2 is inactive. Further analysis of SPIN result is provided in the We ran HMRF, SPIN’s method, on our data, functional genomic signals, and significant Hi-C interactions from GM12878 cell type. We observed that SPIN results in just two common labels with coverage of 0.5 and 0.49 , even thThen, we compared three integrative models in terms of explaining different cellular activities and chromatin features . We can To find the contribution of different genomic signals in different annotations, we calculated the normalized variance explained for 12 functional genomic signals and 8 structural embeddings given different annotations  for GM12We found that the domain types learned by the combinatorial HMM recapitulate subcompartments and identify additional granularity among these compartment types. As a result of the aggregation of functional and structural genomic signals, it is expected to find more than 6 domain types. Therefore, we evaluated combinatorial domain annotations with 6-10 types according to gene expression VE and RT VE . We chooThese eight combinatorial domain types can be classified into four active (HC1–HC4) and four inactive types (HC5–HC8). These types largely recapitulate known subcompartments. HC1 corresponds to the A1 subcompartment (associated with broad transcription) and is enriched with transcribed genes.Combinatorial domains reveal a distinction between two types of A2 subcompartments (associated with cell type-specific transcription), represented by HC2 and HC3. HC2 overlaps with A2 and has a lower gene density compared to HC1; however, its genes are highly transcribed. HC3 also overlaps with A2, however, it is less enriched with active marks, and its genes are moderately expressed compared to HC2. It shows that the A2 subcompartment can be further divided into two domain types with different epigenomic and gene expression patterns.HC4 is the last active type, which has a lower coverage and is less transcribed compared to other active types. These domains are mixed between the A1 and B1 subcompartments and feature moderate activating signals and gene expression. This domain type confirms the existence of genomic regions interacting with the active compartment but without high transcription.The other four combinatorial labels correspond to inactive domain types. HC5 corresponds to the B1 subcompartment (associated with facultative repression), and it is highly enriched with H3K27me3, representative of facultative heterochromatin (HC6 corresponds to the B2 subcompartment (associated with nucleolar domains): it is enriched with the H3K9me3 mark, representative of constitutive heterochromatin, and depleted from genes.Combinatorial domains also reveal a distinction between two types of constitutively inactive subcompartments (B2 and B3) (associated with constitutive repression). HC7 is enriched with both inactive marks, H3K27me3 and H3K9me3, while HC8 is depleted of all epigenomic marks.We also visualized the same plot for HMM_functional and HMM_structural eight domain types . We founP-value = 0.09) is much smaller than between HC3 and HC4 , while chromatin 3D domain types, or subcompartments, have been studied in coarse resolutions (btac813_Supplementary_DataClick here for additional data file.


Acrocomia intumescens (“macaúba”) is a species that during processing generates a large amount of waste. The use of this residue for the production of flour for incorporation into food products is a way to minimize the cost and nutritionally enrich the final product. This work aimed to develop and analyze, in terms of physico-chemical and microbiological properties, cookies with macaúba almond cake residual flour. Sequilhos formulations were obtained using three different proportions of flour . The analysis of the flour allowed to find 4.29% of moisture, 1.13% of acidity, 5.33 of pH, 44.46% of carbohydrates, 28.74% of lipids, 20.06% of proteins and 2.45% of ash. In the analysis of the sequilhos formulations, the following values were found: moisture (5.03 to 8.13%), acidity (0.10 to 0.14%), pH (5.52 to 5.93), carbohydrates (67.17 to 73.37%), lipids (18.77 to 31.77%), proteins (0.85 to 1.92%), ash (0.83 to 0.94%) and total energy value . In microbiological analyzes it was highlighted that the sequilhos presented adequate sanitary conditions. The results indicate almond flour as an ingredient to be incorporated in the preparation of sequilhos, which in turn, presented satisfactory physico-chemical properties and microbiological results. Acrocomia intumescens (“macaúba”) is found in several regions of Brazil, including Chapada do Araripe, Cariri Cearense, where there is a vast wealth of fruits and native species such as Caryocar coriaceum (pequi), Orbignya speciosa (babassu), Mauritia flexuosa (buriti), among others  were collected in an area of Chapada do Araripe (Sítio Arajara), municipality of Barbalha, CE, Brazil. Exsiccate of the species (#9710) is deposited in the Herbarium Caririense Dárdano Andrade Lima (HCDAL) of the Universidade Regional do Cariri (URCA).Fruits of 2) for 2 h. This mechanism is normally used by industries and cooperatives to obtain macaúba oil.After collection, the fruits were separated, excluding those that had mechanical lesions and/or the presence of microorganisms, then sanitized under running water and immersed for 15 min in a 1% sodium hypochlorite solution. The fruits were peeled and then manually pulped using stainless steel knives. The almond was obtained by partitioning the fruits with the aid of mechanical press . Residual cake was obtained from 400 g of macaúba almond after oil extraction in hydraulic pressure  preheated to 180°C for 15–25 min. After 20 min in the oven the sequilhos were removed and cooled for approximately 30 min at room temperature. The sequilhos were stored for 72 h in sealed plastic packages and kept at room temperature around 26°C with a relative humidity of 40%.The physico-chemical analyses of the flour and sequilhos were carried out in triplicates. Moisture was determined by the desiccation loss method in direct drying in an oven  at 105°C for 24 h  of the sequilhos were based on RDC n°C 359 . The valThe microbiological analyses carried out on the sequilhos are those indicated for the food group: biscuits and crackers, without filling, with or without topping, including honey bread, biscuits, and the like . The micThe presence of thermotolerant coliforms was confirmed by inoculation in 2% bright bile green broth incubated at 44°C. The presence of gas in the inverted Durhan tubes evidences the fermentation of the lactose present in the medium. The reading took place after 24–48 h of incubation using the most probable number (MPN) .The mold and yeast count was performed using the standard plate counting method, in which 25 g of the sample was homogenized in 225 ml of peptone water with consequent serial dilutions. The number of colony forming units per gram (CFU/g) was determined by inoculation in potato dextrose agar acidified with a 10% tartaric acid solution at a temperature of 25 ± 1°C for 5–7 days .post hoc test at the 5% level which was used to separate the means using GraphPad Prism 5.0. The obtained value p < 0.05 should be considered significant.The data were expressed in means ± SD. The values were subjected to analysis of variance (ANOVA) by Tukey’s The moisture values of the sequilhos formulations ranged from 5.03 to 8.13%. Moisture increased in the formulations added to the flour. This fact may be due to the protein and/or fiber content that has the capacity to retain a greater amount of water in the final product , 16. An The carbohydrate content in the flour was 44.46%. Overall, flours demonstrate relevant carbohydrate values . Pequi aHancornia speciosa (mangaba) pulp flour were also observed to reduce carbohydrates, with values ranging from 51.53 to 45.89% , acidic (pH between 4.0 and 4.5), and very acidic (pH <4.5) . AcidityThe amounts of carbohydrates and lipids in relation to proteins indicated a more effective contribution of these nutrients to the caloric value of the formulations. In addition, flours are an excellent source of energy, normally given their carbohydrate and lipid content . HoweverMicrobiological analyses are extremely important to verify the hygienic-sanitary conditions of food, the risks that they can present to health and how long they can have a shelf life. Through the results of the microbiological analysis, it can be seen that the sequilhos met the standards required by legislation  for therFood is considered safe when it does not pose a risk to the health of consumers. Food is vulnerable to contamination by various agents, which can trigger various foodborne diseases, which can be derived from pathogenic microorganisms or the toxins produced by them . The preMacaúba almond flour was a viable ingredient for the formulation of sequilhos-type biscuits, highlighting the nutritional content. The microbiological analyses highlight adequate sanitary conditions in the preparation of the sequilhos and indicate that they are suitable for human consumption. The sequilhos showed physicochemical changes with a significant increase in protein levels. The use of flour for the nutritional enrichment of this type of biscuit can be put to good use, recommending an increase of mainly 6%.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.ES: conceptualization. MF and LP: methodology. MS: formal analysis and investigation. HC, AR, and ES: project administration. JV, FS, CL, HC, AR, HmA, ZA, and HaA: visualization. AR, JA, HmA, ZA, and HaA: writing—review and editing and funding acquisition. All authors have read and agreed to the final version of the manuscript.


The fractographic investigation demonstrated that fatigue striations and secondary cracks were the main features in the crack initiation zone. Dislocation analyses indicated that the HCF deformation of the basket-weave microstructure was mainly affected by the dislocation slipping of the primary α (αp) phase. In addition, the dislocation pile-up at the αp/βtrans interface led to crack initiation. EBSD analyses indicated that the prismatic <a> type slip system of the αp phase was preferentially activated during the HCF deformation process of the Ti55511 alloy, followed by the basal <a> type and pyramid <a> type systems.High-cycle fatigue (HCF) is a critical property of metastable β Ti alloys in aerospace applications. In this work, the HCF behavior and corresponding microscale deformation mechanisms of a metastable Ti-5Al-5Mo-5V-1Cr-1Fe (Ti55511) alloy with a basket-weave structure were investigated. HCF and its deformation mechanisms of a Ti55511 alloy were systematically studied in the deformed condition by using a scanning electron microscope (SEM), a transmission electron microscope (TEM), and electron backscatter diffraction (EBSD). It was found that the Ti55511 alloy exhibited an excellent HCF strength (10 In addition, the basket-weave microstructure is the most frequently used microstructure in titanium alloys due to its excellent fracture toughness and crack propagation resistance [The Ti-5Al-5Mo-5V-1Cr-1Fe (Ti55511) alloy is a typical metastable β titanium alloy with high strength and excellent fracture toughness . Therefosistance . Correspt and stress ratios R) [t. Other studies also reported the HCF properties of Ti55511 variant alloys  with different microstructures. Huang et al. explored the HCF properties of the Ti55531 alloy with bimodal and lamellar microstructures and revealed the crack initiation behaviors and α phase deformation mechanism [<a> and <c+a> slip systems, which can be activated depending on the deformation condition. During the HCF process at microscale, however, relevant studies have rarely been performed and are urgently required to determine the deformation mechanisms in the Ti55511 alloy.As for aviation applications, high-cycle fatigue (HCF) is a critical property of structural components made with titanium alloys with a basket-weave microstructure , consideatios R) . The resechanism . The effechanism . The micp and αs phases in the Ti55511 alloy under HCF loading were systematically studied using SEM, TEM, and EBSD techniques. This work is expected to provide theoretical and experimental support for understanding the dislocation slip mechanism of the basket-weave structure in β-type titanium alloys during HCF deformation.Herein, the deformation mechanism of the Ti55511 alloy with a basket-weave structure under HCF loading was investigated. In detail, the deformation behaviors of the αβ) was about 865 °C, which was measured by the metallographic method. The metallographic method determining the β transus included microscopic observation of the phases present in heat-treated and quenched samples from temperatures below and above β transus. As shown in The Ti55511 alloy was produced by Hunan Xiangtou Goldsky New Materials Co., Ltd. The chemical composition of the Ti55511 alloy was determined to be Ti-5.10Al-4.98Mo-4.95V-1.02Cr-0.98Fe-0.15O-0.005N (wt%). The β transus temperature (T3 + 95 vol% H2O). The TEM sample was prepared by ion milling . The EBSD sample was polished using a vibration polisher . The accelerating voltage, current, and step size during the EBSD test were set at 20 kV, 2.4 mA, and 2.0 μm, respectively. Channel 5 software  was used for EBSD data processing.Microstructure characterizations were carried out using optical microscopy , SEM  with EBSD , and TEM . The OM and SEM samples were ground and polished by using SiC paper to 3000 mesh and then etched with Kroll’s agent  were performed on an MTS Landmark fatigue machine in air with R = 0.06, Kt = 1, and a frequency of 100 Hz, according to the ISO12107 standard. The size of the standard HCF samples is presented in Tensile tests were conducted on an Instron 8801 machine  equipped with an extensometer. In addition, standard M10 cylindrical tension test samples were cut according to the ASTM E8/E8M-16a standard, as shown in p lamella, and β transformed microstructures (βtrans). The GBα was discontinuous, which ensured the adequate ductility of the Ti55511 alloy. The SEM secondary electron image in p phase. According to the statistical results, the average width of the αp lamella was 1.08 μm. In addition, the βtrans microstructures were recognized among the αp phases, composed of retained β and αs phases. In s with the length ranging from 200 nm to 700 nm. The corresponding STEM-EDS results indicated that the Al (α stabilizer) mainly diffused into the α phase  was fitted to be 738 MPa. Some researchers have proposed that the HCF limit is closely related to the yield