Application Value Research Articles

Coronary heart disease evaluation using PCAT radiomics model based on coronary CT angiography and pericoronary adipose tissue.

To explore the clinical application value of radiomics model based on pericoronary adipose tissue (PCAT) in predicting coronary heart disease. A retrospective analysis was performed for inpatients who had undergone coronary computed tomography angiography from January to December 2023, and 164 cases of coronary artery lesions were screened as the lesion group and 190 cases of normal coronary artery samples were selected as the control group. The clinical data and imaging data of all patients were collected, the radiomics features were extracted by relevant software, and the "region of interest" of pericoronary fat was delineated, and the selection operator and multivariate logistic regression were used to screen the radiomic features of pericoronary fat. A coronary heart disease evaluation model was constructed by the best radiomics features. Area under the curve values of the PCAT radiomics scoring model for predicting the receiver operating characteristic curve of coronary heart disease were 0.863 and 0.851 in training and test sets, respectively. After calibration curve analysis, PCAT radiomics scoring model has a high consistency between the predictive evaluation results and the actual results of coronary heart disease events. In addition, in the training set, the PCAT radiomics scoring model has a net benefit on all threshold probabilities. In the test set, the model has a negative net return with only a small number of threshold probabilities. After combining the clinical characteristics model, the evaluation accuracy of the model for coronary heart disease can reach 0.896. PCAT radiomics model based on coronary computed tomography angiography can effectively predict and evaluate coronary heart disease, which is of great value for the clinical diagnosis of coronary artery disease.

The value of postoperative contrast-enhanced ultrasound parameters in lymph node metastasis, tumor node metastasis, and treatment response evaluation of resected hepatocellular carcinoma.

To explore the application value of postoperative contrast-enhanced ultrasound (CEUS) parameters for lymph node metastasis (LNM), tumor, node, metastasis staging, and treatment response evaluation of resected hepatocellular carcinoma (HCC). We retrospectively analyzed 100 patients with liver cancer who underwent liver CEUS at our hospital between October 2020 and October 2022. The patient's LNM, pathological staging, and therapeutic effects were recorded based on the histopathological results. CEUS parameters were analyzed and compared CEUS parameters between different lymph node metastases, pathological stages, and therapeutic effects. Twenty-three patients experienced LNM, 77 patients did not experience LNM; and the rise time (RT), peak intensity (PI), and area under the curve (AUC) of the metastatic group were significantly smaller than those of the nonmetastatic group (P < .05). 44 cases were classified into groups I to II by pathological staging, and 56 cases were classified into groups III to IV. The RT, PI, and AUC of groups III to IV were significantly lower than those of groups I-II (P < .05). Seventy-nine cases were complete necrosis, 21 cases were residual or recurrent; The RT, PI, and AUC of the residual or recurrent group were significantly lower than those of the complete necrosis group (P < .05). The receiver operating characteristic curve shows that RT, PI, and AUC have a certain value in evaluating LNM, pathological staging, and treatment response of HCC, and the combined evaluation/evaluation value of these 3 factors is relatively high. The postoperative CEUS parameters RT, PI, and AUC can be used for LNM, pathological staging evaluation, and treatment response evaluation of HCC. Moreover, the combination of the 3 parameters is feasible and valuable in evaluating LNM, tumor, node, metastasis staging, and treatment response of HCC.

Application value of SOMATOM Force computed tomography in assisting the preoperative localization of colorectal cancer resection surgery

Background The objective of this study was to assess the application value of SOMATOM Force computed tomography (CT) in assisting the preoperative localization of colorectal cancer resection surgery. Method Retrospectively, the medical data of 120 inpatients with colorectal cancer were collected. The Kappa consistency test was used to evaluate diagnostic consistency in the localization and staging of colorectal cancer. The diagnostic value of preoperative SOMATOM Force CT detection was analyzed. Results In 120 colorectal cancer patients, the accuracy of SOMATOM Force CT for preoperative localization, T staging, and N staging of colorectal cancer were 91.7% (kappa = 0.837), 88.3% (kappa = 0.772) and 91.7% (kappa = 0.773), respectively. Among 45 rectum cancer patients, there were 19 positive cases with circumferential resection margin involvement, and the accuracy of SOMATOM Force CT detection was 86.7% (kappa = 0.767). The sensitivity, specificity, positive predictive value, and negative predictive value of SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer were 78.95%, 96.15%, 93.75%, and 86.21%, respectively. Conclusions There was an important application value of SOMATOM Force CT in assisting the preoperative localization and tumor staging of colorectal cancer resection surgery. There was a good diagnostic value of preoperative SOMATOM Force CT detection in evaluating the circumferential resection margin involvement of rectum cancer.

MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden.

To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H). A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value. Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model. The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Meta-analysis of RCTs on the safety of non-fixation of mesh in TAPP inguinal hernia repair: an updated meta-analysis

ObjectiveThis meta-analysis aims to compare the clinical efficacy of mesh non-fixation and fixation in laparoscopic transabdominal preperitoneal (TAPP) inguinal hernia repair, systematically evaluating the application value of the mesh non-fixation technique in clinical settings.MethodsA computerized search of PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov databases was conducted to identify randomized controlled trials (RCTs) comparing mesh non-fixation and fixation in TAPP inguinal hernia repair. Meta-analysis was performed using RevMan 5.3 software, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) evidence grading system was employed for outcome quality assessment. Publication bias analysis was performed using Begg’s test. A trial sequential analysis (TSA) was performed using TSA 0.9.5.10 Beta software.ResultsA total of nine RCTs involving 1,879 inguinal hernia patients were included. Meta-analysis results demonstrated that, compared to the fixation group, the non-fixation group exhibited significantly lower seroma occurrence rate [RR = 0.43, 95% CI (0.20, 0.89), P = 0.02, heterogeneity P = 0.28, I²=22%], Visual Analog Scale (VAS) pain score at 6 months postoperatively [MD=-0.21, 95% CI (-0.29, -0.12), P < 0.00001, heterogeneity P = 0.34, I²=0%], and cost [MD=-3.23 thousand yuan, 95% CI (-4.26, -2.19), P < 0.00001, heterogeneity P = 0.0003, I²=92%]. There were no statistically significant differences in overall complication rate [RR = 0.88, 95% CI (0.62, 1.23), P = 0.45, heterogeneity P = 0.11, I²=44%], overall infection event rate [RR = 0.96, 95% CI (0.36, 2.56), P = 0.93, heterogeneity P = 0.62, I²=0%] and recurrence rate [RR = 0.75, 95% CI (0.28, 1.99), P = 0.56, heterogeneity P = 0.44, I²=0%] between the two groups. The results of the TSA indicated that the observed lower seroma occurrence rate in the non-fixation group compared to the fixation group requires further validation through the inclusion of additional RCTs.ConclusionMesh non-fixation in TAPP inguinal hernia repair is deemed safe and does not elevate the risk of hernia recurrence. However, given certain limitations in this study, future comprehensive and reliable validation will require further multicenter, high-quality, large-sample double-blind RCTs.

Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer

PurposeTo explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).Materials and MethodsA total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significant differences were used as input variables for the extreme learning machine (ELM) classifier to construct the clinical model. We used the transfer learning strategy to train the ResNet18 model, used the model to extract deep learning features from all CT images, and then used the ELM classifier to classify the deep learning features to obtain the deep learning signature (DLS). A MDLR model incorporating clinicopathological characteristics, subjective CT findings and DLS was constructed. The diagnostic efficiencies of the clinical model, DLS model and MDLR model were evaluated by the area under the curve (AUC).ResultsUnivariate analysis indicated that size (p = 0.004), neuron-specific enolase (NSE) (p = 0.03), carbohydrate antigen 19 − 9 (CA199) (p = 0.003), and pathological stage (p = 0.027) were significantly associated with the progression of solid stage I NSCLC after surgery. Therefore, these clinical characteristics were incorporated into the clinical model to predict the risk of progression in postoperative solid-stage NSCLC patients. A total of 294 deep learning features with nonzero coefficients were selected. The DLS in the progressive group was (0.721 ± 0.371), which was higher than that in the nonprogressive group (0.113 ± 0.350) (p < 0.001). The combination of size、NSE、CA199、pathological stage and DLS demonstrated the superior performance in differentiating postoperative progression status. The AUC of the MDLR model was 0.885 (95% confidence interval [CI]: 0.842–0.927), higher than that of the clinical model (0.675 (95% CI: 0.599–0.752)) and DLS model (0.882 (95% CI: 0.835–0.929)). The DeLong test and decision in curve analysis revealed that the MDLR model was the most predictive and clinically useful model.ConclusionMDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.

