Review
Environmental Sciences
M. Omar Abdeldayem, M. Areeg Dabbish, M. Mahmoud Habashy, K. Mohamed Mostafa, Mohamed Elhefnawy, Lobna Amin, G. Eslam Al-Sakkari, Ahmed Ragab, R. Eldon Rene
Summary: A viral outbreak like COVID-19 poses global challenges, with new detection methods needed for future outbreaks. Wastewater surveillance, air monitoring, and AI technologies are crucial for monitoring and detecting viral outbreaks worldwide.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Engineering, Environmental
Xiaoman Yu, Jie Guan, Xiaojiao Zhang, Hongcheng Wu, Yaoguang Guo, Shuai Chen
Summary: This study used bibliometrics to analyze the application of artificial intelligence in wastewater treatment from 2011 to 2022. The research identified an increasing number of published papers, with a sharp increase after 2018. China had the highest contribution in this field, followed by the US, Iran, and India. Collaborative networks mainly involved cooperation between European countries, China, and the US.
WATER SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Sk Mahmudul Hassan, Arnab Kumar Maji, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska
Summary: The study focuses on using deep convolutional neural network (CNN) models to identify and diagnose diseases in plants from their leaves. By achieving higher disease classification accuracy rates compared to traditional approaches, the implemented models show promise in efficient disease identification with less training time.
Review
Computer Science, Artificial Intelligence
Poornima Singh Thakur, Pritee Khanna, Tanuja Sheorey, Aparajita Ojha
Summary: Globally, major crops are significantly affected by diseases every year, leading to significant crop loss. Existing machine learning solutions are mainly focused on controlled environments and have not been widely applied for in-field plant disease detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Environmental
Mohamed Sherif Zaghloul, Gopal Achari
Summary: In this study, a full-scale biological nutrient removal wastewater treatment process was simulated using artificial intelligence. The researchers developed an ensemble model that combined artificial neural networks, adaptive neuro-fuzzy inference systems, and support vector regression to predict 15 process parameters. The model improved prediction accuracy and reduced ambiguity compared to other machine learning models.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Manisha Panjeta, Aryan Reddy, Rushabh Shah, Jash Shah
Summary: Deep Learning and Machine Learning are gaining popularity due to their improving algorithms, and their application in healthcare is expected to have a significant impact. The addition of AI to healthcare infrastructure has shown potential in improving overburdened and crumbling infrastructures worldwide, especially during the pandemic. These flexible and adaptable technologies can be used to combat COVID-19. This paper extensively examines various ML and DL-based models for detecting COVID-19, analyzing their pros and cons. It provides a systematic study of COVID-19 issues and rates detection methods based on availability, ease of use, accuracy, and cost. The comparison of different detection models helps researchers understand their advantages and disadvantages as a foundation for further research. The paper concludes by discussing the challenges and research questions associated with integrating these techniques with other detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Mathematics, Interdisciplinary Applications
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
Summary: Accurate cryptocurrency price predictions are important for investors and researchers, but the nonlinearity of the market makes it difficult to generate appropriate predictions. This study proposes three types of Recurrent Neural Networks (RNNs) for exchange rate predictions of major cryptocurrencies, and the results show that Bi-LSTM performs better than LSTM and GRU.
