作者机构：
School of Economics, Jinan University, Guangzhou, China;Bangor College, Central South University of Forestry & Technology, Changsha, China;Department of Food, Agricultural And Resource Economics, University of Guelph, Guelph, Canada;School of Agricultural And Resource Economics, The University of Western Australia, Perth, Australia;Department of Economics And Finance, Gordon S. Lang School of Business and Economics, University Of Guelph, Guelph, Canada
通讯机构：
[Zhige Wu] S;School of Economics, Jinan University, Guangzhou, China<&wdkj&>Bangor College, Central South University of Forestry & Technology, Changsha, China
期刊：
Expert Systems with Applications,2022年205:117637 ISSN：0957-4174
通讯作者：
Tang, Cheng(d2072006@ems.u-toyama.ac.jp)
作者机构：
[Tang, Yajiao; Zhu, Yulin] Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Peoples R China.;[Song, Zhenyu] Taizhou Univ, Coll Informat Engn, Taizhou 225300, Peoples R China.;[Hou, Maozhang] Cent South Univ Forestry & Technol, Bangor Coll, Changsha 410004, Peoples R China.;[Tang, Yajiao; Zhu, Yulin; Hou, Maozhang] Res Ctr High Qual Dev Ind Econ, Changsha 410004, Peoples R China.;[Tang, Cheng] Univ Toyama, Fac Engn, Toyama 9308555, Japan.
通讯机构：
[Cheng Tang] F;[Junkai Ji] C;Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan<&wdkj&>College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
关键词：
Dendritic neural network;Differential evolution;Financial time series;Prediction;Scale-free networks
通讯机构：
[Lin, Y ] C;Cent South Univ Forestry & Technol, Bangor Coll, Changsha 410004, Peoples R China.
关键词：
Heavy metal;East Dongting Wetland;Ecological risks;Potential ecological risk index;Land use
摘要：
The properties of heavy metals in soil hold key to effectively protecting the urban ecosystem and predicting the environmental risks. In this study, we selected the wetland of East Dongting Lake as the study area, and measured the contents of their physicochemical properties. We adopted the methods of multivariate statistical analysis and inverse distance weighted (IDW) interpolation, so as to reveal the sources and distribution characteristics of heavy metal content in soil in the study area. By adopting the potential ecological risk index (PERI) method, we assessed the PERI values of heavy metals. The research findings indicate that: (1) As shown by the analysis of correlation matrix, the elements of As, Hg and Zn are positively correlated with SOC (P < 0.01). (2) As shown by the rotation factor load matrix, Factor 1 (F1) accounts for 18.03% of the total variance, which includes As, Fe, Mn, Zn and soil organic carbon. Factor 2 (F2) mainly includes nitrate ammonia, Pb and soil particles, and the factor load all exceeds 0.50. (3) There is a significantly higher level of contents of the elements of Cd, Zn, Pb and Hg in the agricultural land than other types of land use (P < 0.05). (4) Judging from the potential ecological risk coefficient of single heavy metal Eir, Cd features the highest risk coefficient with the mean value of 292, whereas the sampling points that have their E.56ir & GE; 320 account for 36.51% of the total, indicating strong ecological risks. The average value of the comprehensive PERI in heavy metals amounts to 555.03, representing a strong degree of ecological risks.
作者机构：
[张守首] Bangor College, Central South University of Forestry and Technology, Changsha;410018, China;[郭思源] State Grid Hunan Electric Power Company Limited Research Institute, Changsha;410007, China;[张守首] 410018, China
作者机构：
[Bai, Maohui] Cent South Univ, Powder Met Res Inst, State Key Lab Powder Met, Changsha 410083, Peoples R China.;[Liang, Yuhao; Bai, Maohui; Liang, YH; Hong, Bo; Lai, Yanqing] Cent South Univ, Sch Met & Environm, Changsha 410083, Peoples R China.;[Hu, Lina] Cent South Univ Forestry & Technol, Bangor Coll, Changsha 410083, Peoples R China.
通讯机构：
[Bai, Maohui; Bai, MH; Liang, YH; Hong, B] C;Cent South Univ, Powder Met Res Inst, State Key Lab Powder Met, Changsha 410083, Peoples R China.;Cent South Univ, Sch Met & Environm, Changsha 410083, Peoples R China.