Unravelling the physiological and molecular mechanisms of leaf color change in Acer griseum through multi-omics analysis

Paperbark maple (Acer griseum), an endemic and endangered wild plant in China, has red-colored autumn leaves of high ornamental and garden application value. Leaf color change serves as a crucial indicator for evaluating garden tree aesthetics; however, research on A. griseum's leaf color change remains limited. This study aims to elucidate the physiological and molecular mechanisms underlying leaf color change in maple leaves through physiological, transcriptional, and metabolic assays. Data analysis encompasses gene expression levels and metabolite changes in three distinct states of maple leaves: green, half-red, and red. The progessive decrease of chlorophyll and carotenoids and the continuous accumulation of anthocyanidins caused a sharp change in leaf coloration, which was most drastic in the green to half-red period. Subsequently, targeted metabolomics analysis was performed, and a total of 71 anthocyanidins were detected, and the content of eight types of anthocyanidins increased significantly in the half-red and red periods, compared with that in the green period; of which the multiplicative difference was the largest for cyanidin-3,5-O diglucoside, delivering the largest multiplicative difference. Thus, it was plausible that cyanidin-3,5-O-diglucoside-dominated compoundswere likely to be the main metabolites associated with leaf reddening. Correlation analysis revealed that 12 key transcription factors (TFs) were significantly correlated with the anthocyanin-related metabolites and structural genes, which play important regulatory roles during the biosynthesis of anthocyanosides in A. griseum. These findings offered useful insights into the molecular basis of leaf color variation in A. griseum; providing valuable information to guide targeted genetic breeding and varietal improvement strategies.

The Effectiveness of Tiktok in Increasing Brand Awareness Among Users

Technological advancements have revolutionized marketing strategies, particularly in the realm of social media. As businesses recognize the importance of brand awareness in consumer decision-making, platforms like TikTok have emerged as powerful tools for enhancing brand visibility. Despite its rapid rise in popularity, TikTok's effectiveness in increasing brand awareness remains underexplored. This study investigates "The Effectiveness of TikTok in Increasing Brand Awareness Among Users" with three primary objectives: (RO1) to identify the effect of Mobile Application Values, Information Quality, System Quality, and Influencer Trust on Brand Awareness; (RO2) to identify the effect between Brand Awareness and Word of Mouth; and (RO3) to analyze the effect between Brand Awareness on Purchase Intention. The research adopts a quantitative approach, utilizing a questionnaire distributed to B2C TikTok users. A sample size of 343 respondents was determined using stratified sampling. Descriptive and regression analyses were conducted to interpret the data. The results indicate that Mobile Application Value, System Quality, and Influencer Trust significantly influence Brand Awareness on TikTok, while Information Quality does not exhibit a significant effect. Moreover, Brand Awareness positively impacts both Word of Mouth and Purchase Intention, underscoring its pivotal role in consumer behavior. This study contributes to a deeper understanding of TikTok's potential as a branding tool and offers practical insights for marketers seeking to leverage the platform effectively. Despite limitations in sample size and survey biases, the findings provide a foundation for future research and the refinement of marketing strategies in the dynamic landscape of social media.

3D-printed magnesium-doped micro-nano bioactive glass composite scaffolds repair critical bone defects by promoting osteogenesis, angiogenesis, and immunomodulation

Magnesium ions play an important immune-regulatory role during bone repair. For this study, we prepared micro-nano bioactive glass (MNBG) containing magnesium, which can release magnesium, silicon, and calcium ions and has a positive impact on osteogenic differentiation and vascular regeneration. In this study, MgMNBG was compounded and combined with poly(lactic-co-glycolic acid (PLGA) and polycaprolactone (PCL) for 3D printing. Afterwards, the physicochemical properties and bone repair performance of the scaffolds were evaluated throughin vitroandin vivoexperiments. We also investigated the effects of MgMNBG on osteogenic differentiation, immune regulation, and vascular regeneration. The results showed that MgMNBG can inhibit inflammation and promote osteogenesis and angiogenesis by regulating macrophages. PLGA/PCL/MgMNBG scaffolds have good osteogenic and angiogenic effects, and the composite scaffolds have excellent bone repair performance and potential application value.