FRACTAL AND FRACTIONAL
(2023)
Review
Computer Science, Artificial Intelligence
Gongming Wang, Qing-Shan Jia, MengChu Zhou, Jing Bi, Junfei Qiao, Abdullah Abusorrah
Summary: This paper presents a comprehensive survey on water quality soft-sensing in wastewater treatment processes using artificial neural networks (ANNs). It covers problem formulation, common models, practical examples, and performance discussions. Various soft-sensing models are compared in terms of accuracy, efficiency, and complexity, with factors affecting the accuracy discussed as well. Challenges in soft-sensing models of WWTP are also pointed out for future exploration.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Shams Forruque Ahmed, Md. Sakib Bin Alam, Maruf Hassan, Mahtabin Rodela Rozbu, Taoseef Ishtiak, Nazifa Rafa, M. Mofijur, A. B. M. Shawkat Ali, Amir H. Gandomi
Summary: Deep learning is revolutionizing evidence-based decision-making techniques and has the ability to overcome limitations posed by large datasets. However, as a multidisciplinary field that is still in its nascent phase, there is a limited number of articles that comprehensively review DL architectures. This paper aims to provide insights into state-of-the-art DL modelling techniques and their challenges and advantages.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Green & Sustainable Science & Technology
Yi Wang, Yuhan Cheng, He Liu, Qing Guo, Chuanjun Dai, Min Zhao, Dezhao Liu
Summary: This article summarizes and analyzes the applications of artificial intelligence in wastewater treatment. It introduces commonly used AI models and their advantages and disadvantages, and reviews the inputs, outputs, objectives, and major findings of specific AI applications in water quality monitoring, laboratory-scale research, and process design. Although AI models have achieved success in the field of wastewater treatment, there are still challenges and limitations that need to be overcome in order to successfully apply AI models in this area.
Review
Medicine, General & Internal
Arti Rana, Ankur Dumka, Rajesh Singh, Manoj Kumar Panda, Neeraj Priyadarshi
Summary: Parkinson's disease is a neurodegenerative disease that affects motor skills. The use of artificial intelligence and machine learning techniques can enhance the diagnosis of the disease. A literature survey of research articles reveals that machine learning and deep learning methods, along with novel biomarkers, show promise in medical decision-making. However, challenges remain in selecting appropriate approaches and conducting related analyses.
Article
Computer Science, Information Systems
P. P. Fathimathul Rajeena, S. U. Aswathy, Mohamed A. Moustafa, Mona A. S. Ali
Summary: Farmers are not aware of the various corn diseases that affect agriculture, resulting in increasing crop failures due to lack of effective treatment or identification methods. Common corn diseases such as rust, blight, and northern leaf grey spot are prevalent. Accurate detection of diseases is not possible by visual observation alone, leading to improper pesticide use and potential harm to human health. Therefore, ensuring food security depends on accurate and automatic disease detection, which can be achieved by applying modern digital technologies.
Review
Computer Science, Artificial Intelligence
Majid Bahramian, Recep Kaan Dereli, Wanqing Zhao, Matteo Giberti, Eoin Casey
Summary: Increasing energy efficiency in wastewater treatment plants is crucial and exploiting data science and modelling, as well as deploying sensors and artificial intelligence, can help achieve this goal. Artificial Neural Networks (ANN) is the most popular standalone model for WWTP modelling, followed by Decision Trees (DT), Fuzzy Logic (FL), Genetic algorithm (GA), and Support Vector Machine (SVM). Hybrid models, especially the Machine Learning (ML)-metaheuristic, demonstrate better performance than standalone models. Industrial deployment is still lacking, and future research should focus on enhancing collaboration between interested parties for more effective implementation.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Thermodynamics
Lei Sun, Tianyuan Liu, Yonghui Xie, Di Zhang, Xinlei Xia
Summary: Deep learning models, particularly the RNN model, show superior performance in balancing accuracy and efficiency for turbine power prediction compared to shallow models and typical machine learning models. The study also highlights the influence of training size and input time-steps on the performance of the RNN model, demonstrating its potential in improving the accuracy of turbine power prediction.
Article
Computer Science, Interdisciplinary Applications
Darshil Patel, Dustin Bielecki, Rahul Rai, Gary Dargush
Summary: This paper presents a novel deep learning-based computational pipeline to overcome challenges in multiscale topology optimization. The proposed approach utilizes two neural networks to predict optimized microstructure and improve connectivity, resulting in improved optimization performance and faster computational time.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Geosciences, Multidisciplinary
Mustafa El-Rawy, Wael M. Elsadek, Florimond De Smedt
Summary: This study evaluates flood hazard in the Sinai Peninsula by analyzing morphometric characteristics of drainage patterns. The results show that about 17% of the peninsula is very highly prone to flooding, 39% is highly prone, and 34% is moderately prone. The study proposes management plans to reduce flood risk and protect major cities and roads.
