The rapid development of online education, knowledge sharing, big data, and artificial intelligence technology has brought innovation to education. With the popularization of online education, the variety of teaching resources and the amount of curriculum resources have exploded, and traditional curriculum platforms have been unable to meet the growing demand. The paper builds a cloud computing platform based on Hadoop, which is used for big data analysis and application curriculum resource management. It elaborates on the architecture, functional design, resource storage, and implementation and test of the cloud resource platform. The cloud platform applies to and serves the field of education, which improves resource utilization and sharing, and it can provide better services for teachers, students, and staff.

A new generation of ONLINE assays has been developed that offers improved performance and enhanced ease of use. This family of assays is being applied to both the COBAS INTEGRA and Roche/Hitachi line of analyzers. The four ONLINE DAT II assays that were evaluated included cocaine (benzoylecgonine) (BE), methadone (MDN), opiates (OP), and tetrahydrocannabinol (THC). The BE assay has a dual cutoff (150/300 ng/mL) with a dynamic range from 0 to 5000 ng/mL. The MDN assay has a cutoff of 300 ng/mL with a dynamic range from 0 to 2000 ng/mL. The opiates assay has a 300 ng/mL cutoff with a 0 to 2000 ng/mL range and a 2000 ng/mL cutoff with a 0 to 8000 ng/mL range. The THC assay has 20, 50, and 100 ng/mL cutoffs with 0 to 100, 0 to 300, and 0 to 300 ng/mL dynamic ranges, respectively. The ranges of the intra-assay precision (coefficients of variation for n = 20) run in the semiquantitative mode are 2.3–7.5% for BE, 2.0–3.8% for MDN, 1.9–4.2% for OP, and 3.9–5.2% for THC. The intra-assay qualitative precision for all of the assays as calculated from absorbance values is generally higher than that of the intra-assay semiquantitative precision at the cutoff. The qualitative precision ranges between 0.4% and 3.1%. The standard curve stability defined for the COBAS INTEGRA systems for these reagents ranges from 35 to 68 days. The clinical sensitivity and specificity were compared to the OnLine generation I and CEDIA^® immunoassays, as well as gas chromatography-mass spectrometry (GC-MS). The results indicate that for each assay, the sensitivity and specificity were the same or greater when compared to the other two immunoassay technologies. The results of each assay also correlated very well (> 99%) when compared with GC-MS.

Detecting defects in transmission line insulators is crucial to prevent power grid failures as power systems continue to expand. This study introduces YOL011n-SSA, an enhanced insulator defect detection technique method that addresses the challenges of effectively identifying flaws in complex environments. First, this study incorporates the StarNet network into the backbone of the model. By stacking multiple layers of star operations, the model reduces both parameter count and model size, improving its adaptability to real-time object detection tasks. Secondly, the SOPN feature pyramid network is introduced into the neck part of the model. By optimizing the multi-scale feature fusion of the richer information obtained after expanding the channel dimension, the detection efficiency for low-resolution images and small objects is improved. Then, the ADown module was adopted to improve the backbone and neck parts of the model. It effectively reduces parameter count and significantly lowers the computational cost by implementing downsampling operations between different layers of the feature map, thereby enhancing the practicality of the model. Meanwhile, by introducing the NWD to improve the evaluation index of the loss function, the detection model’s capability in assessing the similarities among various small-object defects is enhanced. Experimental results were obtained using an expanded dataset based on a public dataset, incorporating three types of insulator defects under complex environmental conditions. The results demonstrate that the YOLO11n-SSA algorithm achieved an mAP@0.5 of 0.919, an mAP@0.5:0.95 of 70.7%, a precision of 0.95, and a recall of 0.875, representing improvements of 3.9%, 5.5%, 2%, and 5.7%, respectively, when compared to the original YOLO1ln method. The detection time per image is 0.0134 s. Compared to other mainstream algorithms, the YOLO11n-SSA algorithm demonstrates superior detection accuracy and real-time performance.

It is the flourishing development of MOOCs that brings new mode of educa-tion. The paper focuses on the improvement of the teaching methods of pro-gramming experiment course in the environment of MOOCs, the develop-ment of experimental platform for innovative teaching methods, and the as-sessment methods of student learning. The current research and use of MOOCs are discussed, and its application in the programming experiment course. According to the present research analysis, the online programming experiment course platform is built, and a kind of online assessment system for students' achievement evaluation methods is elaborated. The academic performance and interest in learning are improved when experiment course platform is applied through two years teaching comparison. It is feasible to adopt the online platform for programming experiment course.

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 10⁴ plant/ha and 161.05 kg·ha⁻¹ for the large-spike type variety of rice, 75.08 × 10⁴ plant/ha and 159.52 kg·ha⁻¹ for the intermediate type variety of rice, and 75.08 × 10⁴ plant/ha and 133.47 kg·ha⁻¹ for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.