Analysis of Risk Factors and the Establishment of a Predictive Model for Thrombosis in Patients with Immune Thrombocytopenia by Hui Liang MD, Lingxue Duan BSc, Manyu Long BSc, Songyuan Tie BSc, Changyan Sun BSc, Sha Ma PhD, Jing Wang PhD, Shuya Wang BSc

“…Methods We retrospectively analyzed 350 ITP patients who had been hospitalized in the First People's Hospital of Yunnan Province between January 2024 and June 2024. For all patients, we recorded demographic characteristics and clinical data, analyzed the risk factors for thrombosis in ITP patients and then developed a risk prediction model. …”
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Effect of Waterproofing Treatment on Mechanical Properties of Bamboo by Lei Yang, Dongming Li, Lixiang Yu

“…The main conclusions include: the mechanical properties of bamboo produced in Yunnan Province were the highest, followed by Jiangxi Province, and the lowest in Zhejiang Province. …”
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Pathway and molecular mechanisms for malachite green biodegradation in Exiguobacterium sp. MG2. by Ji'ai Wang, Feng Gao, Zhongzhong Liu, Min Qiao, Xuemei Niu, Ke-Qin Zhang, Xiaowei Huang

“…MG2 was isolated from a river in Yunnan Province of China as one of the best malachite green degraders. …”
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Dissolved Oxygen Prediction Based on SOA-SVM and SOA-BP Models by ZHANG Xuekun

“…To improve the accuracy of dissolved oxygen prediction,this paper researches and proposes a prediction method that combines seagull optimization algorithm (SOA) with support vector machine (SVM) and BP neural network,prepares four prediction schemes based on the monthly dissolved oxygen monitoring data of the Jinghong Power Station in Xishuangbanna,a national important water supply source in Yunnan Province,from January 2009 to September 2020,optimizes the key parameters of SVM and weight threshold of BP neural network by SOA to construct SOA-SVM and SOA-BP models,predicts the dissolved oxygen of Jinghong Power Station based on the models,and compares the prediction results with those of SVM and BP models.The results show that:The absolute values of the average relative errors of the SOA-SVM and SOA-BP models for the 4 schemes of dissolved oxygen prediction are between 4.07%~4.98% and 3.85%~4.83%,and that of the average absolute errors are 0.309~0.374 mg/L and 0.294~0.371 mg/L,respectively.With better prediction accuracy than SVM and BP models,they have good prediction accuracy and generalization ability.SOA can effectively optimize the key parameters of SVM and weight threshold of BP neural network.SOA-SVM and SOA-BP models are feasible for dissolved oxygen prediction,which can provide references for related prediction research.…”
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New Aquilariomyces and Mangifericomes species (Pleosporales, Ascomycota) from Aquilaria spp. in China by Tian-Ye Du, Samantha C. Karunarathna, Kevin D. Hyde, Somrudee Nilthong, Ausana Mapook, Dong-Qin Dai, Kunhiraman C. Rajeshkumar, Abdallah M. Elgorban, Li-Su Han, Hao-Han Wang, Saowaluck Tibpromma

“…Our rigorous process led to the collection of two new terrestrial saprobic fungi from the Guangdong and Yunnan provinces in China. After extensive phylogenetic analyses and detailed comparison of morphological characteristics, the two collections were identified as two new species belonging to Pleosporales, Ascomycota. …”
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An Optimal Composition Strategy for Knowledge Service Component Based on Flexible Tracking Particle Swarm Algorithm by Yan-chao Yin, Fu-zhao Chen, Wei-zhi Liao, Cui-yin Liu

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Neoantigen immunotherapy: a novel treatment for bladder cancer by Ruiyang Lv, Zhenzhu Liu, Maoxin Lv, Yuze Song, Junlin Wang, Huizhi Mu, Yu Zhang, Xuejian Wang

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Calculation and Analysis of Effective Utilization Coefficient of Farmland Irrigation Waterin Mountainous Area of Yunnan Plateau by CAO Yan, LI Youliang, WANG Shupeng, ZHANG Lei, ZHOU Xiaoyan, WANG Jie, HAN Ling

