SPBO-ANFIS Model of Combined Monthly Runoff Forecasting Based on Singular Spectrum Analysis by ZHANG Yajie, CUI Dongwen

“…In view of the multi-scale non-stationarity and other characteristics of monthly runoff in hydrological time series,this paper proposes a singular spectrum decomposition (SSD)-based model of combined monthly runoff forecasting that integrates the student psychology based optimization (SPBO) algorithm with the adaptive network based fuzzy inference system (ANFIS),namely the SSD-SPBO-ANFIS model.This model is then applied to the monthly runoff forecasting at a hydrological station in Yunnan Province.Specifically,time series data of sample monthly runoff are decomposed into various independent sub-series components through SSD to reduce the complexity of the time series data;then,the principle of the SPBO algorithm is outlined,and eight standard functions are selected for simulation verification and comparison of the SPBO algorithm;finally,the SPBO algorithm is employed to optimize the ANFIS condition and conclusion parameters.The SSD-SPBO-ANFIS model is built to forecast each sub-series,which is then superimposed to obtain the final monthly runoff forecasting result.In addition,the results of the proposed model are compared with those of the ensemble empirical mode decomposition (EEMD)-based EEMD-SPBO-ANFIS model and the SPBO-ANFIS model without decomposition.The following observations can be made from the results:The SPBO algorithm has favorable optimization accuracy;with a mean absolute percentage error of 5.57%,a mean absolute error of 0.20 m³/s,a Nash coefficient of 0.994 8,and a pass rate of 96.7%,the SSD-SPBO-ANFIS model has an effect better than that of the EEMD-SPBO-ANFIS model and far better than that of the SPBO-ANFIS model in forecasting sample monthly runoff.The proposed model and method can provide references for related research on hydrological time series forecasting.…”
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Border fences threaten movements of large mammals in southwestern China post-COVID-19 pandemic by Jue Lei, Lin Wang

“…During the COVID-19 pandemic, continual border fences were constructed in Yunnan Province and Guangxi Zhuang Autonomous Region (hereafter Yunnan and Guangxi), southwestern China, which may pose huge threats to wildlife movements and weren’t dismantled in the post-pandemic era. …”
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Distribution and Determinants of Peripapillary Retinal Nerve Fiber Layer Thickness and Its Association with Sleep Quality in Chinese Teenagers by Chen-Wei Pan, Yu-Xi Qian, Hua Zhong, Jun Li, Qin Chen

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Unravelling anti-cancer properties of solanaceous extracts using GC–MS and HPLC by Iqra Riaz, Yamin Bibi, Muhammad Arshad, Muhammad Sheeraz Ahmad, Manzer H. Siddiqui, Yawen Zeng, Abdul Qayyum

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Early Revelation of Leprosy in China by Sequential Antibody Analyses with LID-1 and PGL-I by Pan Qiong-Hua, Zheng Zhong-Yi, Yang Jun, Wen Yan, Yuan Lian-Chao, Li Huan-Ying, Steven G. Reed, Malcolm S. Duthie

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Effects of Highway Landscapes on Drivers’ Eye Movement Behavior and Emergency Reaction Time: A Driving Simulator Study by Fengxiang Guo, Mingyuan Li, Yao Chen, Jian Xiong, Jaeyoung Lee

“…In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and enclosed space landscapes on highways in Yunnan Province, China. Twenty-four experienced drivers participated in a 6-kilometer driving experiment in each landscape scenario. …”
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Genome-wide identification and expression analysis of the WRKY gene family in Mikania micrantha by Zihan Zhang, Mei Ji, Sangzi Ze, Wenzheng Song, Bin Yang, Ning Zhao

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Design Flood Calculation Model for Extra-Small Watersheds in Ungauged Basin by Yun Wang, Zengchuan Dong, Xinhua Zhu, Wenzhuo Wang, Yupeng Liu, Ronghao Chen, Yunjia He

“…Taking the “Stormwater Runoff Chart” as the data source, the runoff generation, and concentration model was established with the design flood of Baludi Reservoir in the Gelangram River basin of Menglian, Yunnan Province as the research object. A comparative study of the results of the design floods calculated by different methods was carried out. …”
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SSA-ELM Hydrological Time Series Forecast Model Based on Wavelet Packet Decomposition and Phase Space Reconstruction by LI Lude, CUI Dongwen

“…Considering the nonlinear and multi-scale characteristics of hydrological time series,this paper proposes a squirrel search algorithm (SSA)-extreme learning machine (ELM) forecasting model based on wavelet packet decomposition (WPD) and phase space reconstruction.It is then applied to the Shangguo Hydrological Station in Yunnan Province for monthly runoff and precipitation forecasting.Specifically,WPD is performed to decompose the runoff and precipitation time series data,and the Cao method is applied to reconstruct the phase space of each subseries component.Then,the principle of SSA is outlined,and objective functions are constructed through the training samples of each component.The objective functions are optimized by SSA,and the results are compared with the optimization results of the whale optimization algorithm (WOA),the gray wolf optimization (GWO) algorithm,and the particle swarm optimization (PSO) algorithm.Finally,the weight of the ELM input layer and the hidden layer bias obtained by optimization based on SSA,WOA,GWO algorithm,and PSO algorithm,respectively,are utilized to build SSA-ELM,WOA-ELM,GWO-ELM,and PSO-ELM models,which,in addition to the unoptimized ELM models,are applied to forecast each subseries component,and the forecast results are summed and reconstructed to obtain the final forecasting results.The results show that SSA outperforms WOA,GWO algorithm,and PSO algorithm in optimizing the objective functions of each component and that it offers better optimization accuracy.The mean relative error,mean absolute error,mean square root error,and forecast pass rate of the proposed SSA-ELM model for monthly runoff and monthly precipitation forecast are 5.32% and 3.84%,0.078 m³/s and 0.169 mm,0.103 m³/s and 0.209 mm,97.5% and 95.8%,respectively,indicating that its forecasting accuracy is higher than that of other models such as the WOA-ELM model.…”
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Study on hierarchical regulation of crop irrigation threshold under severe drought conditions by Ruidan Gu, Huaxiang He, He Chen, Jiake Tian

