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Shukla AK, Behera SK, Basumatary A, Sarangthem I, Mishra R, Dutta S, Sikaniya Y, Sikarwar A, Shukla V, Datta SP. PCA and fuzzy clustering-based delineation of soil nutrient (S, B, Zn, Mn, Fe, and Cu) management zones of sub-tropical Northeastern India for precision nutrient management. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 365:121511. [PMID: 38909579 DOI: 10.1016/j.jenvman.2024.121511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/25/2024] [Accepted: 06/16/2024] [Indexed: 06/25/2024]
Abstract
Understanding the spatial distribution of plant available soil nutrients and influencing soil properties and delineation soil nutrient management zones (MZs) are important for implementing precision nutrient management options (PNMO) in an area to achieve maintainable crop production. We assessed spatial distribution pattern of plant available sulphur (S) (PAS), boron (B) (PAB), zinc (PAZn), manganese (PAMn), iron (PAFe), and copper (PACu), and soil organic carbon (SOC), pH, and electrical conductivity (EC) to delineate soil nutrients MZs in northeastern region of India. A total of 17,471 representative surface (0-15 cm depth) soil samples were collected from the region, processed, and analysed for above-mentioned soil parameters. The values of PAS (0.22-99.2 mg kg-1), PAB (0.01-6.45 mg kg-1), PAZn (0.05-13.9 mg kg-1), PAMn (0.08-158 mg kg-1), PAFe (0.50-472 mg kg-1), PACu (0.01-19.2 mg kg-1), SOC (0.01-5.80%), pH (3.19-7.56) and EC (0.01-1.66 dS m-1) varied widely with coefficient of variation of 15.5-108%. The semivariogram analysis highlighted exponential, Gaussian and stable best fitted models for soil parameters with weak (PACu), moderate (PAB, PAZn, PAFe, SOC, pH, and EC) and strong (PAS, and PAMn) spatial dependence. The ordinary kriging interpolation revealed different distribution patterns of soil parameters. About 14.8, 27.5, and 3.40% area of the region had PAS of ≤15.0 mg kg-1, PAB of ≤0.50 mg kg-1, and PAZn of had ≤0.90 mg kg-1, respectively. About 67.5, and 32.5% area had SOC content >1.00 and < 1.00%, respectively. Soil pH was ≤5.50, and >5.50 to ≤6.50 in 41.7 and 40.3% area of the region, respectively. The techniques of principal component analysis and fuzzy c-mean algorithm clustering produced 6 MZs of the region with different areas and values of soil parameters. The MZs had different levels of deficiency pertaining to PAS, PAB, and PAZn. The produced MZ maps could be used for managing PAS, PAB, PAZn, SOC and soil pH in order to implement PNMO. The study highlighted the usefulness of MZ delineation technique for implementation of PNMO in different cultivated areas for sustainable crop production.
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Affiliation(s)
- Arvind Kumar Shukla
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India; Rajmata Vijayaraje Scindia Krishi Viswavidyalaya, Gwalior 474 002, Madhya Pradesh, India
| | - Sanjib Kumar Behera
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India.
| | | | | | - Rahul Mishra
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India
| | - Samiron Dutta
- Assam Agricultural University, Jorhat 785 013, Assam, India
| | - Yogesh Sikaniya
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India
| | - Akanksha Sikarwar
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India
| | - Vimal Shukla
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India
| | - Siba Prasad Datta
- ICAR-Indian Institute of Soil Science, Bhopal 462 038, Madhya Pradesh, India
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Kirov A, Sizov V. Development of a method for targeted monitoring and processing of information security incidents of economic entities. JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2022. [DOI: 10.1007/s11416-022-00449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms. AXIOMS 2022. [DOI: 10.3390/axioms11080377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case.
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Tran MK, Panchal S, Khang TD, Panchal K, Fraser R, Fowler M. Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality. BATTERIES 2022; 8:19. [PMID: 35910082 PMCID: PMC9015652 DOI: 10.3390/batteries8020019] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/15/2022] [Indexed: 06/15/2023]
Abstract
Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.
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Affiliation(s)
- Manh-Kien Tran
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada;
| | - Satyam Panchal
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada; (S.P.); (R.F.)
| | - Tran Dinh Khang
- Department of Information Systems, Hanoi University of Science and Technology, Hanoi 10000, Vietnam;
| | - Kirti Panchal
- Department of Mathematics, Bhailalbhai & Bhikhabhai Institute of Technology (BBIT), Vallabh Vidyanagar 388120, Gujarat, India;
| | - Roydon Fraser
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada; (S.P.); (R.F.)
| | - Michael Fowler
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada;
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