Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.

With the development of education informatization and the accumulation of massive educational resources and teaching data in urban environments, educational knowledge graphs that provide good conditions for developing data-driven intelligent education have been proposed. Based on such educational knowledge graphs, the question-answering method can provide students with immediate coaching and significantly increase their learning interest and productivity. However, there is little research on knowledge graph question-answering focused on the educational field. Students tend to consult complex questions that require reasoning; however, the existing QA system cannot satisfy their complex information needs. To help improve sustainable learning efficiency, we propose a novel intelligent question-answering model applied in smart cities, which can reason over the educational knowledge graph to locate the answers to given questions. Our approach uses a highly expressive bilinear graph neural network technology to perform forward reasoning, utilizing the contextual information between graph nodes to improve reasoning ability. On this basis, we propose two-teacher knowledge distillation. We construct two distinct teacher networks by combining forward and backward reasoning, then incorporate the intermediate supervision signals from the two networks to guide the reasoning process, thereby mitigating the phenomenon of spurious path reasoning. Extensive experiments on the MOOC Q&A dataset prove the effectiveness of our approach.

Breast cancer (BRCA) has an undesirable prognosis and is the second most common cancer among women after lung cancer. A novel mechanism of programmed cell death called cuproptosis is linked to the development and spread of tumor cells. However, the function of cuproptosis in BRCA remains unknown. To this date, no studies have used machine learning methods to screen for characteristic genes to explore the role of cuproptosis-related genes (CRGs) in breast cancer. Therefore, 14 cuproptosis-related characteristic genes (CRCGs) were discovered by the feature selection of 39 differentially expressed CRGs using the three machine learning methods LASSO, SVM-RFE, and random forest. Through the PPI network and immune infiltration analysis, we found that PRNP was the key CRCG. The miRTarBase, TargetScan, and miRDB databases were then used to identify hsa-miR-192-5p and hsa-miR-215-5p as the upstream miRNA of PRNP, and the upstream lncRNA, CARMN, was identified by the StarBase database. Thus, the mRNA PRNP/miRNA hsa-miR-192-5p and hsa-miR-215-5p/lncRNA CARMN ceRNA network was constructed. This ceRNA network, which has not been studied before, is extremely innovative. Furthermore, four cuproptosis-related lncRNAs (CRLs) were screened in TCGA-BRCA by univariate Cox, LASSO, and multivariate Cox regression analysis. The risk model was constructed by using these four CRLs, and the risk score = C9orf163 * (1.8365) + PHC2-AS1 * (−2.2985) + AC087741.1 * (−0.9504) + AL109824.1 * (0.6016). The ROC curve and C-index demonstrated the superior predictive capacity of the risk model, and the ROC curve demonstrated that the AUC of 1-, 3-, and 5-year OS in all samples was 0.721, 0.695, and 0.633, respectively. Finally, 50 prospective sensitive medicines were screened with the pRRophetic R package, among which 17-AAG may be a therapeutic agent for high-risk patients, while the other 49 medicines may be suitable for the treatment of low-risk patients. In conclusion, our study constructs a new ceRNA network and a novel risk model, which offer a theoretical foundation for the treatment of BRCA and will aid in improving the prognosis of BRCA.

Currently, glaucoma has become an important cause of blindness. At present, although glaucoma cannot be cured, early treatment can prevent it from getting worse. A reliable way to detect glaucoma is to segment the optic disc and cup and then measure the cup-to-disc ratio (CDR). Many deep neural network models have been developed to autonomously segment the optic disc and the optic cup to help in diagnosis. However, their performance degrades when subjected to domain shift. While many domain-adaptation methods have been exploited to address this problem, they are apt to produce malformed segmentation results. In this study, it is suggested that the segmentation network be adjusted using a constrained formulation that embeds prior knowledge about the shape of the segmentation areas that is domain-invariant. Based on IOSUDA (i.e., Input and Output Space Unsupervised Domain Adaptation), a novel unsupervised joint optic cup-to-disc segmentation framework with shape constraints is proposed, called SCUDA (short for Shape-Constrained Unsupervised Domain Adaptation). A shape constrained loss function is novelly proposed in this paper which utilizes domain-invariant prior knowledge concerning the segmentation region of the joint optic cup–optical disc of fundus images to constrain the segmentation result during network training. In addition, a convolutional triple attention module is designed to improve the segmentation network, which captures cross-dimensional interactions and provides a rich feature representation to improve the segmentation accuracy. Experiments on the RIM-ONE_r3 and Drishti-GS datasets demonstrate that the algorithm outperforms existing approaches for segmenting optic discs and cups.

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship scheduling algorithm based on a Markov-modulated fluid priority queue, which describes the stochastic evolution of the anchorage operation state via a continuous-time Markov chain and abstracts the arrival and service processes of ships into a continuous fluid input and output mechanism modulated by the state. The algorithm introduces a multi-priority service strategy to achieve the differentiated scheduling of different types of ships and improves the computational efficiency and scalability based on a matrix analysis method. Simulation results show that the proposed model reduces the average waiting time of ships by more than 90% compared with the M/G/1/1 and RL strategies and improves the utilization of anchorage resources by about 20% through dynamic service rate adjustment, showing significant advantages over traditional scheduling methods in multi-priority scenarios.

Based on the principle of attitude perception, a real-time detection system for force deformation of suspension beam arm using MPU-6050 module is designed.By constructing the mechanical model of cantilever arm, the bending deformation of cantilever beam model in the elastic deformation range is studied, that is, the Angle change value of cantilever at the free end fulcrum is collected, the deflection equation of cantilever beam is deduced, and then the parameters of the cantilever beam are calculated.Then, a new monitoring system can be designed to use the cantilever point Angle change to carry out the stress deformation of the beam. The system is integrated with software and hardware design, which is an integrated detection system composed of embedded monitoring platform and PC software monitoring platform.The experimental results show that the force error range of the cantilever beam is 0.3N, and the deflection error range of the free end is 1.2cm, which is only 6% compared with the length of the beam arm.Therefore, the detection error of the system is small, easy to operate, and it can efficiently detect the force size of the beam arm bending deformation, the position of the force point, the maximum deflection value, the position of the maximum deflection point and other parameters in real time online.