“…This study employed the WOFOST model to simulate rice yield in Chuxiong Yi Autonomous Prefecture, Yunnan Province, under various irrigation scenarios, focusing on severe drought conditions. …”
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Case Report: Rare cardiovascular characteristics of tuberous sclerosis complex with novel TSC2 variant by Zhiqin Du, Xiao Ma, Xiao Ma, Xiao Ma, Jianhua Li, Fang Yang, Yangfan Guo, Yangfan Guo

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Research on a 44 Day Quasi-periodic Oscillation of Optical Bands for BL Lac S5 0716+714 by Lin Lu, Bin Sun, Zhen-Xing Fang, Meng Wan, Yunlu Gong

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Prevalence of enteroviruses in healthy populations and excretion of pathogens in patients with hand, foot, and mouth disease in a highly endemic area of southwest China. by Qiang Wu, Xiaoqing Fu, Lili Jiang, Rusong Yang, Jianping Cun, Xiaofang Zhou, Yongming Zhou, Yibing Xiang, Wenpeng Gu, Jianhua Fan, Hong Li, Wen Xu

“…A cross-sectional study and a follow-up study were conducted in three HFMD endemic counties of Yunnan Province. Six hundred sixty-seven healthy subjects were recruited to participate in the cross-sectional study, and two stool specimens were collected from each subject. …”
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Dynamic changes in physicochemical property, biogenic amines content and microbial diversity during the fermentation of Sanchuan ham by Zhongai Chen, Hong Chen, Huan Du, Cong Chen, Kaixiang Lu, Qiaoli Xue, Yongjin Hu

“…Sanchuan ham is appreciated in Yunnan Province, China, for its characteristic flavor and taste, while the microbial community structure and biogenic amines content remain unclear during fermentation processes. …”
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Comprehensive Characterization of the Odor-Active Compounds in Different Processed Varieties of Yunnan White Tea (<i>Camellia sinensis</i>) by GC×GC-O-MS and Chemometrics by Junaid Raza, Baosong Wang, Yue Duan, Huanlu Song, Ali Raza, Dongfeng Wang

“…This study investigates the aroma characterization of unique white tea varieties from the Lüchun county of Yunnan province, Mainland China. These include shaken, unshaken, steam-cooked, and compressed varieties. …”
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A Complex Evaluation and Optimization Approach for Oxygen-Enriched Combustion Characteristics of Blended Fuels Based on Response Surface Methodology by Y. C. Liu, H. Zhang, Z. M. Luo, S. Qing, A. M. Zhang, S. P. Yang

“…With the energy consumption increasing, the coal supply in China has been becoming tight, which has made it difficult for thermal power generation in Yunnan Province. Making full use of local inferior coal and biomass resources in Yunnan can remedy the lack of fuel in power plants. …”
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Portrait of WWP1: the current state in human cancer by Jiaming Lei, Jun Chen, Wenwen Yu, Qing Wu, Shuang Jing, Yuanguang Tang, Li Lin, Meichun Hu

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The curious case of C-88: impacts of a potato variety on farmers in Yunnan, China by Stephanie Myrick, Willy Pradel, Canhui Li, Victor Suarez, Guy Hareau, Catherine Larochelle, George W. Norton, Jeffrey Alwang

“…This paper analyzes how a particular potato variety generates benefits to producers and discusses how these benefits may have contributed to poverty reduction in Yunnan province. Cooperation 88 (C88) is a high-yielding, late blight-resistant variety that was developed through a partnership between the International Potato Center and Yunnan Normal University in Kunming, China. …”
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Land use transition and its driving mechanisms in China’s human-elephant conflict areas by WANG Yuan, WANG Yahui, YANG Aoxi, FAN Hui, XIE Fei

“…[Methods] This study focuses on the human-elephant conflict areas in Pu’er City and Xishuangbanna Prefecture in Yunnan Province as the study areas, based on four periods of remote sensing images in 1990, 2000, 2010, and 2020, as well as two periods of household questionnaire survey data in 2020 and 2022, utilizing spatial analysis and input-output analysis methods to reveal the characteristics and driving mechanisms of land use transition in human-elephant conflict zones. …”
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Process optimization and evaluation of quality properties of natto with co-culture of Bacillus subtilis natto and Limosilactobacillus fermentum by Yi-Ni Ma, Xun Xu, Lai-Feng Chen, Jin-Ping Zhou, Zhen-Hui Cao, Qiu-Ye Lin

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