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Lei K, Li Y, Zhang Y, Wang S, Yu E, Li F, Xiao F, Xia F. Development of a new method framework to estimate the nonlinear and interaction relationship between environmental factors and soil heavy metals. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167133. [PMID: 37730041 DOI: 10.1016/j.scitotenv.2023.167133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/12/2023] [Accepted: 09/14/2023] [Indexed: 09/22/2023]
Abstract
The intricate and multifaceted nature of soil system profoundly influences the highly complex and often nonlinear changes that soil heavy metals (HM) undergo. Spatial heterogeneity, location and scale variability, and the interaction and superposition among environmental drivers challenged researchers to determine the sophisticated nature of soil HMs changes at the regional scale. This study aims to develop a new method framework and selects Ningbo as the case study to apportion the environmental factors responsible for soil HMs pollution that include Cd, Cr, Pb, Hg, As, Cu, Zn and Ni, focusing on nonlinearity and interaction. We harnessed the Random Forest model to apportion the environmental drivers of soil HM change. The directionality and shape of the nonlinear relationship between HMs and their individual contributors were derived by Partial Dependence Plots. The interactions of multiple drivers were quantitatively assessed by the Conditional Inference Tree. Our results demonstrated that soil HMs in the study area varied spatially. Soil HMs pollution was mitigated by natural factors and anthropogenic factors. The main influencing factors were pH, soil parent material type, enterprise activities, and agricultural application. The effects of some factors on soil HMs showed a monotonic linear trend, but some have apparent threshold effects. The direction of influence on soil HMs will shift when pH and phosphate fertilizer reach a specific value. The addition of enterprises in the area would rarely have an impact on the HMs pollution once it reached around 2 per km2 because of the industrial agglomeration. Soil HM concentrations were mainly from multi-pollutants and were governed by a combination of environmental factors. Our study provided managers and policymakers with site-specific and definite guidelines for preventing and controlling soil HM pollution.
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Affiliation(s)
- Kaige Lei
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Yan Li
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China.
| | - Yanbin Zhang
- Zhejiang Land Consolidation and Rehabilitation Center, Hangzhou 310007, China
| | - Shiyi Wang
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Er Yu
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Feng Li
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fen Xiao
- Institute of Land Science and Property, School of Public Affairs, Zhejiang University, Hangzhou 310058, China
| | - Fang Xia
- College of Economics and Management, Zhejiang A&F University, Hangzhou 311302, China
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Chen L, Zhou S, Tang C, Luo G, Wang Z, Lin S, Zhong J, Li Z, Wang Y. A novel methodological framework for risk zonation and source-sink response concerning heavy-metal contamination in agroecosystems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 868:161610. [PMID: 36646223 DOI: 10.1016/j.scitotenv.2023.161610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 12/15/2022] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
The accurate identification of high-risk zones and risk source-sink responses for heavy-metal (HM) contamination of agroecosystems remains challenging due to involving multiple environmental media such as soils, dustfall, and crops and a wide range of evaluation criteria and constraints. This study established a novel evaluation model based on the integration of Geographical Information Systems (GIS) and multi-criteria decision analysis (MCDA) to assess agroecosystem risk in the Lihe River watershed, China. Bivariate local indicators of spatial association (LISA) were adopted to explore the spatial interaction of risk sources and sinks with outputs ranging from 0.0003 (no risk) to 0.83 (high risk). Areas with moderate, considerable, and high risk constituted 67.4 % of the total land area, and only 1.8 % of the area was classed as low risk. Central urban and eastern areas around Taihu Lake were risk accumulation regions that needed more remedial attention. Risk cluster zones in the central urban area involved significant source-sink response relationships with the spatial distribution of industries, whereas eastern zones were linked to vehicular traffic distribution, accounting for 27.5 % and 16.5 % of the total area, respectively. This study provides a new methodological framework for the assessment of environmental risk, risk zonation, and risk source-sink spatial interaction in agroecosystems.
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Affiliation(s)
- Lian Chen
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China
| | - Shenglu Zhou
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing 210023, Jiangsu, China.
| | - Cuiming Tang
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China; Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510610, Guangdong, China.
| | - Guoqing Luo
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China; Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510610, Guangdong, China
| | - Zhenjiang Wang
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China; Key Laboratory of Urban Agriculture in South China, Ministry of Agriculture and Rural Affairs, Guangzhou 510610, Guangdong, China
| | - Sen Lin
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China
| | - Jianwu Zhong
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China
| | - Zhiyi Li
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China
| | - Yuan Wang
- Sericultural & Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510610, Guangdong, China
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Muhammad Y, Almoteri M, Mujlid H, Alharbi A, Alqurashi F, Dutta AK, Almotairi S, Almohamedh H. An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3372296. [PMID: 36187499 PMCID: PMC9519282 DOI: 10.1155/2022/3372296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/19/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022]
Abstract
Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient's heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.
