Tandem duplication (TD) represents a crucial type of structural variations within the human genome. When the sequencing depth is low, TD signal at each single nucleotide position becomes more indistinct. So the detection of TDs under low coverage remains a challenging task. This paper proposes a method called TD-TSPS (Two-Step Progressive Segmentation for TD detection) on whole genome sequencing data. A two-step progressive segmentation strategy is employed to divide the genome into continuous and similar bins. Additionally, it integrates split read and paired-end mapping strategies to refine TD regions. Performance tests on simulated and real datasets show that TD-TSPS achieves a good F1-score. Therefore, it can be used as an effective tool for TDs detection.

Background Copy number variation (CNV) refers to the duplication or deletion of DNA sequences in the genome. It plays an important role in organism development and disease occurrence. Methods Read-depth (RD)-based strategies are among the most widely used approaches for CNV detection; however, RD signals typically allow only approximate breakpoint localization. In contrast, split-read (SR) methods can provide much more precise breakpoint resolution. As a result, hybrid RD+SR strategies have become increasingly popular in recent CNV detection frameworks. Nevertheless, these hybrid methods still exhibit limitations, particularly in detecting CNVs of diverse lengths, where performance may degrade for very short or very long variants. A new CNV detection method called SOSCNV (Detection of Copy Number Variations based on Stochastic Outlier Selection) is proposed in this article. In order to enhance the recognition and processing ability of those difficult-to-detect areas, and also to make the detection of breakpoints more accurate, SOSCNV incorporates a two-stage segmentation technique. SOS algorithm is used to model the RD signal and Guanine and Cytosine (GC) content, and calculate the correlation between datasets to identify anomalous datasets. Compared with traditional RD+SR hybrid detection strategies, SOSCNV is better suited for identifying CNVs across a wide range of variant lengths and demonstrates stronger robustness under varying sequencing depths and tumor purities. Results After testing on 900 simulated datasets and four real datasets, and comparing with four existing CNV detection tools, experiments show that SOSCNV can significantly improve detection precision, recall and F1-score.

Copy number variation (CNV), as a major type of DNA structural variations (SVs), plays a key role in causing human diseases and contributing to genetic diversity. Accurate identification of CNVs is significant for disease mechanism analysis, personalized diagnosis and treatment, and drug development. Although next-generation sequencing (NGS) technology has greatly promoted the development of CNV detection methods, the existing methods generally have problems such as high false positives and inaccurate boundaries. Therefore, a new method is proposed for detecting CNVs in a single sample of NGS data, called CNV-ECOD. The method first employs the empirical-cumulative-distribution-based outlier detection (ECOD) algorithm to identify abnormal signals of read depth (RD) for preliminary detection of CNVs. To correct false positives and refine CNV boundaries further, it integrates paired-end mapping (PEM) and split read (SR) strategies. The integration of the RD-PEM-SR hierarchical progressive framework and the anomaly scoring mechanism based on ECOD can effectively improve the accuracy of CNV detection. Comparing our approach to four peer methods, simulation results demonstrate that it achieves the best balance between precision and sensitivity. Also, the proposed method has the best F1-scores and the highest overlap density scores (ODSs) in real-sample experiments. Therefore, CNV-ECOD is expected to develop into an efficient and robust CNV detection tool.

Copy number variation (CNV) plays an important role in disease susceptibility as a type of intermediate-scale structural variation (SV). Accurate CNV detection is crucial for understanding human genetic diversity, elucidating disease mechanisms, and advancing cancer genomics. A variety of CNV detection tools based on short sequencing reads from next-generation sequencing (NGS) have been developed. Although many researchers have conducted extensive comparisons of the detection performance of various tools, these studies have not fully considered the comprehensive impact of factors such as variant length, sequencing depth, tumor purity, and CNV types on tools performance. Therefore, we selected 12 widely used and representative detection tools to comprehensively compare their performance on both simulated and real data. For the simulated data, we compared their performance across six variant types under 36 configurations, including three variant lengths, four sequencing depths, and three tumor purities. For the real data, we used the overlapping density score (ODS) to evaluate the performance of the 12 detection tools. Additionally, we compared their time and space complexities. In this study, we analyzed the impact of each configuration on the tools and recommended the most suitable detection tools for each scenario. This study provides important guidance for researchers in selecting the appropriate variant detection tools for complex situations.

