Rounds Of Training Research Articles

A knowledge graph based remanufacturing equipment resource modeling method.

Background The uncertainty and non-standardization of the remanufacturing process result in multi-source heterogeneity of remanufacturing equipment capabilities and knowledge information, which limits the development of informationization and intelligence in remanufacturing. Methods Therefore, we propose a knowledge graph-based modeling method for Remanufacturing Equipment Resource (RMER) information. By analyzing the categories of remanufacturing equipment resources and integrating resource information such as man, machine, material, method, and environment, a unified remanufacturing equipment information model and service capability information model were constructed. Based on the top-down knowledge graph construction process, the RMER semantic association information of the ontology pattern and data layers was extracted and integrated to construct the RMER knowledge graph. Results In this paper, 18 enterprise texts, 50 experience texts and thesis patent texts were selected as the training set, and 2 enterprise texts, 50 experience texts and thesis patent texts were selected as the test set. After multiple rounds of model training, the accuracy rate of entity information extraction reached 91.4%, which proved the accuracy and effectiveness of the model for entity information document extraction. Conclusions This study provides a unified information foundation for mapping and optimizing the resource capabilities of remanufacturing equipment. It can help remanufacturing enterprises to quickly configure equipment resources, and improve the intelligent and automated management level of RMER.

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) (P < 0.05). By setting the cluster size K = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm−1 for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

Application of Target Detection Based on Deep Learning in Intelligent Mineral Identification

In contemporary society, rich in mineral resources, efficiently and accurately identifying and classifying minerals has become a prominent issue. Recent advancements in artificial intelligence, particularly breakthroughs in deep learning, have offered new solutions for intelligent mineral recognition. This paper introduces a deep-learning-based object detection model for intelligent mineral identification, specifically employing the YOLOv8 algorithm. The model was developed with a focus on seven common minerals: biotite, quartz, chalcocite, silicon malachite, malachite, white mica, and pyrite. During the training phase, the model learned to accurately recognize and classify these minerals by analyzing and annotating a large dataset of mineral images. After 258 rounds of training, a stable model was obtained with high performance on key indicators such as Precision, Recall, mAP50, and mAP50–95, with values stable at 0.91766, 0.89827, 0.94300, and 0.91696, respectively. In the testing phase, using samples provided by the Geological and Mineral Museum at the School of Earth Sciences and Engineering, Sun Yat-sen University, the model successfully identified all test samples, with 83% of them having a confidence level exceeding 87%. Despite some potential misclassifications, the results of this study contribute valuable insights and practical experience to the development of intelligent mineral recognition technologies.

Research on high-precision angular measurement based on machine learning and optical vortex interference technology

Abstract The paper innovatively constructs a regression prediction model based on the Stacking ensemble learning algorithm by utilizing the distortion degree of vortex optical interference patterns, achieving high-precision measurement of small angles. It constructs a regression prediction model based on the Stacking ensemble learning algorithm. Initially, in the spiral optical conjugate interference system, minute variations in the optical axis yield corresponding interference patterns, within an angle range of 0.0006° to 0.3°. The angle formed between the centroids of the upper two petals in the deformed interference patterns and the center is extracted as a feature for feature extraction. A dataset is established and randomly divided into training, validation, and testing sets in a 6:2:2 ratio. Subsequently, four models—support vector regression, particle swarm optimization back propagation, Gaussian process regression, and the stacking ensemble algorithm—are optimized for hyperparameters, trained, and evaluated based on coefficients of determination, root mean square error, and mean absolute error to compare their predictive performance. Through multiple rounds of training and prediction on randomly partitioned datasets, it is evident that the ensemble model exhibits a reduction in relative error compared to single learners, demonstrating that the Stacking-based ensemble algorithm can combine the strengths of base learners, showcasing superior predictive performance and enhanced stability. Moreover, the Stacking ensemble model achieves a measurement precision of 0.0006°, with a relative error maintained within 0.6%, indicating the feasibility of achieving high-precision measurement of tiny angles in the optical axis using machine learning and spiral optical conjugate interference systems.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BackgroundTo evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.MethodsThe participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the ‘AI intelligent assisted diagnosis system’ teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.ResultsThere was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.ConclusionThe AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Fault Location in Distribution Network Based on RNN and Transfer Learning