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Affiliation(s)
- Yar Muhammad
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Moteeb Almoteri
- Department of Management Information Systems, Business Administration College King Saud University, Riyadh 11451, Saudi Arabia
| | - Hana Mujlid
- Department of Computer Engineering, Faculty of Computer Engineering, Taif University, Taif, Saudi Arabia
| | - Abdulrhman Alharbi
- Computer Sciences and Information Department, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia
| | - Fahad Alqurashi
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, Al Maarefa University, Riyadh 13713, Saudi Arabia
| | - Sultan Almotairi
- Department of Natural and Applied Sciences, Faculty of Community College, Majmaah University, Majmaah, 11952, Saudi Arabia
- Department of Information Systems, Faculty of Computer and Information Sciences, Islamic University of Madinah, 42351, Saudi Arabia
| | - Hamad Almohamedh
- Faculty of King Abdulaziz City for Science and Technology (KACST) Riyadh, Riyadh, Saudi Arabia
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Nourouzi Z, Chamani A. Characterization of ambient carbon monoxide and PM 2.5 effects on fetus development, liver enzymes and TSH in Isfahan City, central Iran. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118238. [PMID: 34600063 DOI: 10.1016/j.envpol.2021.118238] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 09/21/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
Ambient carbon monoxide (CO) and particulate matters (PMs) are two important air pollutants in urban areas with known impacts on fetuses. Hence, this study measured some biochemistry factors of 200 neonates with birth dates from January 19 to October 12, 2020, including the birth weight and height and the serum levels of ALT, AST, ALP, GGT, and TSH. The Support Vector Machine-fitted land-use regression approach was used to predict the spatio-temporal variability of intra-urban PM 2.5 and CO concentrations by month during the pregnancy period of the cases employing 5 variables of Digital Elevation Model (DEM), slope, and distance from Compressed Natural Gas (CNG) stations, Bus Rapid Transit (BRT) stations, and mines and industries. Spearman correlation analysis (p < 0.05) was performed between the neonate indices and mean monthly PM 2.5 and CO concentrations at the exact residential address of maternal cases and their nearby areas in 250, 500, 1000, 1500, and 2000 m-radius buffer rings. All modeling efforts succeeded in predicting CO and PM 2.5 levels with acceptable adjusted r2 values. Northern Isfahan had relatively higher CO and PM 2.5 concentrations due to its adjacency to low-vegetated open lands and its high traffic load as compared to southern areas. The correlation results between the neonate biochemistry indices and mean PM 2.5 and CO concentrations were mostly positive in most buffer rings, especially in the >500 m-radius buffer rings for PM 2.5 and in the 2000 m-radius rings for CO. Although the correlation results of PM 2.5 followed a detectable trend in the buffer rings, the associations between CO and the neonate biochemistry indices differed significantly between the buffer rings. Results showed that increasing mean monthly concentration of CO and PM 2.5 may stimulate further production of liver enzymes while decreasing the birth weight and height.
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Affiliation(s)
- Zohreh Nourouzi
- Environmental Science Department, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
| | - Atefeh Chamani
- Environmental Science Department, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
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An Adaptive Inverse-Distance Weighting Interpolation Method Considering Spatial Differentiation in 3D Geological Modeling. GEOSCIENCES 2021. [DOI: 10.3390/geosciences11020051] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.
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Naresh A, Venkata Krishna P. An efficient approach for sentiment analysis using machine learning algorithm. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00429-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Statistical modelling and parametric optimization in document fragmentation. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04068-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Efficiency of optimization algorithms on the adjustment of process parameters for geometric accuracy enhancement of denture plate in single point incremental sheet forming. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04354-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Prasanna Kumar KR, Kousalya K. Amelioration of task scheduling in cloud computing using crow search algorithm. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04067-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Dhivyaa CR, Vijayakumar M. An Effective Detection Mechanism for Localizing Macular Region and Grading Maculopathy. J Med Syst 2019; 43:53. [PMID: 30693385 DOI: 10.1007/s10916-019-1163-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/08/2019] [Indexed: 11/24/2022]
Abstract
The eye disease is prominent in many nations including India and is said to affect up to 80% patients having diabetes. Diabetic Retinopathy is the medical term for denoting the damages to retina caused due to diabetes mellitus. Implying K means Clustering algorithm for coarse segmentation, hard distils are identified with better accuracy than the classical approaches. The variance based methods for segmenting hard distils are reviewed in the surveys and had to be improved. To remove the background features from the picture and conserve computational costs, a mathematical morphological method is used to reconstruct the image features for better segmentation. The results obtained for 96.4% sensitivity and 97.2% specificity. Along with this advantage, a graphical user interface is developed which will simplify the usage of this system. This model will divide the fragments into regions of interests having lesions and normal regions carrying normal features. After this segmentation, ophthalmologists will utilize the results to grade diabetic retinopathy and devise a treatment plan.
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Affiliation(s)
- C R Dhivyaa
- Computer Science and Engineering, Nandha College of Technology, Erode, India.
| | - M Vijayakumar
- Computer Science and Engineering, Nandha College of Technology, Erode, India
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Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals. J Med Syst 2018; 42:228. [DOI: 10.1007/s10916-018-1093-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 10/02/2018] [Indexed: 10/28/2022]
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Selvaraj S, Sivaraman S. Prediction model for optimized self-compacting concrete with fly ash using response surface method based on fuzzy classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3575-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Nagarajan SM, Gandhi UD. Classifying streaming of Twitter data based on sentiment analysis using hybridization. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3476-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Lokesh S, Malarvizhi Kumar P, Ramya Devi M, Parthasarathy P, Gokulnath C. An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3466-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Abdel-Basset M, Gunasekaran M, Mohamed M, Smarandache F. A novel method for solving the fully neutrosophic linear programming problems. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3404-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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