strength [y (1190 MPa), with a σmax /σy ratio at 0.48. In contrast, the Ti-6Al-4V alloy had a σmax /σy ratio at 0.42 (σmax: 375 MPa and σy: 915 MPa) [max /σy ratio [max/σy value of the metastable β Ti alloys  was 0.49~0.507 [max/σy reached 0.672, which proved that the Ti55511 alloy with a basket-weave microstructure had excellent HCF properties.The engineering stress–strain curves of the Ti55511 alloys are provided in strength ,14,15. A49~0.507 ,14,15. I6 cycles) were characterized with SEM at different magnifications, as shown in p boundary (which was considered a weak point) when the crack source appeared inside the sample (rather than on the surface). In addition, many fatigue striations and secondary cracks were observed in Region A, as displayed in p phase. Therefore, it is proposed that the striations were mainly generated in the αp lath. Furthermore, enormous dimples appeared in Region B  are presented in p, according to p is a relatively soft phase, and cracks driven by fatigue loading would firstly activate at the αp/βtrans interface [p phase provided enough room for dislocation slipping. Therefore, massive dislocations were generated and entangled in the αp phase. In addition, the dislocation structure was also recognized in the fine αs phase, demonstrating that the αs phase did bear part of the deformation. In order to analyze the crack initiation behavior, the cross section of the fatigue specimen  near the crack initiation sites was characterized by SEM, as shown in p/βtrans interface during fatigue deformation. For the basket-weave structure, previous studies have observed crack initiation at the αp/βtrans interfaces [The TEM images of the Ti55511 fatigue specimen . Since the deformation of the Ti55511 alloy was mainly provided by the αp phase under the HCF test process, we only focus on the deformation mechanism of the αp phase in this work. The EBSD results of the AOI are shown in p phase was randomly distributed on the β matrix, which was consistent with the OM and SEM results. The EBSD kernel average misorientation (KAM) map is a powerful tool to probe local lattice distortion and qualitatively evaluate deformation uniformity. p phases with random orientations exhibited various degrees of deformation. Indeed, the inverse pole figures (IPF), shown in To investigate the microscale deformation mechanisms of the Ti55511 alloy under fatigue loading, EBSD was employed to analyze the fatigue specimen  <11–20>, prismatic (10–10) <11–20>, and pyramidal (10–11) <11–20>, while the <c+a> type slip system contained (10–11) <11–23>. The activation of the slip systems can be determined by the Schmidt law, i.e., once the critical resolved shear stress (τcrss) of a specific slip system is exceeded by the resolved shear stress on the slip plane along the slip direction, the initiation of yielding in a crystal takes place [crss can be calculated by the Equation (1) [y is the yield strength of the material, and m is the Schmidt factor. In order to investigate the activation of the slip systems in the Ti55511 alloy, the Schmidt factor of different slip systems was calculated, as shown in crss for different slip systems of the Ti alloys reported by previous studies are summarized in <a> slip system, the statistical results showed that 78% of the αp grains exhibited the values of the Schmidt factor at above 0.4. In comparison, the fraction of the αp grains with a Schmidt factor at above 0.4 in the basel <a> and pyramidal <a> were 40% and 60%, respectively. More importantly, the τcrss of the prismatic <a> slip system (~375 MPa) was slightly lower than that of the basel <a> slip system (~387 MPa) and was much lower than that of the pyramidal <a> slip system (~400 MPa), as listed in <a> slip system was preferentially activated during the HCF deformation in most grains. The fraction of grains with basal and pyramidal <a> activation should be lower than prismatic <a>, as it had a slightly higher CRSS and lower volume fraction of grains with a Schmid factor above 0.4. Furthermore, the number of αp grains with a Schmidt factor >0.4 in the pyramidal <c+a> was identified as 77%, but the relatively high τcrss (628 MPa) made it difficultto activate the pyramidal <c+a> slip system.The various slip systems for the α titanium were es place . Accordip lamellae, and a βtrans structure. The average width of the αp was 1.08 μm, and the length of the αs ranged from 200 to 700 nm.The basket-weave structure of the Ti55511 alloy consisted of discontinuous GBα, αThe Ti55511 sample in this work exhibited an average ultimate tensile strength of 1119 MPa, a yield strength of 1098 MPa, an elongation of 13.8%, and an ultimate strength of HCF of 738 MPa.p phase, i.e., dislocations slip within the αp phases. In addition, dislocation pile-up at the αp/βtrans interface induced stress concentration and microcrack initiation.The deformation mechanism of the Ti55511 during HCF was mainly determined by the dislocation slipping of the α<a> type slip system of the αp phase was preferentially activated during the HCF deformation process of the Ti55511 alloy, followed by the basal <a> and pyramid <a> systems, while the pyramid <a+c> slip was very difficult to activate.The EBSD analyses indicated the prismatic In this work, the basket-weave structure in the Ti55511 alloy was obtained by the STA heat treatment strategy, and the corresponding HCF properties were systematically studied. The main conclusions are as follows:


Reducing the local stress in the vicinity of the grain boundaries is a favorable way to improve the super-elastic properties of super-elastic alloys. The crystal plasticity finite element method (CPFEM) was applied in this study to simulate the deformation behavior and local stress of a super-elastic Cu-8Al-11Mn (wt.%) alloy containing single grains with various orientations, columnar grains with different misorientation angles, and tri-crystals with distinct grain boundary morphologies. The results indicated that the stress distribution of single grains presented obvious orientation dependence during deformation. Uniformly distributed stress was observed in grains with orientations of 0° and 90° when more slip systems were activated during deformation. With the increase in the misorientation angles of columnar grains, the stresses in the vicinity of the grain boundaries increased, which was related to the difference in the shear stress of the slip systems in adjacent grains. When the difference in the shear stress of the slip systems in two adjacent grains was large, a local stress concentration formed in the vicinity of the grain boundary. Compared with the triple-junction grain boundaries, the local stresses of the straight and vertical grain boundaries were smaller, which was closely related to the number of activated slip systems on both sides of the grain boundary. The above results were obtained experimentally and could be used to design super-elastic alloys with high performance. Cu-based super-elastic alloys (SEAs) have the advantages of a low cost, good super-elastic (SE) properties, and excellent electrical and thermal conductivity, and they have broad application prospects for shock absorption and noise resistance in engineering structures, machinery, architecture, automobiles, high-speed rail, aerospace, and other fields ,2. Cu-baThe super-elastic properties of SEAs are affected by orientation ,4,5, misIt is well-known that the super-elastic properties (such as the recovery strain) of SEAs mainly come from stress-induced martensitic transformation and its inverse transformation . In ordeUntil now, the local stress at the grain scale was difficult to measure and characterize by experiments, so numerical simulation methods were mostly used, which included molecular dynamics  and the In this paper, the crystal plastic finite element method (CPFEM) was applied to analyze the local stress state of a Cu-8Al-11Mn alloy with different crystallographic orientations, misorientation angles, and morphologies, and the deformation mechanism of local stress was systematically investigated.Crystal deformation is caused by lattice distortion and rotation due to dislocation sliding along a slip direction on a slip plane. A single dislocation slip produces displacement discontinuity in the grain, which is difficult to express by a continuous deformation gradient. However, due to the large number of dislocations in the grain, from the mesoscopic point of view, it can be assumed that the slip is uniform in the crystal, and the whole crystal can be regarded as a continuous medium, so dislocation slip can be described by the field variable deformation gradient tensor of the continuous medium. Lattice distortion can be regarded as the elastic deformation of the continuous medium and can be approached with the method of elasticity. The deformation of grains under an external load consists of two parts: the elastic deformation caused by lattice distortion and rotation, and the plastic deformation caused by dislocation sliding along the slip system. These two components occur simultaneously during grain deformation. For the convenience of mathematical description, crystal deformation can be divided into two steps: first, the plastic deformation caused by the dislocation slip; second, the elastic deformation produced by lattice distortion on the basis of plastic deformation. Therefore, it is necessary to add an intermediate configuration between the pre-crystal-deformation reference configuration and the current post-deformation configuration to describe the whole deformation process of the grain, as shown in F is the deformation gradient, x is the position coordinate of the material point in the current configuration, and X is the position coordinate of the material point in the reference configuration. The total deformation gradient can be decomposed into the deformation gradient caused by crystal slip according to the above analysis FP and the elastic deformation gradient caused by lattice deformation and the rigid rotation of the crystal F*:According to the continuum theory, the total deformation gradient from the reference configuration to the current configuration is:α) in a crystal is expressed by the normal vector α, which are unit vectors and satisfy the orthogonal relation:From the lattice in α, N is the total number of slip systems, The plastic deformation gradient tensor is expressed as:ατ and the shear strain rate α; n is the strain rate sensitivity coefficient; αg is the current strength of slip system α; α; The rate-dependent hardening model based on an exponential function proposed by Asaro and Needlem  was usedαβh is the hardening coefficient when α = β; αβh is the self-hardening coefficient ααh, when α ≠ β; αβh is the latent hardening coefficient; αβq is the scale factor; h0 is the initial hardening modulus; sτ is the saturated shear stress; τ0 is the initial critical shear stress; and γ is the cumulative shear strain on all slip systems.L is the instantaneous elastic modulus tensor, and De is the elastic part of the deformation rate tensor.The elastic properties during deformation are not affected by plastic deformation, so the following elastic constitutive equation could be adopted:D is the strain rate tensor; Wα are the symmetric and antisymmetric sections of the Schmid factor, respectively; and σ is the Cauchy stress tensor.Furthermore, the plastic constitutive relation of a single crystal can be described as:11 = 139.329 GPa, C12 = 115.868 GPa, and C44 = 102.718 GPa. The lattice stability criterion should be satisfied, whereby C11 − C12 > 0, C11 + 2C12 > 0, and C44 > 0. The Poisson’s ratio constant of μ is 0.336. The β phase of Cu2AlMn is a L21-type superlattice structure, whose main slip system is {111} <110>, which is a face-centered cubic (FCC) crystal system. Detailed information regarding the slip systems is shown in Cu-8Al-11Mn (wt.%) alloys have a high anisotropy coefficient of elastic constants, i.e., CIn order to determine the hardening parameters of the slip systems, a cube with a size of 6 mm × 6 mm × 6 mm was built, as shown in −1, and the ratio of self-hardening and latent-hardening parameters was 1. The results of the simulation and experiment are shown in The hardening parameters of the slip systems of the Cu-8Al-11Mn alloy are shown in σ was defined as θ, which ranged from 0° to 90° at a step of 15° to represent single grains with distinct orientations. In order to study the effect of the misorientation angles of adjacent grains on local stress, a plastic geometric model of columnar grains with misorientation angles of 5°, 10°, 15°, 30°, 45°, and 60° was established. That is, both low-angle grain boundaries (5°–15°) and high-angle grain boundaries (15°–60°) were considered in the models. These models were meshed by C3D8R elements with a size of 2 mm × 2 mm × 2 mm. A tensile displacement in the Y direction on the upper surface of the single-crystal, columnar-grain, bamboo-like-grain, and equiaxed-grain models was applied with a deformation of 8%. The displacement in the X and Z directions was set to 0. Meanwhile, the displacement in the X, Y, and Z directions on the bottom surface of the cube was set to 0. The step time was 0.001, and the time period was 1.Based on the material parameters in A tracking path was inserted into the middle of the single crystals, and the results are shown in In order to show the effective stress distribution inside the grain and in the vicinity of the grain boundary more clearly, a tracking path (A-B) was inserted, which passed through the middle of the three columnar grains, as shown in It is well known that the stress during grain deformation is closely related to the activated slip systems, which are considerably affected by the grain orientation and loading direction. In order to describe the relationship between orientation and loading direction, the Schmid factor (SF) values of the slip systems for each oriented grain were calculated, as shown in α at time t, α at time t, and t.In addition, the activated slip systems of each grain orientation during tensile deformation were calculated. The relative activity of the slip system was calculated as shown in Equation (17).The relative activity of the slip system during the tension process of each grain is shown in As shown in From the shear stress results of the grains with a misorientation angle of 45° , it is cThere are five independent constraints on the grain boundaries of ordinary polycrystals during deformation. Complex grain boundary deformation can be coordinated only by activating five independent slip systems. When the