To address the issue of operational instability in the VR visualization teaching platform for mechanical manufacturing courses—caused by significant errors in system variables during complex tasks such as virtual environment rendering, interactive operations, and data transmission/processing—an iterative learning PID control algorithm is proposed. This study first examines the operational framework of the platform, followed by a detailed analysis of the key variable factors influencing its control performance. According to the relevant variable factors to determine the platform stable operation control target parameters, set the optimal value of control parameters, joint iterative learning control algorithm and PID algorithm, design iterative learning PID controller, the collected platform operation variable values into the controller, through the controller to make the teaching platform in the daily operation, constantly converge to the optimal control parameters, to prevent the platform operation variable error caused by the platform operation space-time loss of control, to realize the teaching platform operation control, to prevent the platform operation variable error caused by the platform operation space-time loss of control. The controller ensures the teaching platform converges to optimal control parameters during daily operation, preventing instability caused by system variable errors, and realizes the stable operation of the teaching platform. The experimental results show that this method is highly accurate and effective in controlling the operation of the teaching platform.

Copy number alterations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor versus normal cell classification; CNAs profile accuracy, tumor subclone inference, and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.

As an essential part of intelligent transportation system (ITS), traffic forecasting has provided crucial role for traffic management and risk assessment. However, complex spatial–temporal dependencies, heterogeneity, dynamicity, and periodicity of traffic data influence the traffic forecasting performance. Consequently, we propose a novel effective gated spatial–temporal merged transformer (GSTMT) inspired by multimask and dual branch for accurate traffic forecasting in this paper. Specifically, we first conduct a concatenation of gated spatial static mask transformer (GSSMT) and gated spatial dynamic mask transformer (GSDMT) with residual network. The GSSMT and GSDMT evolve from the traditional transformer by making preferable modifications that include gated linear unit (GLU), multimask mechanism including static mask matrix (SMM) and dynamic mask matrix (DMM), and spatial attention (SA). Among them, GLU is to promote the performance of capturing spatial dependency, dynamicity, and heterogeneity due to advanced performance for controlling information flow through layers. Additionally, by developing multimask mechanism including two novel SMM and DMM, the proposed GSTMT can precisely model the static and dynamic spatial structure for effectively highlighting static dependency and dynamicity. And SA is injected for enhancing the ability of capturing spatial dependency of GSSMT and GSDMT. Secondly, we develop a dual‐branch gated temporal transformer (DBGTT) for capturing temporal dependency, heterogeneity, dynamicity, and periodicity via incorporating the GLU and mixed time series decomposition (MTD) into traditional transformer. Similarly, we also introduce the GLU for empowering DBGTT with capability of capturing temporal dependency, dynamicity, and heterogeneity. In addition, MTD, which brings dual‐branch mechanism, can enhance the DBGTT for capturing more detailed temporal information via exploiting global and periodic profile of traffic data. At last, some experiments, which are performed on several real‐world traffic datasets, demonstrate the better results over classic traffic forecasting methods.

Abstract The human brain magnetic resonance image (MRI) is always contaminated by noise and has uncertainty on the boundary between different tissues. These characteristics bring challenges to the human brain image segmentation. To handle these limitations, many variants of standard fuzzy c‐means (FCM) algorithm have been proposed. Some methods attempt to incorporate the local spatial information in the standard FCM algorithm. However, they can't solve the problem of data uncertainty very well. And some other methods can handle the problem of data uncertainty, but they are sensitive to noise since it doesn't incorporate any local spatial information. In this paper, we propose a noise robust intuitionistic fuzzy c‐means (NR‐IFCM) algorithm, which can handle noise and uncertainty problems simultaneously. In order to process the human brain MRI with noise better, we introduce a noise robust intuitionistic fuzzy set (NR‐IFS) which is noise robust in this NR‐IFCM algorithm. Meanwhile, in order to handle the data uncertainty, we also introduce a new intuitionistic fuzzy factor to this NR‐IFCM algorithm which combine the local gray‐level and the spatial information together. A large number of experimental results on human brain MRI validate the effectiveness and the superiority of our proposed NR‐IFCM algorithm.

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

Parking is one of the major problems in many cities. Due to the influence of various factors, the number of available parking spaces and travel time is highly dynamic and random. To find the reliable parking lot and the reliable path for travelers, this article proposes two parking guidance models and algorithms considering the earliest arrival time and the latest departure time. First, an extended shifted lognormal distribution (a 3-parameter lognormal distribution) is introduced to describe travel time. Then, a parking guidance model considering the earliest arrival time and a solution algorithm based on travel time bounds are established to find the reliable parking lot, the earliest arrival time, and the corresponding reliable path. Next, a parking guidance model considering the latest departure time and a solution algorithm based on travel time bounds are developed to find the reliable parking lot, the latest departure time, and the corresponding reliable path. Finally, two case studies with a real-world road network are used to verify the effectiveness and superiority of the proposed models and algorithms.