With the rapid advancement of information technology and online technologies, it has become imperative for all colleges and universities to address the challenge of leveraging modern management ...methods to enhance management efficiency, level, and practices. In the realm of educational institution management, the learning management system stands as the cornerstone of contemporary campus management and the entire campus computer network. In this article, the Q-learning algorithm, specifically applied to a heterogeneous cellular network, is employed to investigate the issues pertaining to teaching management in colleges and universities.The design and implementation of the university teaching management system mainly uses heterogeneous cellular networks to establish a physical model of the Q-learning algorithm, and then in the Rayleigh channel, the coverage factor and the system rate expression are derived in a closed form, and in a closed form Derive the energy efficiency expression, and then optimize the density of the basic micro-station algorithm to maximize performance. In the process of system design, this paper uses an algorithm to analyze the current research status of university teaching management, then proposes the research purpose and subject content, and analyzes the university's teaching management function and potential future functional requirements. When designing the university learning management system, it describes in detail the overall design concept, key technologies, system structure, and operating system structure and system topology. This research mainly focuses on the research of campus information management and Q learning algorithm. Based on the cellular network, a large number of campus management cases were analyzed and data fitting at the same time, and a set of Q-learning algorithm teaching management system based on heterogeneous cellular network was designed.
Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot ...correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.
We present a tool that combines fast mapping, error correction, and de novo assembly (MECAT; accessible at https://github.com/xiaochuanle/MECAT) for processing single-molecule sequencing (SMS) reads. ...MECAT's computing efficiency is superior to that of current tools, while the results MECAT produces are comparable or improved. MECAT enables reference mapping or de novo assembly of large genomes using SMS reads on a single computer.
Parents are significantly important in shaping the screen use of children within a family system. This study aimed to examine the associations of Chinese children's screen time (ST) over four years ...with parents' attitudes toward their own screen use and physical activities (PA) and health behaviors including their ST, PA, cigarette smoking, and alcohol drinking.
The current study utilized data from two waves (2011 and 2015) of the China Health and Nutrition Survey (CHNS), including 1,941 mother-father-child triads in 2011 and 2,707 mother-father-child triads in 2015 (with children aged 0-17-years-old). The ST of children and the parental attitudes and health behaviors were measured via self-report or proxy-report (for children under 6 years old) questionnaires. Pool-OLS regression models were used to assess the associations of parental attitudes and health behaviors with the ST of children. Moderation models were built to assess whether these associations depended on the gender, age, and family income of children, as well as whether paternal and maternal influences were moderated by the other parent. A multilevel cross-lagged panel model (CLPM) was used to assess parental influences on children's ST over four years.
Paternal ST (β = 0.09, p < 0.001), maternal ST (β = 0.10, p < 0.001), and paternal alcohol drinking (β = 0.30, p < 0.05) were positively associated with children's ST. In addition, maternal smoking had a positive association with girls' ST (β = 0.53, p < 0.05). Moreover, the association between maternal ST and children's ST was observed to decline as family income increased (β = -0.03, p < 0.001). Paternal ST had a larger positive association with children's ST when the ST of mothers exceeded 14 h/week (β = 0.06, p < 0.05). Furthermore, lagged associations were found between paternal attitudes toward PA (β = -1.63, p < 0.05) or maternal cigarette smoking (β = 1.46, p < 0.05) and children's ST measured four years later.
Children establish a healthy lifestyle within the family system. From the perspective of the healthy family climate, the current study suggests that future programs for reducing children's ST should be built through an integrative approach with special attention to parental attitudes and health behaviors.
Now the development speed of cities is getting faster and faster, and many cities have reached the stage of urban landscape design. At this stage, the requirements for the urban landscape are mainly ...in the transmission of information, which can be summarized as the identification of information, the requirements for ideology and readability, which clearly tells urban planners that they need to receive information from the perspective of vision To design the urban landscape. After investigation, it is found that under the current technological development trend, the application of geographic information based on smart mobile terminals has become a development trend and has become one of the research hotspots in the field of geographic information science. With more and more methods of acquiring geographic information, such as smart phones, drones, and remote sensing satellites, a large amount of spatial data is continuously presented at an extremely fast speed. In recent years, the rapid development of the computer hardware manufacturing industry has made great contributions to the performance of embedded devices in improving computing speed and expanding memory. In this high-speed development environment, the fittest will survive, and the inadequate will be eliminated. Under the background of the information age, any new field will face the problem of short life cycle. Although UI also has it, the development of UI still has a long way to go. Therefore, the UI design industry still has great development prospects.