number of activated independent slip systems of the alloy is less than five, polycrystals experience incongruous grain boundary deformation, which easily leads to stress concentration and even cracking along the grain boundary. When the grain boundary has a straight morphology, such as in BLGs or CGs, the constraint conditions at the grain boundary during deformation are reduced to three, that is, only three slip systems need to be activated to ensure good deformation coordination. Similarly, for stress-induced phase transformation materials, a straight grain boundary morphology is conducive to the coordinated deformation and stress-induced phase transformation of the grains on both sides of the grain boundary and reduces the possibility of intergranular cracking due to stress concentration around the grain boundary. Ueland et al.  studied (1)The Schmid factor and number of activated slip systems played an important role in the stress value and distribution of single crystals. The effective stresses in the 0 ° and 90 ° grains with a high number of activated slip systems were smaller and more uniform than in the 15°, 30°, 45°, 60°, and 75° grains.(2)When the grain boundaries of columnar grains had low misorientation angles, such as 5~15°, the local stresses in their vicinity were smaller and more uniform, with small difference in the shear stresses of the activated slip systems between the adjacent grains. However, the local stresses in the vicinity of the grain boundaries with high misorientation angles, such as 30~60°, increased sharply due to the large difference between adjacent grains in terms of the shear stresses of the activated slip systems.(3)The grain boundary morphologies had a great influence on the local stress distribution of the polycrystalline grains. The local stress of the triple-junction GBs in the EGs was the largest, while that of the transverse GBs in the BLGs was the smallest, which was mainly related to the number of activated slip systems in the vicinity of the grain boundaries.The crystal plastic finite element model (CPFEM) was used to analyze the influence of crystallographic orientations, misorientation angles, and grain boundary morphologies on the local stress distribution of a Cu-8Al-11Mn alloy subject to tensile deformation, and the deformation mechanism of local stress formation was revealed. The specific conclusions are as follows:


Transformer-based object detection has recently attracted increasing interest and shown promising results. As one of the DETR-like models, DETR with improved denoising anchor boxes (DINO) produced superior performance on COCO val2017 and achieved a new state of the art. However, it often encounters challenges when applied to new scenarios where no annotated data is available, and the imaging conditions differ significantly. To alleviate this problem of domain shift, in this paper, unsupervised domain adaptive DINO via cascading alignment (CA-DINO) was proposed, which consists of attention-enhanced double discriminators (AEDD) and weak-restraints on category-level token (WROT). Specifically, AEDD is used to aggregate and align the local–global context from the feature representations of both domains while reducing the domain discrepancy before entering the transformer encoder and decoder. WROT extends Deep CORAL loss to adapt class tokens after embedding, minimizing the difference in second-order statistics between the source and target domain. Our approach is trained end to end, and experiments on two challenging benchmarks demonstrate the effectiveness of our method, which yields 41% relative improvement compared to baseline on the benchmark dataset Foggy Cityscapes, in particular. As the fundamental task of computer vision (CV), object detection, which involves two sub-tasks: classification versus regression, is widely used in automatic driving , face reDeep neural networks training is extremely dependent on external manual annotation data whose training set and validation set are supposed to be independent and identically distributed. Data labeling is time-consuming and the process can be costly; while some public benchmarks ,20 alreaThis work aims to train DINO on the labeled source domain so that it can be applied to the unlabeled target domain, as shown in To tackle the above problem, a novel cascading alignment strategy was proposed for learning domain-invariant features and applying them to the DINO; then, cascading alignment DINO (CA-DINO), a simple yet effective DETR-like detector, was further designed. CA-DINO consists of two key components: attention-enhanced double discriminators (AEDD) and weak-restraints on category-level token (WROT). Concretely, AEDD contains two parameter-independent discriminators with attention enhanced, which act on the second-last and third-last layer of the backbone, respectively, to learn the domain-invariant features via adversarial training. The backbone containing domain-invariant features is of great help to the unsupervised training of the encoder and decoder, because usually the decoder is more biased towards the source domain with supervised training. A well-aligned backbone could guide the transformer encoder and decoder during training. Compared to the original discriminator, the capacity of discrimination between two domains is considerably improved by AEDD, which makes it less conceivable it will be easily deceived. The introduction of two discriminators for adversarial training leads to instability in training. It makes it difficult for the model to converge in the right direction, making both fine tuning and end-to-end training challenging. Motivated by these findings, a weak constraint based on the statistical method was proposed to regularize the category-level token produced by the transformer encoder and decoder and increase their discriminability for robust object detection.Overall, the collaboration of these two components results in the proper alignment of domain-invariant features. Compared to other models, our method produced superior outcomes and experiments on two challenging benchmarks, demonstrating that our strategy considerably improves the cross-domain performance of DINO and outperforms various competitive approaches.We observe that a weak discriminator is a primary reason why alignment of feature distribution on the backbone yields only modest gains and propose AEDD. It directly scopes the backbone to alleviate the domain gaps and guide the ascension of the cross-domain performance of the transformer encoder and decoder.A novel weak-restraints loss is proposed to regularize further the category-level token produced by the transformer decoder and boost its discriminability for robust object detection.Extensive experiments on challenging domain adaptation scenarios verify the effectiveness of our method with end-to-end training.The main contributions of this paper are as follows:Object detection is a crucial challenge in CV. Representative object detectors based on deep learning may be broadly classified as either two-stage or one-stage approaches. Specifically, in two-stage detectors such as Faster RCNN , a regioTypically, the performance of these models is significantly influenced by hand-designed components, such as anchor generation, for which prior knowledge about the