Notes

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1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.The current work aims to meet the needs of the development of archives work in colleges and universities and the modernization of management to realize the standards and standardization of all aspects of archives business construction in colleges and universities, so as to improve the political and professional quality of archives cadres. First, the radio frequency identification (RFID) technology based on the Internet of things (IoT) digitizes the university archive labels. Meanwhile, the filing cabinet's intelligent security system preserves confidential files. Second, the convolutional neural network (CNN) algorithm under deep learning is introduced and college profile information is identified. Finally, the concept of professional certification is used to clarify the purpose of the university archives automation management system. Different activation functions are used to analyze the recognition accuracy loss and recognition accuracy of university archives. The identification error of You Only Look Once management of university archives by CNN under different activation functions.This paper describes a novel polynomial inherent attention (PIA) model that outperforms all state-of-the-art transformer models on neural machine translation (NMT) by a wide margin. PIA is based on the simple idea that natural language sentences can be transformed into a special type of binary attention context vectors that accurately capture the semantic context and the relative dependencies between words in a sentence. The transformation is performed using a simple power-of-two polynomial transformation that maintains strict consistent positioning of words in the resulting vectors. It is shown how this transformation reduces the neural machine translation process to a simple neural polynomial regression model that provides excellent solutions to the alignment and positioning problems haunting transformer models. The test BELU scores obtained on the WMT-2014 data set are 75.07 BELU for the EN-FR data set and 66.35 BELU for the EN-DE data set-well above accuracies achieved by state-of-the-art transformer models for the same data sets. The improvements are, respectively, 65.7% and 87.42%.We compared the pre-, intra-, and postoperative characteristics among three groups of patients who underwent posterior vertebral column resection (PVCR) to clarify age-related characteristics and to guide patient management, surgical planning, and complication avoiding. We compared and analyzed the etiology, surgical events, outcomes, and complications among pediatric, adolescent, and adult patients who underwent PVCR in a single-center database retrospectively. Patients were categorized into pediatric (0-12 yr), adolescent (13-19 yr), and adult (>20 yr) cohorts. Demographics, surgical events, clinical and radiographic results, and major complications were compared between groups. A total of 87 patients with a mean follow-up 42 (24-96) months were identified. Pediatric group (14) had a high frequency of congenital vertebral and cardiac abnormal, adolescents (47) presented more intracanal malformations, and idiopathic was common in the adult group (26). Although pediatric patients had shorter fusion levels than adolescent and adult, their mean resected vertebrae (1.91), percentage of blood loss (estimated blood loss per total blood volume) (201.9%), and operative time were much higher. The coronal/sagittal correction rate was significantly higher in the pediatric group (73.6%/72.3%). Overall, surgical complications were more frequent in adults, particularly neuromonitoring alert and implant failure. However, more severe complications were noted in younger patients. For pediatric patients with PVCR, poor physiological conditions and frequent comorbidities indicated cautious patient selection and sufficient preoperative preparation. The higher correction rate may be due to the excellent compliance of the spinal cord. For adult patients, preoperative traction and adjusting the tension of the spinal cord during surgery could contribute to neurological safety.At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system's performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (-2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. click here Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.Posttraumatic stress disorder (PTSD) is a mental disorder characterized by a delayed onset and long-lasting psychiatric disorder in an individual due to unusual threatening or catastrophic stressful events, characterized by repeated experiences of the situation, avoidant behaviors, emotional numbness, hypervigilance, and other mental symptoms. It seriously affects the occupational, psychological, and social functions of the human body, leads to a decrease in the quality of life, and brings a greater economic burden to the patients themselves, their families, and the society. It has attracted widespread attention worldwide. Due to social transformation and fierce competition, college students are increasingly exposed to various stressful or traumatic events, and PTSD is becoming more and more obvious. Therefore, this study took a university student as the research object, analyzed the risk factors of PTSD, and used the method of data mining to analyze the effectiveness of DBT therapy and completed the following work (1) this paper introduces the research status of PTSD pathogenesis at home and abroad and expounds the treatment methods and research results of DBT. (2) The basic principle of BPNN is introduced, the weight and threshold of BPNN are screened by genetic algorithm, and the best weight and threshold after screening are given to BPNN. A GA-BP model is constructed to improve the learning quality of BPNN. (3) The optimal parameters of the model are selected through experiments, and the model is verified by the collected data. The results show that the model has superiority in evaluating the effectiveness of DBT therapy. Then, it was proved by experiments that DBT therapy has a good effect in the treatment of PTSD. Finally, the influencing factors of PTSD were analyzed one by one through the experimental results.Under the modern environment, the reconstruction of enterprise's core competitiveness depends not only on capital and technical strength, but also on the overall strength of its human resources. At the same time, effective allocation and rational use of talents are needed to create good performance for enterprises. Enterprise human resource management is the key part of the whole enterprise management. At the same time, it is also a necessary preparation for the continuous development and innovation of enterprises. In the whole process of human resource management, the core work is person-post matching. Only by promoting the reasonable implementation of person-post matching can other management work be carried out smoothly. This paper expounds two major elements in human resource management, namely, the concept and measurement of person-post matching and the principle of person-post matching. And the factors in the matching of people and posts are analyzed. This paper probes into the implementation of person-post matching in enterprise human resource management. Based on this, this paper puts forward a depth model of accurate matching between people and posts based on ability perception. On the basis of studying the optimization of human resource scheduling, this paper takes into account three factors resource constraints, heterogeneity of employee efficiency and time sequence relationship, and uses integer linear programming theory to model the system with the shortest construction period as the goal. The research shows that the accuracy of this algorithm can reach about 94%, which is about 8% higher than the traditional algorithm. It has certain superior performance. This will provide some reference for related researchers.
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