Fault location in distribution networks is a major challenge that needs to be addressed in power distribution systems. Currently, fault location methods based on matrix algorithms, genetic algorithms, deep learning, and other algorithms have received wide attention from the industry. However, such methods have some drawbacks: (1) they require high accuracy in fault information uploads, which can lead to low fault tolerance; (2) they tend to converge early and get stuck in local optimal solutions; (3) they involve high computational complexity, leading to location delays. In this study, we propose a fault location framework based on recurrent neural network (RNN) and transfer learning. In this method, we first encode the information data collected from distribution terminals, and then use RNN to establish a nonlinear mapping relationship between fault features and fault location intervals, which effectively improves fault tolerance and reduces misjudgment issues. We then use transfer learning to load the pre-trained model onto the target task to address the problem of insufficient data for fault location in distribution networks. Experimental results show that after 15 rounds of training, our T-RNN model has achieved over 80% accuracy. Benefiting from Glorot weight initialization adopted after transfer learning, the model achieves good performance early on compared to the BP model, converges faster, and ultimately achieves a prediction accuracy of 96.5%.

Using Simulation-Based Mastery Learning to Achieve Excellent Learning Outcomes in Physical Therapist Education.

The 2 aims of this observational study are (a) to describe the implementation and feasibility of a bed mobility skills simulation-based mastery learning (SBML) curricular module for physical therapist students and (b) to measure learning outcomes and student perceptions of this module. Simulation-based mastery learning is an outcome-based educational approach that has been successful in other health professions but has not been explored in physical therapy education. Eighty-seven students in a single cohort of a Doctor of Physical Therapy program. The SBML module in this pretest-posttest study included a pretest, instruction, initial posttest, and additional rounds of instruction and assessment as needed for all learners to achieve the minimum passing standard (MPS) set using the Mastery Angoff and Patient Safety methods. Outcome measures were bed mobility assessment pass rates and scores, additional student and faculty time compared with a traditional approach, and student perceptions of their self-confidence and the module. All students achieved the MPS after 3 rounds of training and assessment beyond the initial posttest. Mean Total Scores improved from 67.6% (12.9%) at pretest to 91.4% (4.8%) at mastery posttest (P < .001, Cohen's d = 1.8, 95% CI [1.4-2.1]); mean Safety Scores improved from 75.2% (16.0%) at pretest to 100.0% (0.0%) at mastery posttest (P < .001, Cohen's d = 1.5, 95% CI [1.2-1.9]). Students who did not achieve the MPS at the initial posttest (n = 30) required a mean of 1.2 hours for additional instruction and assessment. Survey results revealed an increase in student confidence (P < .001) and positive student perceptions of the module. Implementation of this SBML module was feasible and resulted in uniformly high levels of bed mobility skill acquisition. Based on rigorous learning outcomes, feasible requirements for implementation, and increased student confidence, SBML offers a promising approach for wider implementation in physical therapy education.

A deep learning-based quantitative prediction model for the processing potentials of soybeans as soymilk raw materials

Current technologies as correlation analysis, regression analysis and classification model, exhibited various limitations in the evaluation of soybean possessing potentials, including single, vague evaluation and failure of quantitative prediction, and thereby hindering more efficient and profitable soymilk industry. To solve this problem, 54 soybean cultivars and their corresponding soymilks were subjected to chemical, textural, and sensory analyses to obtain the soybean physicochemical nature (PN) and the soymilk profit and quality attribute (PQA) datasets. A deep-learning based model was established to quantitatively predict PQA data using PN data. Through 45 rounds of training with the stochastic gradient descent optimization, 9 remaining pairs of PN and PQA data were used for model validation. Results suggested that the overall prediction performance of the model showed significant improvements through iterative training, and the trained model eventually reached satisfying predictions (|relative error| ≤ 20%, standard deviation of relative error ≤ 40%) on 78% key soymilk PQAs. Future model training using big data may facilitate better prediction on soymilk odor qualities.

Pre-extension Demonstration and Popularization of Silk Production Technologies in South Ethiopia

The study was conducted in Shebadino, Tula and Wondogenet districts of Sidama regional state of southern Ethiopia from 2018 to 2023 with the objectives of to evaluate the performance of silk production technologies at various levels of silk production with stakeholders/beneficiaries. The study also aimed to create awareness and develop confidence among silkworm producers, development agents, agricultural experts and policy makers for wider dissemination. Nine kebeles’ (the lowest administrative level), were selected from three (3) districts for the purposes of this study. Five (5) farmers from each kebele were selected, totaling 45 farmers, for technology pre-scale up and promotion. Three rounds of theoretical and practical training were given to these farmers after they were selected. Training in the third round focused on managing row silk and cocoons after harvest, with a first round that covered broad knowledge and the importance of sericulture. Of the farmers who were chosen and trained, 95.6 percent were women who began raising silk worms, producing cocoons, and processing row silk. Each farmer received 30 kilograms of castor seed and 50 disease-free adult moths for pre-scaling up and promotion of the technology. During the first year of the study period, 90%, 50%, and 40% of the disease-free moths that were disseminated laid eggs and developed into cocoons at Shebadino, Tula sub city and Wondogenet districts respectively. Sericulture is very beneficial to young people, particularly women, in terms of providing a variety of employment options and viable income without affecting their regular agricultural activities or the existing sociocultural equilibrium. Sericulture technology with its package used, knowledge and skill of the farmers (users) on the technology upgraded, continuous follow up assured by DAs and experts. This study highlights the significance of sericulture for rural development and offers insightful information for its dissemination.