task needs to be explicitly encoded alongside non-maximum suppression . To simpLike other DETR-like models, DINO generally consists of three parts: the backbone for extracting low-level features, the transformer encoder and decoder for modeling sequence features, and multiple prediction heads for making predictions.Given an image, the backbone extracts the representation of multi-scale features DINO uses the L1 loss  and GIOUDomain-adaptive object detection, which seeks to train the detector on the source domain and then apply it to the target domain, has attracted growing interest in recent years. As the pioneering work in adapting domain-adaptive techniques to object detection, DA Faster R-CNN  proposesWith the extensive use of a transformer in object detection, the DETR-like domain-adaptive object detector has also produced some remarkable outcomes. SFA  proposesIn this paper, CA-DINO adopts adversarial learning as the primary mechanism and aims to improve the cross-domain performance of DINO, which is still unexplored.Domain-invariant features from the backbone are essential for detection transformers to alleviate the domain shift problem. As in Deformable DETR, DINO applies the multi-scale backbone features to enhance the detection performance for small objects. The structure of AEDD is shown in P in , To distinguish the feature distribution between source and target domains in different perspectives, the backbone was made to learn domain-invariant representations to fool the discriminator; the features of different domain e (CBAM)  includede (CBAM) . After ad is the domain label, which values 0 for source domain and 1 for target domain. Both source source and target source The standard adversarial formulation N denotes Deep CORAL  is a simTo summarize, the final training objective of CA-DINO is defined as:In this section, comprehensive experiments on many cross-domain object-detection scenarios demonstrate the effectiveness of CA-DINO. Ablation studies and visualization analysis validate that our design makes DINO capable of detection in the target domain.In these experiments, the following three public datasets will be employed: Cityscapes , Foggy CCityscapes  has a suFoggy Cityscapes  is a synSim10k  is a synBased on these datasets, these experiments evaluate CA-DINO under two widely used adaptation scenarios: (1) Normal weather to Foggy weather (Cityscapes→ Foggy Cityscapes), where the models are trained on Cityscapes and validated on Foggy Cityscapes, which aims to test domain adaptation across different weather conditions; and (2) synthetic scene to real scene (Sim10k→ Cityscapes), where Sim10k is used as source domain and Cityscapes is used as the target domain, which evaluates the shared category “Car”. Following previous works, the paper reports the results of mean average precision (mAP) with a threshold of 0.5.By default, ResNet-50   end-to-end. The software configuration adopted the deep-learning framework PyTorch 1.9, CUDA version 11.1, and Python 3.8.13. Taking Cityscapes→ Foggy Cityscapes as an example, it took about 14 h to train the model with 50 epochs.In this experiment, the Cityscapes dataset (source domain)  was usedThe comparisons of results with other methods are reported in We used the SIM10k as the source domain and the Cityscapes as the target domain to adapt synthetic scenes to the real world. The only common category between SIM10K and Cityscapes is the car. In this section, we conduct exhaustive ablation experiments on Cityscapes→ Foggy Cityscapes to determine the effect of different components in our method by adding components to DINO and comparing components before improvements as shown in First, by adding WROT, the mAP achieved a + 4.1% improvement. Then the simple single discriminator was added without involving an attention mechanism on the penultimate layer of the backbone; it outperforms the last one, significantly, which indicates that discriminator does help align the distributions. Further, we introduce the channel attention module to this discriminator, and the mAP is +1.3% higher than this module without attention. In addition, we separately introduce the spatial attention module on the discriminator again, which raised the mAP to 46.4. As demonstrated by the preceding results, by introducing an attention mechanism to enhance the performance of the discriminator, the discriminator is less susceptible to being deceived and the detector can learn domain-invariant features better during the adversarial learning process. Afterwards, introducing CBAM which contains a spatial-attention module and channel-attention module to the single discriminator, the mAP is +3.1% higher than the discriminator without attention and mAP reaches 48.6. By adding another discriminator with attention-enhanced for united alignment, we reach our proposed method, which yields the best performance. At the same time, we also implemented the AEDD-only version, which is slightly worse than the final model.To verify that our proposed model is effective, we visualized some detection results by DINO , SFA 4040 and CATo analyze why cascading alignment improves the detection performance, we visualize the class activation mapping  of backbThe outstanding performance is primarily attributed to our designed AEDD, which captures more context features at the image level. Therefore, t-SNE  is utiliAdditionally, we attempted to implement three attention-enhanced discriminators on the backbone, and the experiments revealed that not only did we not obtain more excellent performance, but the training time was also extended. Then, we experimented with the optimal placement of these two discriminators and discovered that this has a lower influence on performance than hyperparameter adjustment. Thus, we chose the present strategy with fewer parameters. For the study, we chose CA-DINO based DINO-4scale. The parameters have 52.4 M, which includes 47 M for DINO and 5.4 M for AEDD. WROT does not contain parameters. It is noteworthy that the methods we proposed are only involved in the training stage and do not take part in inference, which allows us to infer the images at the same theoretical speed as the standard DINO, which runs at 24 FPS, similar to Faster R-CNN-FPN with the same backbone.Segmentation ,50 has aIn this paper, we were devoted to enhancing the cross-domain performance of DINO for unsupervised domain adaptation. Specifically, CA-DINO includes attention-enhanced double discriminators (AEDD), which are proposed to extract more domain-invariant features and weak-restraints on category-level token (WROT) for minimizing the difference in second-order statistics between the source and target domain. Numerous experiments and ablation studies have also demonstrated the effectiveness of our method. Although CA-DINO has excellent performance, one GPU can only carry one batch in the experiments. Our method requires more memory than previous work and takes longer to train. The introduction of WROT largely alleviates the instability brought by adversarial training. However, the model’s training is still accompanied by a slight perturbations in some scenarios, which makes the adjustment of hyperparameters particularly difficult. Balancing performance and stability is the next important direction for us to explore.