Obtaining Highly Efficient Radon Adsorbents by Adjusting the Pore Structure of Bamboo Charcoal with Particle Size Fractionated Activation.

To achieve highly efficient adsorbents for radon (Rn), this study prepared bamboo activated carbon (BAC) using bamboo charcoal as the precursor and KOH and NaOH as activators and employed a method of activation by adjusting the pore structure through particle size fractionation. Under the same activation conditions, KOH was consumed less and showed better Rn adsorption performance than NaOH, and the solid milling method saved more activator dosage compared to the liquid immersion method. After carbonizing the bamboo, the highest Rn adsorption coefficients of the bamboo charcoal activated with particle size fractionation (0.43-0.85, 0.25-0.43, 0.18-0.25, and <0.18 mm) were 8.41 ± 0.05, 10.03 ± 0.24, 9.31 ± 0.01, and 8.15 ± 0.21 L/g, respectively under N2 conditions (at a temperature of 25 °C and humidity of 35%), which were significantly greater than those of bamboo charcoal with overall activation (7.05 ± 0.23 L/g). After optimization, all of the bamboo charcoals with different particle sizes had the highest volume ratio for pores with a diameter of 0.77 nm. BC0.43-0.85K1.25 and BC0.25-0.43K0.75 maintained 95.74 and 92.36%, respectively, of the original adsorption coefficient after 5 cycles of regeneration under nitrogen. As the bamboo charcoal particle size decreased, the optimal mass ratio of alkali/carbon also decreased. This indicates that particle-size-fractionated activation can reduce the raw material and activator waste. The optimized BAC has a significantly higher Rn adsorption coefficient than the currently used coconut shell activated carbon (4.13 ± 0.51 L/g), making it an efficient and economical adsorbent with excellent application value for radon removal.

Web log mining techniques to optimize Apriori association rule algorithm in sports data information management

To optimize the current college sports data information management system, this study combines the Apriori association rule algorithm with web application development technology to upgrade the management system. Firstly, this study explores novel log mining techniques in genetic algorithms and web application development technology. Secondly, by integrating log mining techniques to optimize the Apriori algorithm, associations between sports data and information are discovered. Through the optimized algorithm, this study identifies key association rules of sports data information and validates the optimized system’s reliability and effectiveness through experiments. The experimental results show that the running time of the traditional Apriori algorithm exponentially grows with the increase in information volume, while the optimized execution efficiency is improved by approximately 10–15%. Additionally, the average retrieval accuracy of this optimized system can reach 98.3%, although the retrieval time also increased by 23%. Therefore, the technology and algorithms proposed in this study have certain application value in the sports information management system and contribute to the optimization of data information management in this field.

CuFe Cooperativity at the Membrane-Electrode Interface Elicits a Tandem 2e-+2e- Mechanism for Exclusive O2-To-H2O Electroreduction.

High O2 reduction reaction (ORR) kinetics and exclusive 4e- pathway selectivity are keys to realizing a sustainable society. However, nonprecious electrocatalysts at present cannot enhance the ORR turnover frequency and H2O Faradaic efficiency (FE) concurrently. To address these two challenges, hybrid bilayer membrane (HBM) electrodes with earth-abundant metal centers are developed to control proton-coupled electron transfer (PCET) in ORR. Here, an oxidase-inspired CuFe active site is supported on a tris(2-pyridylmethyl)amine HBM and explored as a unique interface for efficient ORR. This bimetallic HBM displayed an ORR activity 1.4 times higher than the monometallic systems and exhibited the highest FE for H2O (∼94%) among Cu-, Fe-, Ni-, and Co-based HBMs. Contrary to previous studies where the ORR current decreases upon embedding the metal center in a hydrophobic lipid environment, here, the incorporation of a nitrile-terminated proton carrier at the HBM interface boosts the ORR current by 1.7 folds relative to the case where the catalytic site is directly exposed to protons in solution. This intriguing dual improvement is supported by density function theory calculations where an additional 2e-+2e- mechanism occurs in parallel to the direct 4e- pathway, highlighting the synergistic effect of the CuFe HBM for facilitating high-performance ORR. A Zn-air battery is constructed using this CuFe HBM for the first time, further demonstrating that the knowledge gained from this HBM technology holds practical values in real-life applications. These findings on interfacial PCET are envisioned to spark new design principles for future catalysts with optimal electrochemical properties for advanced energy conversion schemes.