GAN-based self-supervised message passing graph representation learning

Graph representation learning aims to learn embedding representations of nodes in graph-structured data through supervised or unsupervised methods. However, for unlabeled or sparsely labeled graph datasets, conventional supervised approaches fail to perform adequately, and existing unsupervised methods have difficulty achieving high accuracy on multiple downstream tasks such as node classification and link prediction at lower embedding dimensions. To address this issue, we propose a novel self-supervised graph representation learning approach called GAN-based Message Passing Graph Representation Learning. This approach significantly improves the accuracy of node embeddings without relying on label information. Specifically, GMP-GL has developed a generator based on neighbor-weighted random walk to obtain positive and negative node sample pairs, as well as a discriminator that distinguishes whether a real connection exists between node pairs. Through adversarial learning, GMP-GL automatically learns node embeddings. Furthermore, GMP-GL optimizes the training outcomes of the generative adversarial model through self-supervised embedding aggregation learning, thus introducing rich structural information from the graph for the subsequent rounds of generative adversarial training. In addition, GMP-GL employs multiple autoencoders to generate and filter original embeddings, ensuring that the pre-embeddings preserve a significant amount of original feature information for formal learning. The study selects six real-world datasets and conducts comparative experiments with ten SOTA baseline methods. The experimental results demonstrate that the proposed model not only surpasses previous unsupervised methods but also matches or even exceeds the performance of supervised methods. This indicates the great potential of the proposed approach for representation learning tasks on unlabeled graph datasets and its effective applicability to real-world problems.

Segmentation analysis of UAV images based on Unet deep learning algorithm

The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features, and decoder is used to map the features back to the original image size. features to map back to the original image size. UAV image segmentation technology can be applied in agriculture, urban planning, environmental monitoring and other fields. In the field of agriculture, UAV image segmentation can help farmers better manage and monitor farmland to improve crop yield and quality. In the field of urban planning, UAV image segmentation can help urban planners better understand the development status of the city and provide a more scientific basis for urban planning. In the field of environmental monitoring, UAV image segmentation can help us better monitor the changes in the natural environment and provide more effective means for environmental protection. By setting the ratio of training set, validation set and test set as 6:2:2 and performing 100 rounds of training, the U-Net model shows good results in UAV image segmentation. The loss of the model gradually stabilizes at 0.2979 and the accuracy gradually converges to 90.34%. The test results show that the prediction results are very close to the true mask, indicating that the U-Net model can segment UAV images well. The application of UAV image segmentation technology can help us better understand and process the data acquired by UAV and provide more information and basis. In the future, with the continuous development of UAV technology, UAV image segmentation technology will also be more widely used. Through the application of UAV image segmentation technology, we will better understand and protect our environment, manage our farmland more efficiently, and plan our cities more scientifically.

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model

N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-terminal acetylation modifications is important to gain insight into cellular processes and other possible functional mechanisms. Although some algorithmic models have been proposed, most have been developed based on traditional machine learning algorithms and small training datasets. Their practical applications are limited. Nevertheless, deep learning algorithmic models are better at handling high-throughput and complex data. In this study, DeepCBA, a model based on the hybrid framework of convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism deep learning, was constructed to detect the N-terminal acetylation sites. The DeepCBA was built as follows: First, a benchmark dataset was generated by selecting low-redundant protein sequences from the Uniport database and further reducing the redundancy of the protein sequences using the CD-HIT tool. Subsequently, based on the skip-gram model in the word2vec algorithm, tripeptide word vector features were generated on the benchmark dataset. Finally, the CNN, BiLSTM, and attention mechanism were combined, and the tripeptide word vector features were fed into the stacked model for multiple rounds of training. The model performed excellently on independent dataset test, with accuracy and area under the curve of 80.51% and 87.36%, respectively. Altogether, DeepCBA achieved superior performance compared with the baseline model, and significantly outperformed most existing predictors. Additionally, our model can be used to identify disease loci and drug targets.

Improvement and implementation of central sterile supply department training program based on action research.

