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Amador-Castro F, González-López ME, Lopez-Gonzalez G, Garcia-Gonzalez A, Díaz-Torres O, Carbajal-Espinosa O, Gradilla-Hernández MS. Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 352:119959. [PMID: 38194871 DOI: 10.1016/j.jenvman.2023.119959] [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: 10/12/2023] [Revised: 12/20/2023] [Accepted: 12/23/2023] [Indexed: 01/11/2024]
Abstract
The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.
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Affiliation(s)
- Fernando Amador-Castro
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona No. 2514, 45201, Zapopan, Jal., Mexico
| | - Martín Esteban González-López
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona No. 2514, 45201, Zapopan, Jal., Mexico
| | - Gabriela Lopez-Gonzalez
- Water@leeds, School of Geography, University of Leeds, Leeds, LS2 9JT, UK; School of Geography, University of Leeds, Leeds, LS2 9JT, UK
| | - Alejandro Garcia-Gonzalez
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de La Salud, Av. General Ramon Corona No. 2514, 45201, Zapopan, Jal., Mexico
| | - Osiris Díaz-Torres
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona No. 2514, 45201, Zapopan, Jal., Mexico
| | - Oscar Carbajal-Espinosa
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona No. 2514, 45201, Zapopan, Jal., Mexico
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Mishra S. Internet of things enabled deep learning methods using unmanned aerial vehicles enabled integrated farm management. Heliyon 2023; 9:e18659. [PMID: 37576187 PMCID: PMC10415670 DOI: 10.1016/j.heliyon.2023.e18659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/17/2023] [Accepted: 07/24/2023] [Indexed: 08/15/2023] Open
Abstract
Smart livestock farming strives to make farming more lucrative, efficient, and ecologically beneficial by using digital technologies. Precision livestock fencing, in which each animal is followed and studied independently, is the most promising kind of smart livestock farming. The Internet of Things (IoT) allows farmers to save money and effort by keeping tabs on crops, mapping out their land, and giving them data to develop sensible management strategies for their farms. Surveillance, disaster management, firefighting, border patrol, and courier services employ Unmanned Aerial Vehicles (UAVs) that are originally created for the military. The segment focuses on UAVs in livestock and agricultural production. This is achieved via employing robots, drones, remote sensors, and computer imagery in unison with ever-improving in-Depth Learning for farming. Deep learning (DL) algorithms find many uses in the agricultural sector, from identifying plant diseases to estimating yields to detecting weeds to forecasting the weather and determining how much water is in the soil. The challenging characteristics of smart livestock farming are climate change, biodiversity loss, and continuous monitoring. Hence, in this research, the Unmanned Aerial Vehicles enabled Integrated Farm Management (UAV-IFM) has been designed to improve smart livestock farming. Safe and reliable tracking of livestock from farm to fork is made possible by this sensor, which has far-reaching implications for detecting and containing disease outbreaks and preventing the resulting financial losses and food-related health pandemics. UAV-IFM aims to improve the assessment process so that smart livestock farming may be more widely adopted and offers growth-supportive help to farmers. Conclusions gathered from this study's examination of the UAV-IFM reveal that these instruments correctly forecast and verify smart livestock farming management within the framework of the assessment procedure. The experimental analysis of UAV-IFM outperforms smart livestock farming in terms of efficiency ratio, performance, accuracy, and prediction.