关键词：
Lithium ion battery;Magnesium borate oxide;Li-rich layered oxide;Surface
摘要：
Due to manganese mining and slag accumulation, the geological structure of the wetland polluted by heavy metals in Xiangtan Manganese Mine area was seriously damaged, hence biodiversity loss, severe soil, and water pollution, as well as serious heavy metal pollution to food, vegetables, and other natural sources. In order to restore the ecological environment of the mining area, in 2015, the ecological restoration test of heavy metal polluted wetlands in the mining area was carried out. The results showed that the Mn content of different parts of Koelreuteria paniculata root from high to low order: fine root > small root > medium root > large root. The Mn content of different parts of Elaeocarpus decipiens root from high to low order: large root > medium root > small root > fine root. The order of Mn content in plants of the wetland restoration from high to low is as follows: Canna warscewiezii > Thalia dealbata > Boehmeria > Pontederia cordata > Typha orientalis > Nerium oleander > Softstem bulrush > Iris germanica > Acorus calamus > Arundo donax > Phragmites australis; The order of Internal Cu content from high to low is as follows: Acorus calamus > Thalia dealbata > Softstem bulrush > Canna warscewiezii > Typha orientalis > Arundo donax > Boehmeria > Iris germanica > Pontederia cordata > Nerium oleander > Phragmites australis; Zn content from high to low order is as follows: Canna warscewiezii > Acorus calamus > Thalia dealbata > Typha orientalis > Pontederia cordata > Arundo donax > Softstem bulrush > Iris germanica > Boehmeria > Phragmites australis > Nerium oleander; Cd content from high to low order is as follows: Phragmites australis > Softstem bulrush > Thalia dealbata > Nerium oleander > Boehmeria > Canna warscewiezii > Acorus calamus > Iris germanica > Typha orientalis > Pontederia cordata > Arundo donax. The results of this study have provided a theoretical basis and decision-making reference for the evaluation of heavy metals polluted wetland restoration, protection, and reconstruction effects and the selection of ecological restoration modes.</p>
作者：
Mittal, Mohit;Iwendi, Celestine*;Khan, Suleman;Rehman Javed, Abdul
期刊：
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES,2021年32(6):e3997- ISSN：2161-3915
通讯作者：
Iwendi, Celestine
作者机构：
[Mittal, Mohit] Kyoto Sangyo Univ, Dept Informat Sci & Engn, Kyoto, Japan.;[Iwendi, Celestine] Cent South Univ Forestry & Technol, Bangor Coll, Changsha, Peoples R China.;[Rehman Javed, Abdul; Khan, Suleman] Air Univ, Dept Comp Sci, Islamabad, Pakistan.
通讯机构：
[Iwendi, Celestine] C;Cent South Univ Forestry & Technol, Bangor Coll, Changsha, Peoples R China.
摘要：
Wireless sensor network (WSN) is a collection of a huge number of autonomous sensor nodes having capabilities such as sensing, processing, and manipulation. In any WSN, routing protocols are the backbone for performing all type tasks such as sensing, controlling, and transmission of packets in ubiquitous environment. In this article, a LEACH protocol with Levenberg-Marquardt neural network (LEACH-LMNN) is considered to analyze the overall network lifetime. The aim of LEACH-LMNN protocol comprises two parts: selection of cluster head node using LMNN approach and the second part is to locate the shortest path from the cluster-head node to base-station node adopting various route discovery algorithms, that is, breadth-first search, Bellman-Ford, and Dijkstra. The simulation result shows that the LEACH-LMNN protocol with the Dijkstra shortest path algorithm outperforms other route discovery algorithms. In addition to this, this work also analyzes normal and anomaly detection based on intrusion detection system in wireless sensor networks using gated mechanism, that is, long short-term memory (LSTM) and gated recurrent unit (GRU) in deep learning models. The proposed model achieves the highest detection rate of 97.84% for GRU and 97.85% for LSTM as well as improves the false positive rate (FPR) of 5.87% and 3.88% FPR for GRU and LSTM, respectively.