In industrial steel plate production, process parameters and steel grade composition significantly influence the microstructure and mechanical properties of the steel produced. But determining the ...exact relationship between process parameters and mechanical properties is a challenging process. This work aimed to devise a deep learning model, to predict mechanical properties of industrial steel plate including yield strength (YS), ultimate tensile strength (UTS), elongation (EL), and impact energy (Akv); based on the process parameters as well as composition of raw steel, and apply it online to a real steel manufacturing plant. An optimal deep neural network (DNN) model was formulated with 27 inputs parameters, 2 hidden layers each having 200 nodes and 4 output parameters (27 × 200 × 200 × 4) with an initial learning rate 0.0001, using Adam optimizer and subjected to Z pre-processing method, to yield an accurate model with R2 = 0.907. The tuned DNN model, had a root mean square error of 21.06 MPa, 16.67 MPa, 2.36%, and 39.33 J, and root mean square percentage error of 4.7%, 2.9%, 7.7%, and 16.2%, for YS, UTS, EL and Akv respectively. Through comparative analysis, it was found that the accuracy of DNN model was higher than other classic machine learning algorithms. To interpret the model assumptions and findings, several local linear models were devised and analyzed to establish the link between process parameters and mechanical properties. Finally the tuned DNN model was deployed in the real-steel plant for online monitoring and control of steel mechanical properties, and to guide the production of targeted steel plates with tailored mechanical properties.
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•An online prediction system of the mechanics of the hot-rolled steel plate is developed.•Real-world data (11,101 groups) were used to avoid overfitting and allow broad applicability.•Deep neural network is trained to achieve high prediction accuracy.•Impacts of model hyperparameters are discussed in detail.•Physical metallurgy knowledge learned by deep learning is interpreted.
Video and image sources are frequently applied in the area of defect inspection in industrial community. For the recognition and classification of sewer defects, a significant number of videos and ...images of sewers are collected. These data are then checked by human and some traditional methods to recognize and classify the sewer defects, which is inefficient and error-prone. Previously developed features like SIFT are unable to comprehensively represent such defects. Therefore, feature representation is especially important for defect autoclassification. In this paper, we study the automatic extraction of feature representation for sewer defects via deep learning. Moreover, a complete automatic system for classifying sewer defects is proposed built on a two-level hierarchical deep convolutional neural network, which shows high performance with respect to classification accuracy. The proposed network is trained on a novel data set with over 40 000 sewer images. The system has been successfully applied in the practical production, confirming its robustness and feasibility to real-world applications. The source code and trained model are available at the project website. 1
Parent‒child communication in migrant families is essential to family bonds and the mental health of left-behind children (LBC). Little is known about the different patterns of communication between ...migrant parents and LBC and associated communication quality and mental health outcomes.
A sample of 2,183 Chinese children (mean age = 12.95 ± 1.29 years) from Anhui province, including LBC whose parents had both migrated (n = 1,025) and children whose parents had never migrated (never-LBC, n = 1,158), was analyzed. With the LBC sample, latent class analysis was applied to identify the patterns of parent‒child communication. Multinomial logistic regression analysis was conducted to assess the associations between the sociodemographic variables and class membership of LBC. Analysis of covariance and chi-square tests were used to compare communication quality and mental health outcome differences among the classes of LBC and between each of the classes and never-LBC.
Five latent classes of communication formed through different media or channels between migrant parents and their LBC were identified. Higher household economic status (OR = 2.81, p < 0.05) was associated with adequate communication. LBC in Class 1, defined by frequent technologically-mediated and face-to-face communication, had a significantly higher quality of communication with their migrant parents (F = 8.92, p < 0.001) and better mental health than those in other latent classes; these children did not have significantly worse mental health outcomes compared to never -LBC.
Facilitating multichannel parent‒child communication is a practical way of reducing mental health inequities between LBC and their peers.
Pressure sensors are a key component in electronic skin (e-skin) sensing systems. Most reported resistive pressure sensors have a high sensitivity at low pressures (<5 kPa) to enable ultra-sensitive ...detection. However, the sensitivity drops significantly at high pressures (>5 kPa), which is inadequate for practical applications. For example, actions like a gentle touch and object manipulation have pressures below 10 kPa, and 10-100 kPa, respectively. Maintaining a high sensitivity in a wide pressure range is in great demand. Here, a flexible, wide range and ultra-sensitive resistive pressure sensor with a foam-like structure based on laser-scribed graphene (LSG) is demonstrated. Benefitting from the large spacing between graphene layers and the unique v-shaped microstructure of the LSG, the sensitivity of the pressure sensor is as high as 0.96 kPa(-1) in a wide pressure range (0 ~ 50 kPa). Considering both sensitivity and pressure sensing range, the pressure sensor developed in this work is the best among all reported pressure sensors to date. A model of the LSG pressure sensor is also established, which agrees well with the experimental results. This work indicates that laser scribed flexible graphene pressure sensors could be widely used for artificial e-skin, medical-sensing, bio-sensing and many other areas.
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