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Affiliation(s)
- Shailendra Mishra
- Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
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Rao KS, Tirth V, Almujibah H, Alshahri AH, Hariprasad V, Senthilkumar N. Optimization of water reuse and modelling by saline composition with nanoparticles based on machine learning architectures. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 87:2793-2805. [PMID: 37318924 PMCID: wst_2023_161 DOI: 10.2166/wst.2023.161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water is a necessary resource that enables the existence of all life forms, including humans. Freshwater usage has become increasingly necessary in recent years. Facilities for treating seawater are less dependable and effective. Deep learning methods have the ability to improve salt particle analysis in saltwater's accuracy and efficiency, which will enhance the performance of water treatment plants. This research proposes a novel technique in optimization of water reuse with nanoparticle analysis based on machine learning architecture. Here, the optimization of water reuse is carried out based on nanoparticle solar cell for saline water treatment and the saline composition has been analyzed using a gradient discriminant random field. Experimental analysis is carried out in terms of specificity, computational cost, kappa coefficient, training accuracy, and mean average precision for various tunnelling electron microscope (TEM) image datasets. The bright-field TEM (BF-TEM) dataset attained a specificity of 75%, kappa coefficient of 44%, training accuracy of 81%, and mean average precision of 61%, whereas the annular dark-field scanning TEM (ADF-STEM) dataset produced specificity of 79%, kappa coefficient of 49%, training accuracy of 85%, and mean average precision of 66% as compared with the existing artificial neural network (ANN) approach.
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Affiliation(s)
- Koppula Srinivas Rao
- Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
| | - Vineet Tirth
- Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Asir 61421, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Guraiger, P.O. Box 9004, Abha, Asir 61413, Saudi Arabia
| | - Hamad Almujibah
- Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City 21974, Saudi Arabia
| | - Abdullah H Alshahri
- Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City 21974, Saudi Arabia
| | - V Hariprasad
- Department of Aerospace Engineering, Jain (Deemed-to-be) University, Jain Global Campus, Jakkasandra Post, Kanakapura 562112, India
| | - N Senthilkumar
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai 602105, India E-mail:
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Ghattavi S, Homaei A. Marine enzymes: Classification and application in various industries. Int J Biol Macromol 2023; 230:123136. [PMID: 36621739 DOI: 10.1016/j.ijbiomac.2023.123136] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/23/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023]
Abstract
Oceans are regarded as a plentiful and sustainable source of biological compounds. Enzymes are a group of marine biomaterials that have recently drawn more attention because they are produced in harsh environmental conditions such as high salinity, extensive pH, a wide temperature range, and high pressure. Hence, marine-derived enzymes are capable of exhibiting remarkable properties due to their unique composition. In this review, we overviewed and discussed characteristics of marine enzymes as well as the sources of marine enzymes, ranging from primitive organisms to vertebrates, and presented the importance, advantages, and challenges of using marine enzymes with a summary of their applications in a variety of industries. Current biotechnological advancements need the study of novel marine enzymes that could be applied in a variety of ways. Resources of marine enzyme can benefit greatly for biotechnological applications duo to their biocompatible, ecofriendly and high effectiveness. It is beneficial to use the unique characteristics offered by marine enzymes to either develop new processes and products or improve existing ones. As a result, marine-derived enzymes have promising potential and are an excellent candidate for a variety of biotechnology applications and a future rise in the use of marine enzymes is to be anticipated.
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Affiliation(s)
- Saba Ghattavi
- Fisheries Department, Faculty of Marine Science and Technology, University of Hormozgan, Bandar Abbas, Iran
| | - Ahmad Homaei
- Department of Marine Biology, Faculty of Marine Science and Technology, University of Hormozgan, Bandar Abbas, Iran.
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Zhu M, Wang J, Yang X, Zhang Y, Zhang L, Ren H, Wu B, Ye L. A review of the application of machine learning in water quality evaluation. ECO-ENVIRONMENT & HEALTH (ONLINE) 2022; 1:107-116. [PMID: 38075524 PMCID: PMC10702893 DOI: 10.1016/j.eehl.2022.06.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/19/2022] [Accepted: 06/01/2022] [Indexed: 12/31/2023]
Abstract
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.
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Affiliation(s)
- Mengyuan Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Jiawei Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Xiao Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Yu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Linyu Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Lin Ye
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, China
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An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition. ELECTRONICS 2022. [DOI: 10.3390/electronics11060968] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.
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Abstract
The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.
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