Problems and Development Suggestions of Nature Education in Jiangxi Province

The forest coverage rate of Jiangxi Province ranks second in China.It has rich natural resources,a long history of ancient color culture and rich red culture.In the development of nature education,Jiangxi Province has great potential and advantages.This paper introduces the development conditions of nature education in Jiangxi Province,summarizes the problems existing in the development of nature education in Jiangxi Province from the aspects of the types of nature education and the construction of nature education base,such as simple content and single form,imperfect infrastructure and lack of professionals,and puts forward some suggestions on the development of nature education in Jiangxi Province.

Influencing Factors and Improvement Suggestions for Agricultural Green Development Performance:Empirical Insights from China

Agricultural green development is an essential direction for global sustainable agriculture.The academic literature,however,needs to place greater emphasis on studying the factors influencing agricultural green development performance and how such performance can be improved.A theoretical framework for agricultural green development performance was constructed in this paper using the Super-SBM model,which considers undesirable outputs,to measure the agricultural green development performance of 330 cities at or above the prefecture level in China(excluding Tibet Autonomous Region,Hong Kong,Macao and Taiwan of China)from 2007 to2018.Furthermore,the influencing mechanism of agricultural green development performance was then analyzed using a spatial econometric model.The results show that:1)from 2007 to 2018,China’s agricultural green development performance experienced three stages of evolution:‘rise,decline and rise’.2)The regions with high performance agricultural green development are mainly distributed in eastern China,northeastern China,and southern Qinghai Province.3)The agricultural economic level,industrialization process,and labor quality play significant roles in promoting local agricultural green development performance,while such performance is obviously inhibited by the openness level and the government’s environmental regulations.Local agricultural green development performance is significant inhibited by the agricultural economic level and accelerated industrialization process in neighboring cities,while significantly promoted by the agricultural industrial structure in neighboring cities.Some suggestions for improving agricultural green development performance are proposed based on these research results,which can provide scientific references for promoting sustainable agriculture.

Introduction and Protection Suggestions of Biodiversity

Through the introduction of the concept,content,conservation significance and species value of biodiversity,the cognition of the research content,analysis methods and research directions of biodiversity was deepened.In view of the problems faced by biodiversity conservation at present,such as the accelerated disappearance of species,the unsound protection regulation system,the irrational spatial pattern of species protection,and the lack of long-term follow-up research,specific improvement suggestions were put forward,and have a positive guiding significance for future biodiversity conservation.

Analysis of Investigation on Ebian Spotted Cattle and Suggestions

Ebian spotted cattle has the ability to adapt to the local natural ecological environment,and is one of the excellent local cattle breeds in Sichuan.Ebian spotted cattle is a breed of cattle for both service and meat use and formed through long-term natural selection and artificial selection.Because of the weak development and utilization of this breed,the economic benefits of excellent breeds have not been fully brought into play,and farmers'enthusiasm in breeding is poor,so that the number of Ebian spotted cattle has reduced year by year.In order to strengthen the protection,development and utilization of Ebian spotted cattle as a local excellent breed,combined with the third national survey of livestock and poultry genetic resources in 2021,Ebian spotted cattle was investigated,and a comprehensive,scientific and objective analysis of its population numbers was conducted.Besides,reasonable suggestions were put forward.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND Hepatectomy is the first choice for treating liver cancer.However,inflammatory factors,released in response to pain stimulation,may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.AIM To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.METHODS Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed.Thirty-five patients underwent laparoscopic hepatectomy for liver cancer(liver cancer resection group)and 35 patients underwent medical image-guided microwave ablation(liver cancer ablation group).The short-term efficacy,complications,liver function,and immune function indices before and after treatment were compared between the two groups.RESULTS One month after treatment,19 patients experienced complete remission(CR),8 patients experienced partial remission(PR),6 patients experienced stable disease(SD),and 2 patients experienced disease progression(PD)in the liver cancer resection group.In the liver cancer ablation group,21 patients experienced CR,9 patients experienced PR,3 patients experienced SD,and 2 patients experienced PD.No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups(P>0.05).After treatment,total bilirubin(41.24±7.35 vs 49.18±8.64μmol/L,P<0.001),alanine aminotransferase(30.85±6.23 vs 42.32±7.56 U/L,P<0.001),CD4+(43.95±5.72 vs 35.27±5.56,P<0.001),CD8+(20.38±3.91 vs 22.75±4.62,P<0.001),and CD4+/CD8+(2.16±0.39 vs 1.55±0.32,P<0.001)were significantly different between the liver cancer ablation and liver cancer resection groups.CONCLUSION The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar,but liver function recovers quickly after microwave ablation,and microwave ablation may enhance immune function.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Some Suggestions for College Students to Improve Listening