“…To explore the calculation method of effective utilization coefficient of farmland irrigation water in the mountainous area of Yunnan Plateau,we scientifically evaluated the efficiency of farmland irrigation water.Taking Yongshan County of Zhaotong City,Yunnan Province as the research area,we selected 2 sample irrigation districts and15 typical fields to calculated and analyze the effective utilization coefficient of farmland irrigation water.The net irrigation water of sample irrigation districts was calculated after measurement and investigation.Head-end measurement was used to calculate and analyze the effective utilization coefficient of farmland irrigation water in the sample irrigation districts,and then the coefficient of Yongshan County from 2018 to 2020 was calculated.The results show the followings.① The net irrigation water in the sample irrigation districts of Yongshan County exhibited a downward trend from 2018 to 2020 overall.The net irrigation water for Zanthoxylum bungeanum,Amomum villosum,Citrus reticulata,flue-cured tobacco and Solanum tuberosum in Yongshan County decreased successively.② The effective utilization coefficient of farmland irrigation water was larger in the medium-sized irrigation district than in the small irrigation district.③ The effective utilization coefficient of farmland irrigation water in Yongshan County was increased gradually from 2018 to 2020,i.e.,0.489 2,0.502 0 and 0.508 5,respectively.The research results are reasonable and reliable,which can reflect the actual situation in the irrigation districts.…”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

“…To improve the accuracy of runoff forecast,and establish a runoff forecast model combining principal component analysis (PCA),future search algorithm (FSA),and multiple linear regression (MLR),this paper reduces the dimensionality of the sample data by PCA,selects 8 standard test functions and simulates and verifies FSA under different dimensional conditions,optimizes MLR constant terms and partial regression coefficients by FSA,proposes a PCA-FSA-MLR runoff forecast model,constructs PCA-LS-MLR,PCA-FSA-SVM,and PCA-SVM models with dimensionality reduction processing by PCA and FSA-MLR,LS-MLR,FSA-SVM,and SVM without dimensionality reduction processing as a comparison model,and verifies each model through forecasting the annual runoff and monthly runoff in December of Longtan station in Yunnan Province.The results show that:①FSA has better optimization accuracy and global extremum search ability under different dimensional conditions;②The average absolute relative error of the annual runoff and monthly runoff in December of Longtan station through PCA-FSA-MLR model are 1.63% and 3.91% respectively,and its forecast accuracy is better than the other 7 models,with higher forecast accuracy and stronger generalization ability;③For the same model,the forecast accuracy after dimensionality reduction processing by PCA is better than that without dimensionality reduction processing,so the data dimensionality reduction by PCA is helpful to improve the forecast accuracy of models.…”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring by R. Chang, B. Zhang, Y. Zhang, S. Gao, S. Zhao, Y. Rao, X. Zhai, T. Wang, Y. Yang

“…However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address this issue, we propose a method that utilizes infrared and visible images to make the images more informative, thereby improving the accuracy of the detection algorithm for electric power construction site safety. …”
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Analysis of the Deformation Characteristics and Formation Mechanism of Reservoir Landslides under Rainfall Conditions by DOU Sijun, XU Xingqian, LI Jiguo, LING Huikun, ZHOU Shasha

“…Hexi reservoir landslide is located in Tianyuan Town,Changning County,Baoshan City,Yunnan Province.There are obvious tensile cracks,fault,bank slope retaining wall and cut-off ditch crack deformation at its rear edge,which bring a serious threat to the safety of spillways and dam body.Based on the basic morphological features and geological environment conditions of the landslide,combined with the geological survey data,this paper analyzes the deformation characteristics and induced factors of the landslide and judges the landslide as a large-scale thick-layer traction-pushing bedding landslide,simulates the change of slope pore water pressure and safety coefficient under the conditions of different rainfall intensity and reservoir water level change by Geo-Studio numerical software,and analyzes & evaluates its stability.The results show that:Under continuous rainfall,the reservoir water level rises,the front edge of the slope body softens,the pressure of pore water in the slope body increases,the natural discharge effect produces,and the sliding force increases,which aggravate the deformation,failure and destabilization of slope body.In the process of the sudden drop of reservoir water level,the backpressure effect of the reservoir water is weakened,and the water penetration of the pre-slope body produces the dynamic water pressure,which is more likely to lead to the occurrence of such landslides.The steep slope terrain and bedding structure of downward rock formation in the landslide area create favorable conditions for the development of the landslide,and thecontinuous rainfall is the main factor of the landslide.The joint cutting slope and backpressure anti-slip pile is an effective means to prevent and control the landslide.The results can serve as theoretical reference for the analysis,judgment and timely prevention of similar landslides in daily operation and management of reservoir.…”
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Establishment of the Luoping Biota National Geopark in Yunnan, China by Michael Benton, Shixue Hu, Qiyue Zhang, Tao Xie, Changyong Zhou, Wen Wen, Jinyuan Huang