Copy number variation (CNV) is a major type of structural variation (SV) that plays critical roles in genetic diversity and disease. Currently, many CNV detection tools have been developed. Although each tool exhibits different advantages under specific scenarios, they still have disadvantages such as suboptimal sensitivity, imprecise breakpoint resolution, and reduced robustness in complex sequencing environments. Developing more effective CNV detection tools by building upon the strengths of existing tools presents a significant challenge in the field. To fully leverage the detection results of existing tools and improve the accuracy of CNV detection under complex sequencing conditions, a new method called SSLCNV (semi-supervised learning framework for CNV detection) is proposed. It combines consensus-based pseudo-labeling using density-based clustering. SSLCNV generates high-confidence pseudo-labels by intersecting CNV predictions from four representative tools (CNVkit, GROM-RD, Matchclips2, OTSUCNV) and uses these as core seeds for clustering. Additionally, SSLCNV introduces a new constraint z-score into the DBSCAN algorithm to enhance clustering accuracy. By leveraging the improved DBSCAN and incorporating reliable labels, SSLCNV effectively detects CNV from partially labeled and unlabeled data. Comprehensive evaluations on both simulated and real datasets demonstrate that SSLCNV consistently achieves superior F1-scores compared to existing tools across diverse sequencing depths and tumor purities. Importantly, it maintains robust performance under low-coverage conditions, yielding higher recall without a substantial loss in precision. SSLCNV offers a scalable and accurate solution for CNV detection, particularly advantageous in scenarios with complex genomic backgrounds.

Copy number variation (CNV) is an important part of human genetic variations, which is associated with various kinds of diseases. To tackle the limitations of traditional CNV detection methods, such as restricted detection types, high error rates, and challenges in precisely identifying the location of variant breakpoints, a new method called MSCNV (copy number variations detection method for multi-strategies integration based on a one-class support vector machine model) is proposed. MSCNV establishes a multi-signal channel that integrates three strategies: read depth, split read, and read pair. First, a one-class support vector machine algorithm is used to detect abnormal signals in read depth and mapping quality values to determine the rough CNV region. Then, the rough CNV region is filtered by using paired read signals to improve the precision of MSCNV method. Finally, MSCNV explores and recognizes tandem duplication regions, interspersed duplication regions, and loss regions. It uses split read signals to determine the precise location of mutation points and to determine the type of variation. Compared with Manta, FREEC, GROM-RD, Rsicnv, and CNVkit, MSCNV significantly improves the sensitivity, precision, F1-score, and overlap density score of CNV detection while reducing the boundary bias of the detection results.

Background Tandem duplication (TD) is a common and important type of structural variation in the human genome. TDs have been shown to play an essential role in many diseases, including cancer. However, it is difficult to accurately detect TDs due to the uneven distribution of reads and the inherent complexity of next-generation sequencing (NGS) data. Methods This article proposes a method called DTDHM (detection of tandem duplications based on hybrid methods), which utilizes NGS data to detect TDs in a single sample. DTDHM builds a pipeline that integrates read depth (RD), split read (SR), and paired-end mapping (PEM) signals. To solve the problem of uneven distribution of normal and abnormal samples, DTDHM uses the K-nearest neighbor (KNN) algorithm for multi-feature classification prediction. Then, the qualified split reads and discordant reads are extracted and analyzed to achieve accurate localization of variation sites. This article compares DTDHM with three other methods on 450 simulated datasets and five real datasets. Results In 450 simulated data samples, DTDHM consistently maintained the highest F1-score. The average F1-score of DTDHM, SVIM, TARDIS, and TIDDIT were 80.0%, 56.2%, 43.4%, and 67.1%, respectively. The F1-score of DTDHM had a small variation range and its detection effect was the most stable and 1.2 times that of the suboptimal method. Most of the boundary biases of DTDHM fluctuated around 20 bp, and its boundary deviation detection ability was better than TARDIS and TIDDIT. In real data experiments, five real sequencing samples (NA19238, NA19239, NA19240, HG00266, and NA12891) were used to test DTDHM. The results showed that DTDHM had the highest overlap density score (ODS) and F1-score of the four methods. Conclusions Compared with the other three methods, DTDHM achieved excellent results in terms of sensitivity, precision, F1-score, and boundary bias. These results indicate that DTDHM can be used as a reliable tool for detecting TDs from NGS data, especially in the case of low coverage depth and tumor purity samples.

