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Unmanned Aerial Vehicles (UAVs) play an important role in remote sensing image classification because they are capable of autonomously monitoring specific areas and analyzing images. The embedded platform and deep learning are used to classify UAV images in real-time. However, given the limited memory and computational resources, deploying deep learning networks on embedded devices and real-time analysis of ground scenes still has challenges in actual applications. To balance computational cost and classification accuracy, a novel lightweight network based on the original GhostNet is presented. The computational cost of this network is reduced by changing the number of convolutional layers. Meanwhile, the fully connected layer at the end is replaced with the fully convolutional layer. To evaluate the performance of the Modified GhostNet in remote sensing scene classification, experiments are performed on three public datasets: UCMerced, AID, and NWPU-RESISC. Compared with the basic GhostNet, the Floating Point Operations (FLOPs) are reduced from 7.85 MFLOPs to 2.58 MFLOPs, the memory is reduced from 16.40 MB to 5.70 MB, and the predicted time is improved by 18.86%. Our modified GhostNet also increases the average accuracy (Acc) (4.70% in AID experiments, 3.39% in UCMerced experiments). These results indicate that our Modified GhostNet can improve the performance of lightweight networks for scene classification and effectively enable real-time monitoring of ground scenes.

Computed tomography (CT) scans have been shown to be an effective way of improving diagnostic efficacy and reducing lung cancer mortality. However, distinguishing benign from malignant nodules in CT imaging remains challenging. This study aims to develop a multiple-scale residual network (MResNet) to automatically and precisely extract the general feature of lung nodules, and classify lung nodules based on deep learning. The MResNet aggregates the advantages of residual units and pyramid pooling module (PPM) to learn key features and extract the general feature for lung nodule classification. Specially, the MResNet uses the ResNet as a backbone network to learn contextual information and discriminate feature representation. Meanwhile, the PPM is used to fuse features under four different scales, including the coarse scale and the fine-grained scale to obtain more general lung features of the CT image. MResNet had an accuracy of 99.12%, a sensitivity of 98.64%, a specificity of 97.87%, a positive predictive value (PPV) of 99.92%, and a negative predictive value (NPV) of 97.87% in the training set. Additionally, its area under the receiver operating characteristic curve (AUC) was 0.9998 (0.99976–0.99991). MResNet's accuracy, sensitivity, specificity, PPV, NPV, and AUC in the testing set were 85.23%, 92.79%, 72.89%, 84.56%, 86.34%, and 0.9275 (0.91662–0.93833), respectively. The developed MResNet performed exceptionally well in estimating the malignancy risk of pulmonary nodules found on CT. The model has the potential to provide reliable and reproducible malignancy risk scores for clinicians and radiologists, thereby optimizing lung cancer screening management.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.

Regional bias of N 6 -methyladenosine (m 6 A) mRNA modification avoiding splice site region, calls for an open hypothesis whether exon-intron boundary could affect m 6 A deposition. By deep learning modeling, we find that exon-intron boundary represses a proportion (12% to 34%) of m 6 A deposition at adjacent exons (~100 nt to splice site). Experiments validate that m 6 A signal increases once the host gene does not undergo pre-mRNA splicing to produce the same mRNA. Inhibited m 6 A sites have higher m 6 A enhancers and lower m 6 A silencers locally and show high heterogeneity at different exons genome-widely, with only a small proportion (12% to 15%) of exons showing strong inhibition, enabling more stable mRNAs and flexible protein coding. m 6 A is majorly responsible for why mRNAs with more exons be more stable. Exon junction complex (EJC) only partially contributes to this exon-intron boundary m 6 A inhibition in some short internal exons, highlighting additional factors yet to be identified. m 6 A mRNA modification is not typically found near splice junctions in mRNAs. Here the authors show exon-intron boundary inhibits m6A deposition at ~100 nt region nearby splice site, enabling m 6 A distribution hallmark, more stable mRNA and flexible protein coding.

Fine roots of plants play an important role in terrestrial ecosystems. There is a close association between the anatomical characteristics and physiological and ecological functions of plants, but we still have a very limited knowledge of anatomical traits. For example, (1) we do not know if herbs and grasses have anatomical patterns similar to those of woody plants, and (2) the variation among different woody plants in the same ecosystem is unclear. In the present study, we analysed the anatomical structures of the fine root systems of various groups of vascular plants (ferns, eudicot herbs, monocots and woody plants) from the same ecosystem (a natural secondary forest on Mao'er Mountain, Heilongjiang, China) to answer the following questions: (1) How does the anatomy of the fine roots change with root order in various plant groups in the same ecosystem? (2) What is the pattern of variation within group? The results show that anatomical traits can be divided into 3 categories: traits that indicate the root capacity to transport resource along the root (stele diameter, xylem cell diameter and xylem cell area); traits that indicate absorptive capacity cortical thickness, (the number of cortical cell layers and the diameter of cortical cells); and traits that are integrated indicators (diameter and the stele to root diameter ratio). The traits indicate the root capacity to transport resource along the root order is generally similar among groups, but absorptive capacity is very different. The shift in function is the main factor influencing the fine root anatomy. Some traits show large variation within groups, but the variations in other traits are small. The traits indicate that the lower-order roots (absorbing roots) in distinct groups are of the first one or two root order in ferns, the first two or three orders in eudicot herbs, the first (only two root orders) or first two orders (more than three root orders) in monocots and the first four or five root orders in woody plants and the other roots are higher-order roots (transport roots). The result will helpful to understand the similarities and differences among groups and the physiological and ecological functions of plant roots.

Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.

With the rapid growth of e-commerce and ongoing innovations in the logistics industry, intelligent unmanned logistics warehousing systems have emerged to significantly enhance operational efficiency and reduce costs. In these systems, the two critical stages of order assignment and path planning are interconnected through racks in the order picking process. However, prior research has largely overlooked their joint optimization. In this paper, we investigate the real time task planning problem (RTTP) in intelligent unmanned logistics warehousing, where racks are dynamically assigned to orders arriving in real time, and robots are responsible for delivering the racks to workstations according to planned paths, with the goal of jointly minimizing total order processing time and travel costs. To solve the RTTP, we first design a joint optimization evaluation indicator and propose a joint optimization task planning (JOTP) algorithm. Furthermore, we innovatively introduce a reinforcement learning-based approach (JOTP-RL) by modeling order selection as a partially observable Markov decision process (POMDP), and employing the Q-Mix algorithm to solve it. To enhance path planning efficiency, we optimize the improved THA ∗ algorithm by eliminating redundant calculations and accounting for congestion times. Finally, extensive experiments conducted on two datasets demonstrate that our proposed algorithms significantly outperform the baseline and state-of-the-art methods, achieving superior efficiency and effectiveness in both execution time and task optimization.

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights.

Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.