Article
Environmental Sciences
Mustafa El-Rawy, Okke Batelaan, Nassir Al-Arifi, Ali Alotaibi, Fathy Abdalla, Mohamed Elsayed Gabr
Summary: This research aims to study the potential negative impacts of climate change on water irrigation management in Saudi Arabia. By using various climate models and scenarios, the study analyzes the increasing irrigation water requirements for major crops in the region. The findings indicate that without proper measures, there will be significant water shortages for agricultural purposes in the future.
Article
Engineering, Multidisciplinary
Ahmed Khaled Abdella Ahmed, Amira Mofreh Ibraheem, Mahmoud Khaled Abd-Ellah
Summary: This paper investigates the impact of different living styles on the type and quantity of municipal solid waste (MSW) and proposes a forecasting model using deep learning techniques to predict the future waste generation. The results show that the average solid waste generation varies among different areas with different living styles. The forecasting model effectively predicts the future waste composition for each area. This analysis provides valuable insights for decision-makers to optimize solid waste recycling.
RESULTS IN ENGINEERING
(2022)
Article
Agronomy
Ahmed Elbeltagi, Aman Srivastava, Jinsong Deng, Zhibin Li, Ali Raza, Leena Khadke, Zhoulu Yu, Mustafa El-Rawy
Summary: This study used six machine learning algorithms to forecast vapor pressure deficit in eight regions of Egypt, and the random forest model performed the best. The study recommends using this model for future climate adaptation research.
AGRICULTURAL WATER MANAGEMENT
(2023)
Article
Computer Science, Hardware & Architecture
Ali Ismail Awad, Mostafa Shokry, Ashraf A. M. Khalaf, Mahmoud Khaled Abd-Ellah
Summary: One of the crucial components of a smart grid is the advanced metering infrastructure (AMI), which integrates information and communication technologies with a conventional electricity grid. An information security risk assessment (ISRA) process must be carried out to identify potential risks before applying any security measures to the AMI system. This study contributes to AMI security by assessing potential security risks using the OCTAVE Allegro (OA) approach, identifying 11 risk scenarios that affect the confidentiality, integrity, or availability of the AMI system, and recommending risk mitigation approaches.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Environmental Sciences
Mustafa El-Rawy, Heba Fathi, Fathy Abdalla, Fahad Alshehri, Hazem Eldeeb
Summary: Jazan province in Saudi Arabia is facing significant challenges in water management due to its arid climate, limited water resources, and growing population. A study was conducted to assess the adaptability of groundwater for irrigation using hydro-chemical parameters and PCA. The results showed the importance of incorporating different techniques to understand and control groundwater quality in the study area.
Article
Environmental Sciences
Ahmed Elbeltagi, Aman Srivastava, Abdullah Hassan Al-Saeedi, Ali Raza, Ismail Abd-Elaty, Mustafa El-Rawy
Summary: This study aimed to accurately estimate reference evapotranspiration (ETo) in Egypt's agricultural governorates using machine learning algorithms. The REPTree model outperformed competitors and showed remarkable accuracy in predicting ETo. The study suggests that policymakers in Egypt should focus on climate adaptation for better water resource management.
Article
Green & Sustainable Science & Technology
Ahmed Khaled Abdella Ahmed, Mustafa El-Rawy, Amira Mofreh Ibraheem, Nassir Al-Arifi, Mahmoud Khaled Abd-Ellah
Summary: Assessing groundwater quality and future forecasting is crucial in Egypt's Sohag region. A water quality index model was developed using Deep Learning Time Series Techniques and long short-term memory model based on ten groundwater quality parameters from seven pumping wells. The model showed good performance and can assist in better managing groundwater resources in arid areas.
Article
Green & Sustainable Science & Technology
Mostafa Shokry, Ali Ismail Awad, Mahmoud Khaled Abd-Ellah, Ashraf A. M. Khalaf
Summary: Information security risk assessment is a crucial stage in the risk-management process, helping to identify current weaknesses and potential risks, and evaluating their likelihood and potential impact. This study aims to identify an appropriate ISRA method for advanced metering infrastructure (AMI) by aligning the risk assessment criteria for AMI systems with the characteristics of ISRA methodologies. The OA method is found to be the best-suited risk assessment method for AMI.