Tandem duplications significantly influence the diversity of the human genome and the occurrence of many complex diseases. However, accurate detection of tandem duplications of low coverage remains a challenging task. Based on RD (read depth), the tandem duplication detection method assumes that there is a linear relationship between the RD value and the tandem duplication number in the region in the genome. However, at low coverage, the RD values of tandem duplication regions and normal regions in the sequencing sample are not significantly different, and it will affect the performance of tandem duplication detection. Therefore, relying on traditional statistical models based on RD strategy to detect tandem duplications often leads to relative low precision and recall. For solving this problem, we propose a new method for identifying tandem duplications in whole-genome sequencing data.TD-COF, a tandem duplication detection method, utilizes the COF (Connectivity-Based Outlier Factor) algorithm. Considering the relative connectivity between intervals in the genome, the algorithm applies a connectivity factor to each bin to calculate its outlier score Additionally, TD-COF introduces the Split Read strategy into RD-based methods, enabling precise identification of the start and end points of tandem duplications down to the level of individual bases. Furthermore, TD-COF incorporates mapping quality as a feature signal. The bins with lower mapping quality are assigned higher outlier values, effectively mitigating interference from mapping errors. Simulation experiments demonstrate that TD-COF outperforms other methods in terms of sensitivity, precision and F1 score. And, TD-COF exhibits high consistency with other methods when applied to real sequencing samples. This study indicates that TD-COF is an effective method for detecting tandem duplications even in regions of low or moderate coverage. In summary, we consider the TD-COF to be an effective method for detecting tandem duplications.

Copy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. However, low CN (copy number of 3–4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. In addition, the RD-based approach can only obtain rough breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. CNV-PPC first uses the split read signal to search for potential breakpoints. A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.

为了提高镁合金的耐蚀性,采用电镀铝-化学镀锌工艺在其表面制备了Al-Zn复合镀层.通过镀层形貌观察及物相分析考察了其组成结构,采用电化学方法研究了主盐浓度、络合剂浓度、槽液温度对镀层耐蚀性的影响.结果 表明:采用电镀铝-化学镀锌工艺得到的Al-Zn复合镀层平整度、均匀性优异,镀层与基体结合牢固.最佳施镀工艺参数为ZnSO4·7H2O质量浓度40 g/L,K4P2O7·3H2O质量浓度150 g/L,化学镀液温度为50℃.与基体试样相比,在最佳工艺参数条件下施镀后,试样的腐蚀速率大幅降低.该工艺能够实现很好的电镀效果和防腐蚀性能,具有一定的工业价值.

In the multi-chiller of the air conditioning system, the optimal chiller loading (OCL) is an important research topic. This research is to find the appropriate partial load ratio (PLR) for each chiller in order to minimize the total energy consumption of the multi-chiller under the system cooling load (CL) requirements. However, this optimization problem has not been well studied. In this paper, in order to solve the OCL problem, we propose an improved fruit fly optimization algorithm (IFOA). A linear generation mechanism is developed to uniformly generate candidate solutions, and a new dynamic search radius method is employed to balance the local and global search ability of IFOA. To empirically evaluate the performance of the proposed IFOA, a number of comparative experiments are conducted on three well-known cases. The experimental results show that IFOA found 14 optimal values (the optimal values among all algorithms) under a total of 17CLs in three cases, and the ratio of the optimal values found was 82.4%, which was the highest among all algorithms. In addition, the mean value of all objective functions of IFOA is smaller and the standard deviation is equal to or close to 0, which proves that the algorithm has high stability. It can be concluded that IFOA is an ideal method to solve the OCL problem.

Tandem duplication (TD) is an important type of structural variation (SV) in the human genome and has biological significance for human cancer evolution and tumor genesis. Accurate and reliable detection of TDs plays an important role in advancing early detection, diagnosis, and treatment of disease. The advent of next-generation sequencing technologies has made it possible for the study of TDs. However, detection is still challenging due to the uneven distribution of reads and the uncertain amplitude of TD regions. In this paper, we present a new method, DINTD (Detection and INference of Tandem Duplications), to detect and infer TDs using short sequencing reads. The major principle of the proposed method is that it first extracts read depth and mapping quality signals, then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to find the possible TD regions. The total variation penalized least squares model is fitted with read depth and mapping quality signals to denoise signals. A 2D binary search tree is used to search the neighbor points effectively. To further identify the exact breakpoints of the TD regions, split-read signals are integrated into DINTD. The experimental results of DINTD on simulated data sets showed that DINTD can outperform other methods for sensitivity, precision, F1-score, and boundary bias. DINTD is further validated on real samples, and the experiment results indicate that it is consistent with other methods. This study indicates that DINTD can be used as an effective tool for detecting TDs.

耐海水腐蚀冲刷蝶阀是核电循环水冷却系统的重要装备.为满足恶劣的工况条件及核电行业对安全生产的严格要求,开发一种新型核电海水蝶阀,并根据海水的腐蚀与冲刷特性进行合理选材.采用ANSYS Workbench有限元分析软件,建立核电海水蝶阀的有限元模型,分析1.5倍设计压力载荷下蝶板的形变及应力分布情况.仿真结果表明,经优化设计的海水蝶阀结构合理,蝶板的形变与应力均满足设计要求.