Design of an Energy-Saving System for Smart Home Decoration Based on Situational Awareness

This article analyzes the specific energy-saving requirements for smart home decoration in the current market to determine the overall plan of the entire smart home decoration energy-saving system. Based on situational analysis combined with grounded theory, organize and summarize the original data, and use factor analysis for dimensionality reduction analysis. Combining the multi-level analysis method, experts evaluated and scored the user experience evaluation indicators, obtained the weight assignment of each influencing factor, and constructed a user experience evaluation model for smart home products based on situational theory. After extensive testing, it was found that the ZigBee based smart home network implemented in this article has low energy consumption, overcomes many shortcomings of smart home networks, and has high application value.

Fine structure control of Fe3O4/ Fe2O3 film by Nitridation-Oxidation-Reduction treatment and high corrosion resistance

The nitriding post-oxidation composite treatment is clean and pollution-free, and the high corrosion resistance of the modified layer has aroused extensive research interest. The phase composition and microstructure of Fe3O4/Fe2O3 film are key factors determining corrosion resistance. We propose a Nitridation-Oxidation-Reduction (NOR) treatment technology nitriding followed by oxidation treatment and then reduction treatment. The precise phase composition and microstructure of the oxide film were characterized and analyzed, and the corrosion resistance of the oxide film was evaluated. Through reduction treatment, the Fe3O4 phase content increased from 54.95 % to 84.75 %, the corrosion potential increased by 0.07 V and the corrosion current density decreased by 2.06 × 10-6A. After NOR treatment, the oxide film has extremely high corrosion resistance, which will have important theoretical and engineering application value.

Global transcription and metabolic profiles of five tissues in pepper fruits

Studying the regulatory mechanisms in different tissues of pepper is crucial for understanding organ formation, growth, and development. However, relevant studies are far from sufficient. In the current study, the stipe, calyx, pericarp, placenta, and seed of ripe pepper were sampled, and metabolites were determined by the untargeted metabolomics method. Transcriptome sequencing was performed by Illumina NovaSeq 6000, and then a high-throughput data set was built. The results showed that a total of 4879 annotated metabolites were detected in 15 samples of the five tissues under positive and negative ion mode. A total of 110.66 Gb of clean data was obtained by transcriptome sequencing, the clean data of each sample reached 6.21 Gb, and a total of 35 336 annotated expression genes were obtained. Furthermore, validate the accuracy of the data by combining principal component analysis and other methods. In summary, this study provides valuable information for the genetic improvement and breeding of peppers, and it holds potential application value, particularly in enhancing the quality and nutritional value of pepper fruits.

Kilowatt-level supercontinuum generation employing a homemade taper-shaped ytterbium-doped fiber amplifier.

A simple generation method of high-power supercontinuum (SC) based on a homemade long ytterbium-doped taper fiber (T-YDF) amplifier has been demonstrated experimentally and analyzed in this work. The power and spectra of the obtained SC are made adjustable by changing the seed pulse repetition rates. Under a 7.5 MHz seed pulse repetition rate, a SC output is obtained with a spectral range of 1000 nm-1650 nm, maximum output power of 1066 W, and conversion efficiency of 75.7%. The core/clad diameters of the homemade T-YDF are ∼20/400 µm and ∼30/600 µm at the input and output ends, respectively, and the total length is 25 m. Such a long taper fiber can further enhance the properties of the SC while ensuring high power and good beam quality. To the best of the authors' knowledge, this is the first report of directly generating a high average power SC based on the T-YDF amplifier, which provides a proof-of-concept experiment to achieve a high average power SC source and greatly improves the potential application value of the SC source.