A comprehensive study was performed of the deformation and temperature behavior during quasi-static tension, as well as the peculiarities of accumulation and dissipation of energy during plastic deformation. Microstructural analysis at the pre-fracture stage of pure titanium and Ti-45Nb alloy in the coarse grain (CG) and ultrafine-grained (UFG) states was also conducted. It was shown that substructural and dispersion hardening leads to a change in the regularities of dissipation and accumulation energies during deformation of the samples of the pure titanium and Ti-45Nb alloy in the UFG state. Some features of structural transformations during deformation of the pure titanium and Ti-45Nb alloy samples in the CG and UFG states were studied. A band and cellular-network and fragmented dislocation structure was formed in the case of the CG state, while large anisotropic fragments were formed in the UFG state, thus specifying a local softening of the material before fracture. The development of modern technologies imposes ever-increasing requirements for improving the functional properties of new structural materials. Currently, titanium and titanium-niobium alloys are the key materials and, in many cases, non-alternative materials for strategic industries such as aircraft and rocket engineering, nuclear power engineering, shipbuilding, medicine, food and chemical industries, electronics, etc. ,2,3.In medical applications, the materials widely used for manufacturing implants are “pure” α-titanium  and (α + β)-medium-strength titanium alloys , whose elastic modulus is in the range of 100–120 GPa, which is much higher than the elastic modulus of the cortical bone tissue (10–40 GPa) ,9,10,11.One of the ways to solve this problem is the use of severe plastic deformation (SPD) . During Over the past decades, significant efforts of researchers in many countries have been directed to studying the microstructure and properties of NS and UFG materials ,28,29,30Relatively new for medical applications are Ti-(40–45) wt.% Nb alloys, which have been studied to a fairly lesser extent ,33,34,35When developing designs and products for medicine and engineering, it is necessary to analyze the processes of plastic deformation and fracture. It is known that the deformation and fracture of materials are characterized by an increase in the temperature of the plastically deformed material that determines the thermoplastic effect . Energy The infrared (IR) thermography  has beenAt the same time, it should be noted that peculiarities of analyzing deformation heat generation in UFG metals by means of IR thermography have not been well reflected in the literature ,46,47.A deeper review of the world publications in the field is beyond the scope of this paper. However, it is worth noting the works ,45,48,49The peculiarities of temperature distributions during deformation under quasi-static stretching of samples of bioinert alloys VT1–0 titanium, Zr–1Nb and Ti–45Nb in CG and UFG states, including those with structural deviations from “typical samples”, were studied elsewhere in order to identify thermal precursors of deformation and destruction . It was xxε, yyε, xyε in the work zone during stretching of Zr-1Nb alloy samples in the CG and UFG states, are associated with substructural hardening of the matrix phase α-Zr under SPD. In the recent work , the feaA drawback of the studies above is the need to study the microstructure, that is formed during deformation loading, and to establish the relationship between the parameters of the deformation structure and the physical and mechanical characteristics of the materials during stretching.The aim of the present work is to comprehensively analyse the deformation and temperature evolution of alloys under quasi-static tension, as well as the patterns of energy accumulation and dissipation during plastic deformation. The microstructure features after deformation at the pre-fracture stage of VT1-0 titanium and Ti-45Nb alloy in the CG and UFG states have also been investigated.It should be noted that the novelty of the present work is in the development of a novel approach based on the analysis of deformation, temperature curves and microstructure features in the “neck” formation zone at the stage of pre-fracture. Such approach has allowed to investigate the influence of the UFG structure on the deformation and temperature processes during tension and identify the features of the microstructure after deformation of titanium and Ti-45Nb alloy. This allows a better understanding of the physical mechanisms of deformation and fracture of UFG materials.abc-pressing with multi-pass rolling followed by pre-recrystallization annealing at 300 °C [Commercially pure titanium  in the UFG state, as well as the Ti-45 wt.% Nb (Ti-45Nb) alloy in CG and UFG states, were investigated. The UFG state in pure titanium and Ti-45Nb alloy was formed by using the combined SPD method, which included the t 300 °C . To form−1 were performed on an Instron VHS40/50-20 test machine . The FLIR SC7700M  thermal imaging system was used to measure sample surface temperature, as well as to evaluate sample size and appearance of a deformation “neck” directly on IR thermograms. This allowed to obtain the temperature Δtrue)T(ε and true deformation trueσ(trueε) curves and determine the work of plastic deformation pA(trueε), the specific amount of the heat dissipated during deformation (Q), and the energy stored during deformation (Es). The respective calculations were described in [The uniaxial tensile mechanical tests of flat samples with a constant strain rate of 0.01 sribed in .maxε is the maximum deformation of the sample before fracture.The total specific work of deformation was estimated using the true strain curves asp)A is defined asA is the total mechanical deformation work, and eA is the elastic deformation work.The plastic deformation work (Q) was estimated using the parameters estimated thermographically:c is the heat capacity of the sample material, m is the sample mass, V is the sample volume, and ΔT is the average temperature