Article
Green & Sustainable Science & Technology
Mustafa El-Rawy, Heba Fathi, Wouter Zijl, Fahad Alshehri, Sattam Almadani, Faisal K. Zaidi, Mofleh Aldawsri, Mohamed Elsayed Gabr
Summary: This study examines the impact of climate change on the water demands of key food crops in Saudi Arabia and investigates adaptation techniques to mitigate these effects. The results indicate that climate change will lead to increased crop and irrigation demand in the mid to long term. This research is significant for improving water resource management planning in the Al-Riyadh region of Saudi Arabia.
Article
Environmental Sciences
Mohamed Wahba, Mustafa El-Rawy, Nassir Al-Arifi, Mahmoud M. Mansour
Summary: This study investigates the hazards of landslides and floods in two adopted basins in Japan. By employing a machine-learning approach, the researchers generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed and proved to be highly effective. The produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods.
Article
Green & Sustainable Science & Technology
Mohamed Elsayed Gabr, Mustafa El-Rawy, Nassir Al-Arifi, Wouter Zijl, Fathy Abdalla
Summary: In this study, a decentralized sewage water treatment system was suggested and designed in Ar Riyadh, Saudi Arabia, with the aim of safeguarding the environment and reusing treated water for irrigation purposes. The system consists of a septic tank, a subsurface horizontal flow constructed wetland, and a storage ground tank. The results showed that the system was able to effectively remove pollutants and meet Saudi Arabia's wastewater reuse requirements.
Article
Water Resources
Ahmed Awad, Mustafa El-Rawy, Mohmed Abdalhi, Nadhir Al-Ansari
Summary: In this study, the sensitivity of DRAINMOD predictions in farmland water balance to the time step was assessed, showing significant differences in the model's predictions when using hourly or daily time steps for ET0 computations. These differences affected the model's predictions of water fate, highlighting the importance of choosing the appropriate time step for accurate simulations and better utilization of agricultural water.
Article
Engineering, Multidisciplinary
Haitham M. Amin, Ali A. M. Gad, Mustafa El-Rawy, Usama A. Abdelghany, Rabiee A. Sadeek
Summary: Egypt, a semi-arid country, can use the Soil Aquifer Treatment (SAT) system as an efficient and safe method to treat and reuse wastewater for unrestricted irrigation.
JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Environmental
Francisco de Borja Ojembarrena, Elena Fuente, Angeles Blanco, Carlos Negro
Summary: Accumulation of Cr(VI) in industrial fiber cement process water can affect product quality and health, and it needs to be removed to avoid environmental risks. The study found that cationic cellulose nanocrystals (CCNCs) have higher adsorption efficiency and lower cost compared to conventional adsorbents.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Review
Engineering, Environmental
Shentan Liu, Yangchen Zhang, Xiaojuan Feng, Sang-Hyun Pyo
Summary: This paper summarizes the common problems of constructed wetlands (CWs) and proposes corresponding optimized solutions, including thermal insulation and additional aeration in cold climates, choosing suitable plant species and planting patterns to enhance phytoremediation in CWs, using various methods to monitor and evaluate CW clogging, implementing anti-clogging measures, controlling greenhouse gas emissions during CW treatment, constructing and utilizing CW databases, designing appropriate CW types, and implementing strict technical management and supervision.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Norzila Mohd, Nazlina Haiza Mohd Yasin, Wan Hasnidah Wan Osman, Mohd Sobri Takriff
Summary: This study demonstrates that Chlamydomonas sp. is effective in removing nutrients from palm oil mill effluent and shows particular efficiency in the removal of ammonium and phosphate.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Mingzhen Zhang, Guijian Liu, Ruijia Liu, Jinzhao Xu, Wen Si, Yong Wei
Summary: Tieguanyin tea stem was used to synthesize modified biochar for the adsorption of copper and zinc. Among the modified biochar, Fe-BCs had the highest adsorption capacity, with Fe-BC650 being the best adsorbent. It reached adsorption equilibrium in 1 minute and had a maximum adsorption capacity of 303.5996 mg/g for Cu (II) and 254.0711 mg/g for Zn (II) in single-metal solutions. However, there were antagonistic impacts between Cu2+ and Zn2+.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Lingling Zhao, Qiaoshu Zhou, Yun Yang, Yuan Zhang, Yewei Qiu, Yanjun Chen, Xian Jin, Xiangjun Yang, Shixiong Wang