Research on Fraud Detection Method of Financial Data of Listed Companies Based on HMCRAN

This study investigates the application of deep learning technology to improve accuracy and efficiency in detecting financial fraud in listed companies. Using data from the China Economic and Financial Research Database on Main Board A-share listed companies with disclosed fraudulent behaviors, a hybrid multi-channel residual attention network (HMCRAN) was constructed, combining multi-channel residual networks and transformers. The study involved feature extraction, data preprocessing, model training, and performance evaluation, validating the superiority of the HMCRAN model for financial fraud detection. Experimental results demonstrate that the model excels in handling high-dimensional financial data and capturing long-term dependencies, significantly improving detection accuracy and robustness. The HMCRAN model outperforms traditional machine learning methods across various evaluation metrics, demonstrating its high practical application value.

Optimization of a Typical Gas Injection Pressurization Process in Underground Gas Storage

In the early construction of an underground gas storage facility in an oil and gas field in southwest China, the increasing gas injection volume led to a continuous rise in energy consumption, which affects the economic sustainability of gas injection and extraction. In order to improve efficiency and reduce energy consumption, optimization of the pressurization process was carried out. An optimization model for the process of pressurization in underground gas storage has been established. Based on the model, a joint optimization approach is applied, where MATLAB is responsible for the iterative process of finding the optimal parameter combinations and HYSYS is responsible for the establishment of the process and calculation of the results of the process parameters. The key parameters include the outlet parameters of the compressor and the air cooler, which are critical in determining the overall energy consumption and operational performance of the system. Accordingly, the results related to the optimal parameter combinations for two-stage compression and three-stage compression were obtained in the case study. Compared with one-stage compression, two-stage and three-stage compression can diminish energy consumption by 1,464,789 kJ/h and 2,177,319 kJ/h, respectively. The reduced rate of energy consumption of three-stage compression was 16.10%, which was higher than that of two-stage compression by 10.83%. Although the construction costs of three-stage compression were higher than those of two-stage compression, from the perspective of long-term operation, three-stage compression had lower operating costs and superior economy and applicable value. The research results provided scientific references and new ideas for the optimization and adjustment of the pressurization process in underground gas storage.

Research on Blended Teaching of College English Writing Based on 'POA' Theory Under the Background of Big Data

This paper discusses the application value of the writing teaching mode combined with the mixed teaching mode in college English writing teaching against the background of big data. Focusing on production-oriented approach (POA) theory, this paper proposes a mixed learning writing model for English teaching and applies the POA mixed learning English writing teaching model to practical teaching by using the cloud classroom platform, collecting students' big data, and analyzing students' seriousness, classroom participation, satisfaction, and English writing performance. Although the overall student engagement is not high, their classroom satisfaction is very high. Research shows that after the teacher adjusted the lesson plan and encouraged students to participate more in class interactions, their scores improved in the second exam but declined in the third exam . The levels of engagement, participation, and satisfaction have significantly decreased across all grades. The POA combined with the blended learning mode has a great application value in practical teaching in the era of big data.

Evaluation of Boundary Layer Characteristics at Mount Si’e Based on UAV and Lidar Data

The atmospheric boundary layer is a crucial transitional region connecting the surface with the free atmosphere, playing a bridging role in land-sea-air interactions and the interactions between different atmospheric layers. This study utilizes rotary-wing UAVs, high-resolution lidar, and WRF simulation data to analyze the vertical distribution characteristics of temperature, humidity, wind speed, and wind direction boundary layer over the Mount Si’e region in 4–6 April 2024. The results indicate that the boundary layer temperature decreases with increasing altitude, reaching up to 18°C, while humidity decreases with height, dropping to as low as 35%. Daytime wind speeds range from 4 to 8 m/s, decreasing to 2 to 4 m/s at night. The boundary layer height can reach up to 900 m during the day and drops to 100–200 m at night, showing distinct diurnal variation characteristics. UAV observations are in good agreement with lidar and WRF simulation results, highlighting the application value of UAVs in high temporal and spatial resolution boundary layer studies. The study also reveals the significant impact of complex terrain on boundary layer characteristics, providing scientific insights into the dynamic and thermal processes of the boundary layer and offering reference value for improving regional weather forecasting and numerical simulations.