change on the sample surface under deformation.The specific quantity of the heat dissipated during deformation (Es) is defined asThe energy stored during deformation . The average size of structural elements  was determined by the secant method .In the CG Ti-45Nb alloy, the microstructure is represented by matrix subgrains of the β-phase e, and thIn the DF image obtained in the reflection for the ω-phase σ curves obtained by considering the neck formation and temperature curves true)T = dσtrue/dεtrueθσ (true(εtrue)σ for CG titanium has an ascending parabolic section, which turns into a descending section with the θ coefficient equal to 5 GPa (true)T a. The cutrue~0.04, indicating its ability to effectively store thermal energy during deformation. With further deformation of UFG Ti, the temperature rises rapidly and reaches ΔT~50 °C before fracture in the neck formation zone, which is accompanied by an inflection in the Δtrue)T GPa, thus indicating local softening of the material before fracture  and (−20) GPa for the CG and UFG states respectively , the amount of released heat (Q), and the energy stored during plastic deformation (Es). The limiting specific work of plastic deformation was 110 MJ/m3 and 95 MJ/m3 for pure titanium and Ti-45Nb alloy in the CG state respectively, and 85 MJ/m3 and 48 MJ/m3 for these alloys in the UFG state  is stored by the metal.The analysis of the dependences above shows that the energy 57 MJ/m3 . The remQ released as a result of deformation is uneven at different stages of deformation. At the initial stage of deformation, until εtrue ~0.04, almost 100% of Ap is stored by the material and turns into the internal energy. Then the dependencies true)Q(ε and true)Es, and the remaining ~30% of the energy (25 MJ/m3) is stored by the metal . The remaining 60% of the energy (55 MJ/m3) is stored by the material. A different picture is observed during plastic deformation of the Ti-45Nb alloy, both in the CG and in the UFG states. At the initial stage of deformation of Ti-45Nb in the CG and UFG states until Q increases abruptly and reaches 32 MJ/m3 that is about 70% of Ap. Accordingly, Es for the UFG Ti-45Nb alloy drops sharply and amounts to ~30% of Ap and is 16 MJ/m3 respectively (In the UFG Ti-45Nb alloy, ectively , Table 2Before the fracture of UFG titanium, the bands seem to be predominant in the microstructure. The size of the resulting bands in the cross section slightly decreases and drops to 0.3–0.5 µm, whereas their length reaches 1 µm a,b. The It is worth noting that, in comparison with the CG titanium, in the UFG state, along with the band structure, one can also observe anisotropic fragments, i.e., local large volume regions with multidimensional misorientation boundaries c. These Before fracture, the structure of the Ti-45Nb alloy in the CG state is rather heterogeneous . Two typAnother type is a fragmented structure, which is formed as a result of further development of the band structure. With increasing magnitude of local internal stresses, the band splits into fragments, and a dislocation cellular-network structure with misoriented volumes is formed inside . As the In the Ti-45Nb alloy in the UFG state, the microstructure at the pre-fracture stage is of a predominantly fragmented character with low-angle and high-angle boundaries a,b, likeThe data obtained as described above was used to compile a classification of the substructures formed during tension of the studied alloys in the CG and UFG states .The main distinguishing feature of structural transformations of the pure titanium samples and Ti-45Nb alloy in the UFG state before fracture, in comparison with the CG state, is the formation of large regions with a cellular-network dislocation substructure and anisotropic fragments. At the same time, during the deformation of the investigated alloys in the CG state, the band and fragmented structures are mainly formed. The formation of large anisotropic fragments also takes place, indicating a local softening of the studied alloys before their fracture.Thus, the results above indicate that substructural and dispersion strengthening and the formation of the α-phase have a significant influence on the deformation and temperature behavior of the pure titanium and the Ti-45Nb alloy, as well as the patterns of energy accumulation and dissipation during plastic deformations and the type of the emerging substructure during deformation.A comprehensive analysis of the microstructure and deformation, as well as the corresponding temperature dependences, demonstrates a significant influence of the UFG state on the regularities of energy accumulation and dissipation during plastic deformation of pure titanium and Ti-45Nb alloy.trueε ~0.04, at which the total energy of plastic deformation is stored by the metal.It has been found that substructural hardening in UFG pure titanium under SPD leads to changes in deformation, thermal behavior, and energy accumulation and dissipation during deformation. The thermal and energy curves for UFG pure titanium have a plateau (stage up to ω-phase particles and the α-phase formation in the UFG Ti-45Nb alloy reduces the influence of the UFG structure on the deformation and thermal behavior, especially at the initial stage of deformation. At this stage, up to trueε ~0.05, almost the total energy of plastic deformation is stored by Ti-45Nb in both the CG and the UFG states.The dispersion strengthening by the The main distinguishing features of structural transformations during deformation of samples of pure titanium and Ti-45Nb alloy in CG and UFG states were discovered in this study. A band and cellular-mesh and fragmented dislocation structures were formed in the case of the CG state, while large anisotropic fragments were formed in the UFG state. Thus, it indicates a local softening of the material before its fracture.For a deeper understanding of the deformation mechanisms of pure titanium and Ti-45Nb alloy, it is necessary to investigate the evolution of the microstructure at various stages of plastic deformation. This is the subject of future research.