Summary: This research presents a method for selectively recovering gold from e-waste water using metal sulfides microspheres prepared by hydrothermal technique. The results show that the assistance of visible light significantly improves the gold adsorption performance of metal sulfides. The metal sulfides exhibit excellent gold adsorption properties, high selectivity, ultra-high adsorption capacity, and good recyclability.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Laishram Saya, W. Rameshwor Singh, Sunita Hooda
Summary: Organic-inorganic nanoblends, such as polysaccharide-blended-inorganic ones, have versatile applications and have gained significant research attention. In this study, a magnetic guar gum/graphene oxide nanocomposite blended with CuO was successfully prepared and investigated for its adsorption capacity for ciprofloxacin removal. The results showed that the nanocomposite exhibited excellent adsorption capacity and the adsorption process was spontaneous.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Umit Ecer, Sakir Yilmaz
Summary: A novel and effective catalyst Co@MIL-68(Fe)@Fe3O4@CM-BC was synthesized and used for the catalytic reduction of crystal violet in aqueous environments. The catalyst showed high efficiency in degrading crystal violet under specific conditions, and exhibited good regeneration performance.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Chi Zhang, Miao Zhang, Haohan Yang, Wei Cai, Jun Wu
Summary: Enhancing the bioaugmentation of common nitrifiers in the partial nitrification/anammox process can decrease the activity of nitrite oxidizing bacteria and increase the activity of anammox bacteria, leading to improved total nitrogen removal efficiency.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Runzhang Zuo, Dajun Ren, Yangfan Deng, Canhui Song, Yubin Yu, Xiejuan Lu, Feixiang Zan, Xiaohui Wu
Summary: This study found that fine bubble aeration in membrane bioreactors (MBR) can better control membrane fouling and reduce energy consumption. However, compared to fine bubble aeration, coarse bubble aeration released more proteins and polysaccharides, resulting in a decrease in functional bacteria and enzymes involved in nitrogen and phosphorus pathways.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Ch Tahir Mehmood, Jiekai Dai, Hanjun Zhao, Ziyi Zhong
Summary: Wirelessly powered photocatalyst-coated spheres (WPS) have been developed and demonstrated for various applications, including photodegradation and fouling control. These WPS are cost-effective, easy to fabricate, and have excellent visible light sources, making them suitable as carriers for photocatalysts.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Yifan Wang, Xiaoxuan Kang, Yuting Li, Ruyi Li, Chang Wu, Luyao Wang, Chongqing Wang, Tao Yang, Ming Ge, Zhangxing He
Summary: Cobalt/carbon nanofibers (Co/CNF) were prepared and exhibited excellent catalytic activity in the degradation of tetracycline by activating peroxodisulfate (PDS). The effects of process parameters on tetracycline removal were studied, and the reaction mechanism was confirmed through experiments.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Ting Wei, Yaqian Zhao, Jia Guo, Bin Ji, Alvaro Pun Garcia, Abraham Esteve Nunez
Summary: This study developed a novel lightweight substrate (Al-NLS) composed of aluminum sludge and polyurethane, and investigated its effectiveness in constructed wetlands (CWs). Results showed that Al-NLS had favorable adsorption capacities for phosphorus and removal efficiencies for pollutants in real domestic wastewater. It also positively influenced the microbial community. Therefore, Al-NLS can be considered as a new alternative substrate in CWs technology.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)
Article
Engineering, Environmental
Dabojani Das, Achinta Bordoloi, Heyang Yuan, Daniel J. Caldwell, Rominder P. S. Suri
Summary: This study evaluates the degradation kinetics of antibiotic resistance genes and mobile genetic elements in water matrices by ozone treatment. The results show that extracellular antibiotic resistance genes can be effectively removed, while intracellular genes are more resistant to degradation. The impact of wastewater matrix on gene degradation is minimal.
JOURNAL OF WATER PROCESS ENGINEERING
(2024)