This study aimed to improve and implement the central sterile supply department (CSSD) training program through action research and to evaluate its effect. The project was underpinned by action research. The problems that occurred in training were identified following scenario analysis, interviews, group discussions, and expert consultation to develop improvement measures. The training program characterized by CSSD was developed in the spiral circular process of "Plan-Act-Observe-Reflect". After the two rounds of training, the CSSD nurses significantly improved their professional knowledge, skills, and satisfaction with training compared with those before improvement. The nurses' overall satisfaction with the training and their performance improved. The implementation of the training program designed based on action research can improve CSSD nurses' professional knowledge and skills and increase their enthusiasm for learning, laying the foundation for CSSD talent development.

Deep learning and image preprocessing-based crack repair trace and secondary crack classification detection method for concrete bridges

As a main type of vulnerability, a large number of cracks exists in concrete bridges, and some cracks will be secondary dehisced after repair, and the crack repair traces (CRTs) or the secondary cracks (SCs) are easily confused with concrete spalling and other defects during intelligent disease detection. Therefore, accurate classification of CRT and SC is crucial for effective detection of concrete bridge diseases. To achieve precise classification, the images of CRT and SC will be processed. Initially, images undergo Poisson-noise and Bilateral-filtering. Subsequently, image segmentation is performed using the OTSU algorithm. The resulting binary images, along with the unprocessed original images, are utilised for training the YOLOv7 target detection network. Finally, the trained model is verified by a test set containing negative samples. The training results show that after several rounds of iterative training, the binarized image detection model achieves exceptional performance, accurately classifying and detecting CRTs or SCs with an accuracy rate of 98.4%. In contrast, the accuracy rate for the original image is only 39.9%. The inference results obtained from the test set further validate the method’s rationality and feasibility, with a significant reduction in misclassification probabilities observed after 200 rounds of iterative training.

How Outreach Training and Supportive Supervision (OTSS) Affect Health Facility Readiness and Health-Care Worker Competency to Prevent and Treat Malaria in Niger: A Secondary Analysis of OTSS Data.

The quality of health services is key to the goal of averting morbidity and mortality from malaria. From July 2020 to August 2021, PMI Impact Malaria supported the implementation of four rounds of Outreach Training and Supportive Supervision (OTSS) in 12 health districts in the two regions of Niger: Dosso and Tahoua. Through OTSS, trained supervisors conducted onsite visits to observe an average of 174 healthcare workers (HCWs) per round in 96 public primary health facilities, managing persons with fever or conducting antenatal care (ANC) consultations, and then provided instant and individualized feedback and onsite training. Data from health facility readiness, case management, and malaria in pregnancy (MiP) checklists across the four rounds were analyzed using Wilcoxon's and the χ2 tests. These analyses highlighted improved facility readiness, including an increased likelihood that HCWs had received classroom training, and facilities had increased availability of guidelines and algorithms by round 4 compared with round 1. Median HCW performance scores showed an improvement in the correct performance and interpretation of malaria rapid diagnostic tests, in classification of malaria as uncomplicated or severe, and in the management of uncomplicated malaria across the four rounds. For MiP services, malaria prevention and the management of pregnant women with malaria also improved from round 1 to round 4. These findings provide further evidence that OTSS can achieve rapid improvements in health facility readiness and HCW competency in managing outpatients and ANC clients.

Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting

Operational flood prevention platforms and systems rely upon the advance notice provided by flood forecasts to formulate efficient measures for flood mitigation. Capturing complex spatial heterogeneity and correlation of hydro-meteorological variables is fundamentally challenging for artificial neural networks. This challenge becomes even more significant when the complexity involved introduces systematic biases and time-lag phenomena in flood forecasts. For the first time, this study proposed a Spatiotemporal Hetero Graph-based Long Short-term Memory (SHG-LSTM) model for multi-step-ahead flood forecasting. The case study focused on the Jianxi basin in China. 25,341 hydro-meteorological data, with a temporal resolution of three hours, collected during flood events were divided into training and test datasets for model construction purpose. The model was fed with 3-h streamflow and precipitation data from 23 gauge stations, covering a time span of the preceding 21 h, for generating flood forecasts at 1 up to 7 horizons. To make a comparative analysis, both LSTM and the Spatiotemporal Graph Convolutional Network (S-GCN) were constructed. This study conducted multiple rounds of model training with varying initial parameters to assess the accuracy, stability, and reliability of the LSTM, S-GCN, and SHG-LSTM models. The results demonstrated that the SHG-LSTM model outperformed LSTM and S-GCN models, with an average reduction in the volume error (VE) of 6.5% and 11.1%, respectively, a decrease in the Mean Absolute Error (MAE) of 6.7% and 8.1%, respectively, and a reduction in the Root Mean Square Error (RMSE) of 5.0% and 12.9%, respectively. Furthermore, the SHG-LSTM model not only could efficiently overcome the under-prediction bottleneck, but also could largely mitigate the time-lag phenomenon in flood forecasts, even during the testing stages. These findings indicate that the proposed SHG-LSTM model can provide a general framework for modelling the spatial heterogeneity and correlation of hydro-meteorological variables and achieve accurate and reliable flood forecasts, thereby enhancing the model’s applicability in flood prevention platforms and systems.