“…We track the progress of one of the geoparks, Luoping Biota National Geopark in Yunnan Province, from initial plans after its discovery as a key site for the exceptional preservation of Middle Triassic marine fossils in 2007, to acceptance as a National Geopark in 2011. …”
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Study on the Oxidative Leaching of Uranium from the Lignite in the CO32-/HCO3- System by Y. Ning, Y. Li, Y. Zhang, P. Tang

“…Na2CO3/NaHCO3 mixtures with different oxidants were used to leach uranium in the lignite which was obtained from Lincang, Yunnan province. The experimental results showed that the optimal solid/liquid ratio and CO32-/HCO3- ratio for uranium leaching were 1 : 20 (g/mL) and 2 : 1, respectively. …”
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Coexistence and Competition between Tomicus yunnanensis and T. minor (Coleoptera: Scolytinae) in Yunnan Pine by Rong Chun Lu, Hong Bin Wang, Zhen Zhang, John A. Byers, You Ju Jin, Hai Feng Wen, Wen Jian Shi

“…Competition and cooperation between bark beetles, Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor (Hartig) (Coleoptera: Scolytinae) were examined when they coexisted together in living Yunnan pine trees (Pinus yunnanensis Franchet) in Yunnan province in Southwest China. T. yunnanensis bark beetles were observed to initiate dispersal from pine shoots to trunks in November, while the majority of T. minor begins to transfer in December. …”
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Data on insect biodiversity in a Chinese potato agroecosystem from DNA metabarcoding by Changjin Lin, Chenxi Liu, Lilin Chen, Hongmei Cheng, Muhammad Ashfaq, Paul D. N. Hebert, Yulin Gao

“…This study aimed to explore insect diversity in potato fields in Yunnan Province. From autumn 2021 to summer 2022, five Malaise traps were strategically deployed to capture insect samples. …”
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Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM by Dingchao Zhang, Xin Xiong, Chongyang Shao, Yao Zeng, Jun Ma

“…To validate the superiority of the proposed method, actual hourly power consumption data from a SAG mill in the beneficiation plant in Yunnan Province is utilized, and experiments are conducted comparing it with models such as GRU, ARIMA, SVM, LSTM, TCN, CNN-GRU, and CNN-LSTM. …”
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Immunotherapy of osteosarcoma based on immune microenvironment modulation by Heping Lian, Heping Lian, Jiakui Zhang, Shuna Hou, Shuang Ma, Jiachen Yu, Wei Zhao, Duoyi Zhao, Zhiyu Zhang

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Hydrochemical Characteristics and Formation of the Madeng Hot Spring in Yunnan, China by Ren Zhenhua, Zhou Xun, Yang Miaolin, Wang Xiaocui, Zheng Yuhui, Li Xiaolu, Shen Ye

“…The Madeng hot spring emerges in the central river valley in the northeastern Lanping Basin in Jianchuan county of Yunnan Province in China. Quaternary sand and gravel occur in the valley which is underlain by the red beds consisting of sandstone and mudstone. …”
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Monthly Runoff Prediction with RVM and SVM Optimized by Singular Spectrum Analysis and Gradient-Based Optimization Algorithm by LIANG Xiaoxin, CUI Dongwen

“…Finally,the monthly runoff forecast for 65 years (780 months in total) at Longtan Station in Yunnan Province is discussed as an example.The first 53 years are selected as the training samples,and the next 10 years (120 months in total) are taken as the forecast samples to verify the SSA-GBO-RVM and SSA-GBO-SVM models.The results show that the GBO algorithm,with high optimization accuracy and great global search ability,is better than the marine predators algorithm (MPA) and the particle swarm optimization (PSO) algorithm in the optimization effect under different dimensional conditions.The SSA-GBO-RVM and SSA-GBO-SVM models have an average absolute percentage error of 6.20% and 7.82%,respectively,in the 120-month monthly runoff prediction for the example,respectively.The average absolute errors of the two models are 0.88 m³/s and 1.00 m³/s respectively,and the Nash coefficients are 0.992 6 and 0.991 3 respectively.This means the two models both have high prediction accuracy and reliability.Comparatively speaking,the SSA-GBO-RVM model is better than the SSA-GBO-SVM model.…”
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