In real world applications, there are a large number of unlabeled data, but the number of labeled data is relatively small. It is a fact that the labeled data are often difficult to be gained, and the labeling work is often time consuming. So we must use the few given labeled data more effectively in data analysis. Traditional clustering algorithm can group unlabeled data, but the label of each cluster is uncertain. Clustering only gives partitions to dataset. In this paper, we propose a two-stage semi-supervised clustering algorithm. It can take advantage of clustering algorithm firstly. Then guided by the labeled data, it can fine clustering results based on hybrid particle swarm optimization. Experiment results show that the proposed method can give good results using both labeled data and unlabeled data.

Clustering is the technique to partition data patterns into different clusters. In real datasets, there are often a large number of unlabeled data patterns and a small number of labeled data patterns. Clustering technology can group data by using the unlabeled data only, but the accuracy is often poor. Classification can learn a classification model through labeled data. Classification can group data too. The classification accuracy is relatively high, but it needs a rich supply of labeled data. Therefore, in order to solve such partition problems of hybrid dataset, semi-supervised clustering is proposed. This paper proposes an improved artificial bee colony (ABC) semi-supervised clustering algorithm. The new algorithm tries to use both labeled data and unlabeled data. It is implemented by simulating the feeding of bees in nature. Experiment results show that the new clustering method performs very well, and it can improve cluster accuracy greatly.

Parallel test sheet generation (PTSG) is a NP-hard combinational optimization problem, in which test sheet generation algorithm with high quality and efficiency is the core technology. Basic fruit fly optimization algorithm (FOA) has the defects of easily relapsing into local optimal and low convergence precision when solving PTSG problem. In this paper, a novel discrete fruit fly optimization algorithm is proposed to solve the PTSG problem, in which a discrete osphesis searching operator based on the problem-specific knowledge is designed to help the FOA escaping from being trapped in local minima. To evaluate the performance of the proposed algorithm, the simulation experiments were conducted using a series of item banks with different scales. The superiority of the proposed algorithm is demonstrated by comparing it with the particle swarm optimization algorithm and differential evolution algorithm.

A 3-Dimensional accurate analysis of the electrostatic valveless micropump involved in electrostatic dynamics,structural dynamics and fluid dynamics of the system.This was made by using multi-field coupled analysis.The results show that the dynamic characteristics of fluid in the micropump are dependent highly on the vibration of flexible membrane.The flow rectification function of the diffuser/nozzle element will work only after the pressure difference between the two ends of the element reaches a critical value.In the pump chamber,the fluidic press distribution almost independent of the depth variation.The maximum of Reynolds numbers is far less than the traditional critical Reynolds number.Due to the increasing demands for accuracy,the nonlinear features of viscous loss of the fluid can no longer be neglected or simplified,but have to be taken into account in detail.

For the clustering partition issue,an unsupervised clustering approach based on improved artificial bee colony and nearest neighbour principle is given.This approach views every honey source as a candidate solution for the clustering,and designs a multidimensional code structure for the bee.To cluster effectively,based on better clustering centre selected by the employed bee and the onlookers in their local search phase,this approach divides all data patterns in clustering space by using the nearest neighbour principle in k-means.To improve local and global search ability of bee,the approach presents new local and global search method according to the feature of clustering problems.Simulative experimental results show that the new approach is feasible and effective.

With the close combination of knowledge management and process quality management, man, as the member of 5M1E, is especially important to the fluctuation effect on product quality. How to improve the comprehensive quality of employees is the core strategy for enterprise to realize the total quality management. Therefore, this study took bearing manufacturing process as an example, expounded the theoretical framework and technological implementation method of system of quality knowledge learning and the system realization interface was given finally.

Micropump is the key component in the micro total analytical system. The major technical impediment in improving the performance of this micro-device lies in the lack of understanding the physical phenomena and their interactions of electric, mechanical, and fluidic fields for performing their intended functions. Because of the complexity of the micropump, the full coupled numerical analysis is extremely needed. This paper presents for the results of such fully coupled simulations. CFDRC® was used for the transient analysis of an electrostatically actuated micropump. The results show that the dynamic characteristics of fluid in the micropump and the vibration of flexible diaphragm are highly interactive each other. The flow rectification of the nozzle/diffuser will work only after the pressure difference between the two ends of valve have reached at a critical value. In the pump chamber, the fluidic press distribution almost independent of the space variation. Due to the increasing demands for accuracy, the nonlinear features of viscous loss of the fluid can no longer be neglected or simplified, but have to be taken into account in detail.