Further exploration on deep learning in flower recognition

Flower recognition is an important research direction in the field of computer vision, and automatic classification of flower images through deep learning methods is of great significance for ecological environment monitoring and plant research. With this background, this study aims to further optimize the existing flower recognition system and improve its classification accuracy by adjusting key parameters. Based on the existing deep learning model and several rounds of training, this paper explores the tuning strategies for parameters such as different learning rates and weight decay to achieve higher recognition accuracy. In the experiments, this paper uses the classical flower dataset and enhances the diversity of the data through image preprocessing and data enhancement. Through a hundred rounds of training, the model in this paper achieves about 80% classification accuracy on the test set, which is significantly improved compared with the initial model. Further analysis of the results shows that by reasonably adjusting the learning rate and weight decay parameters, the model achieves certain improvements in different flower classes, demonstrating the impact of parameter tuning on the overall performance of the model.

Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning.

Accurately classifying and identifying non-cooperative targets is paramount for modern space missions. This paper proposes an efficient method for classifying and recognizing non-cooperative targets using deep learning, based on the principles of the micro-Doppler effect and laser coherence detection. The theoretical simulations and experimental verification demonstrate that the accuracy of target classification for different targets can reach 100% after just one round of training. Furthermore, after 10 rounds of training, the accuracy of target recognition for different attitude angles can stabilize at 100%.

The application of the BOPPPS model in the ward rounds of nurses' standardized training in Southwest China: a mixed methods study.

Teaching ward rounds are the main teaching method used to develop clinical skills in standardized nursing training. However, the existing methods lack of cultivation of comprehensive ability and humanistic care for nurses, cannot meet the requirements of standardized training for nurses. BOPPPS (bridge-in, objective, pre-assessment, participatory Learning, post-assessment, and summary) is a student-centered teaching model that has been proven to enhance classroom teaching effectiveness. Therefore, the BOPPPS model was applied and its effectiveness in standardized nursing training was evaluated. In total, 260 nursing students were randomly allocated to two groups: the experimental group used the BOPPPS model and the control group used the traditional teaching model. This study used a mixed quantitative and qualitative research method to evaluate the effectiveness of the BOPPPS model. The quantitative results were as follows: no significant difference in baseline scores was observed between the two groups before training. After training, the theory and practical scores in the experimental group were significantly higher than that of the control group. Similarly, students in the experimental group presented higher comprehensive ability scores than their counterparts. The students in the experimental group also exhibited higher satisfaction compared to the control group, while there was no difference in teacher satisfaction scores between the two groups (p = 0.323). Qualitative data showed that the vast majority of nurses and teachers agreed on the value of BOPPPS training. Compared to traditional teaching methods, the BOPPPS model was more effective in standardized nursing training. We recommend applying the BOPPPS model to nursing training.

Pedestrian Target Recognition Algorithm in Public Places Based on Representation Learning and Similarity Learning

To ensure public safety, the government has placed a lot of cameras in public places and used them to monitor key targets. In target detection, pedestrian target detection is undoubtedly a research hotspot. How to realize the high efficiency of pedestrian detection is the focus of this field. As a result, this research suggests an algorithm for pedestrian target detection in public spaces that is based on representation learning and similarity learning. The algorithm uses representation learning to extract pedestrian features and Singular Value Decomposition (SVD) to build a more trustworthy Feature Extraction Network (FEN). In addition, the improved Softmax function is used for similarity learning, and the K-Nearest Neighbor algorithm (KNN is applied for image retrieval to fgreatly increase the identification accuracy of pedestrians. The algorithm proposed in this study only needs two rounds of constraint training to achieve the best state. The mean absolute error and mean square error are 0.31 and 9.38, respectively. Its Relative Robustness (RR), Relative Generalization (RG) and Relative Scalability (RS) are excellent. In the final practical test, the model finally achieved 98.8% accuracy and 2.1% false positive rate. The proposed algorithm in this study has good application value in pedestrian target detection, and can better promote the development of social public safety