Language is the tool of communication. English is a compulsory course for every student. English listening, as an essential part of English learning, is an effective way to obtain new information. But for many students, the most difficult part for them is listening. The paper aims to give some suggestions to the college English learners and to help them to improve their listening comprehension abilities. Such as: to strengthen language knowledge, to build up self-confidence, to use listening strategies, to learn western culture, and to listen extensively.

The Problems and Suggestions in Expanding the Vocabulary for College Students

Vocabulary plays a very important role in learning English. Having a large vocabulary is undeniable necessary, but the important thing is that proper approaches should be adopted in English learning. This paper focuses on the analyses of the students’ problems in vocabulary learning process, and gives some suggestions to our students so as to help them expand their vocabulary.

Suggestions on How to Learn English at College

Many students don't know how to learn English at college.Without a goal in life and unable to manage their time,they are likely to idle away their youth.This paper intends to offer some suggestions to students on how to learn English at college,and hopefully they will experience the satisfaction and pleasure of learning English.

Suggestions on Forest Ecological Compensation—Taking Mudanjiang City as an Example

The forest ecological compensation is an important factor to balance the interests of different areas for sustainable development and environment protection. Mudanjiang City in Heilongjiang Province of China is rich of forest resources. The forestry coverage rate reached 62.3% in 2014, after forestry conservation program from 2002. The authors explored the factors impacted on forest ecological compensation in Mudanjiang City, which was a demonstration as a case study, through experts＇ evaluation scores and AHP methodology to analyze the forest ecological compensation factors and lay the foundation for the establishment of ecological compensation mechanism. At the same time, we provided an example to explore the effective way and speed up the establishment of ecological compensation mechanism. The study found that the main factors that determined forest ecological compensation in Mudanjiang City were ecology and natural resources. Based on the analyses, some suggestions were put forward to promote the mechanism in a sustainable way.

Status and Suggestions of the Pesticide Use in the Protected Vegetable Fields in Shandong Province

This paper briefly introduced the issues of food safety and environmental pollution caused by pesticide residues in protected vegetables, discussed the status and problems of pesticide use in the protected vegetables in Shandong Province, and analyzed the main factors leading to the pesticide residues, including the low education of most farmers, lack of correct identification of diseases and insect pests, use of pesticides based on personal experience, pesticide preparation by bare hands, large dose of pesticide, frequent application, pesticide spraying without protection, uneven spraying, leakage of pesticide from the sprayers, etc.. Finally, based on the vegetable planting features and advantages in Shandong Province, some suggestions were proposed for references, such as, to enhance the monitoring of pesticide residue, to improve the educational level of farmers and to scentifically use the pesticide.

Current Plant Landscaping in Southern Gardens and Suggestions for the Optimized Configuration

Abundant lands of China such as Suzhou, Hangzhou, Sichuan and Chongqing are all located in the basin of the Yangtze River, and subtropical evergreen broad-leaved forest belt, enjoy mild climate, sufficient rainfall, rich ornamental plant resources and diversifi ed landscaping techniques. In the long-term landscaping practices, plant furnishing arts characterized by diversifi ed layer division, elegant styles, delicate and vivid images have been formed, but are still limited in combination of plant species and selection of varieties. In accordance with personal experience in plant landscaping in recent years, the author provided a few suggestions for the optimized confi guration of plant landscapes in southern regions.

Application status,problems and suggestions of artificial intelligence in medical field

介绍了新一代人工智能应用发展的背景,梳理了人工智能在医学影像、辅助诊断、健康管理、疾病预 测、药物研发领域中的应用现状,从数据、算法、主体责任、法律法规、评估标准、人才保障方面分析了人工 智能在医疗领域应用中的问题与挑战,并从行业整体发展的角度出发,对医学人工智能的应用发展提出相关建议.