Application of response surface methodology to optimize the removal of nitrate from aqueous solutions using electrocoagulation

زمینه و هدف: نیترات یکی از مهم ترین آلاینده های موجود در طبیعت است که تهدیدی جدی برای بهداشت و سلامت انسان و کیفیت منابع آبی محسوب می شود. یکی از کارآمدترین روش های حذف این آلاینده، الکتروکواگولاسیون می باشد. روش بررسی: این مطالعه تجربی، در مقیاس پایلوت انجام گردید. نمونه ها به صورت سنتتیک و در غلظت های 300-100 میلی گرم بر لیتر نیترات تهیه گردید. متغیرهای مستقل شامل زمان واکنش، اختلاف پتانسیل الکتریکی و pH بودند. عملکرد فرایند بر اساس درصد حذف نیترات ارزیابی شد. تعیین تعداد آزمایشات، آنالیز آماری داده های آزمایشگاهی و بهینه سازی کارآیی حذف نیترات با به کارگیری روش پاسخ سطح و طرح مرکب مرکزی انجام شد. یافته ها: نتایج نشان داد که غلظت نیترات، زمان واکنش، اختلاف پتانسیل الکتریکی و pH، مربع غلظت نیترات و اثرات متقابل غلظت نیترات- اختلاف پتانسیل الکتریکی، غلظت نیترات- زمان واکنش، غلظت نیترات– pH و اختلاف پتانسیل الکتریکی- زمان واکنش، مهم ترین پارامترهای تأثیرگذار بر روی حذف نیترات به روش الکتروکواگولاسیون بودند. کارایی حذف نیترات در شرایط بهینه (زمان واکنش 68 دقیقه، اختلاف پتانسیل الکتریکی 17 ولت و pH برابر با 10، 88 بود. درجه مطلوبیت مدل در این شرایط 98 بود. نتیجه گیری: الکتروگواگولاسیون فرایندی موثر در کاهش نیترات است؛ همچنین روش پاسخ سطح با استفاده از طرح مرکب مرکزی برای بهینه سازی متغیرهای موثر در فرایند حذف نیترات به روش الکتروکواگولاسیون مناسب است

Self-scheduling approach to coordinating wind power producers with energy storage and demand response

The uncertainty of wind energy makes wind power producers (WPPs) incur profit loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems (ESSs) and demand response (DR). To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The opti-mization problem is formulated as a Mixed-Integer Linear Programming (MILP) problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method en-hances the profit of the VPP compared to previous models.fi=vertaisarvioitu|en=peerReviewed

Corneal topography and higher-order aberrations in patients with type 2 diabetes mellitus

Background: Changes in blood sugar levels cause alterations in the anterior segment and retina of the eye. This study was aimed at evaluating corneal topography, aberrometry, and corneal asphericity in patients with treatment-naive type 2 diabetes mellitus (T2DM). Methods: Participants with treatment-naive T2DM were enrolled in this cross-sectional study. The inclusion criteria were glycated hemoglobin A1c (Hb A1c) greater than or equal to 7.5% and absence of other ocular or systemic diseases. Patients who refused to participate or had a history of topical or systemic steroid use, hyperlipidemia, hypertension, anemia, prior ocular disorder or surgery, diabetic retinopathy, glaucoma, cataract, active ocular inflammatory or infectious disease, or contact lens use were excluded. All participants underwent a comprehensive ophthalmic examination. The Pentacam HR Scheimpflug tomography system (Pentacam High Resolution; Oculus, Wetzlar, Germany) was used to measure the anterior-segment parameters. Results: Sixty eyes of 30 patients with a male-to-female ratio of 1:1 were included; the mean (standard deviation [SD]) age and Hb A1c were 51.63 (6.73) years and 8.82% (1.31%), respectively. The mean (SD) values of central corneal thickness, root mean square (RMS) of total aberration, RMS of lower-order aberrations, RMS of higher-order aberrations, spherical aberration, 0° coma, 90° coma, flat anterior keratometry (K), steep anterior K, mean anterior K, anterior topographic astigmatism, flat posterior K, steep posterior K, mean posterior K, posterior topographic astigmatism, anterior corneal asphericity, and posterior corneal asphericity were 540.22 (24.47) µm, 1.72 (0.73) µm, 1.63 (0.73) µm, 0.51 (0.17) µm, + 0.31 (0.09) µm, - 0.06 (0.15) diopters (D), 0.003 (0.21) D, 43.87 (1.49) D, 44.69 (1.50) D, 44.28 (1.44) D, + 0.82 (0.83) D, - 6.25 (0.27) D, - 6.55 (0.31) D, - 6.40 (0.28) D, - 0.30 (0.15) D, - 0.32 (0.12) Q-value, and - 0.47 (0.17) Q-value, respectively. Conclusions: We presented the mean values of Pentacam parameters for aberrometry, keratometry, and corneal asphericity in patients with treatment-naive T2DM. These values could serve as a baseline for prospective monitoring of the ocular health status of this cohort and for comparison with future cohorts of patients with well-controlled T2DM. Further studies are required to assess the presence and applicability of ocular changes following intensive blood glucose control in T2DM and further understand the related pathophysiology

A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network

Marine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans’ chemistry, are causing the potential collapse of the marine environment’s health. Due to the increase of marine debris, including plastics in coastlines, ocean and sea surfaces, and even in deep ocean layers, there is a need for developing new advanced technology for the detection of large-sized marine pollution (with sizes larger than 1 m) using state-of-the-art remote sensing and machine learning tools. Therefore, we developed a cloud-based framework for large-scale marine pollution detection with the integration of Sentinel-2 satellite imagery and advanced machine learning tools on the Sentinel Hub cloud application programming interface (API). Moreover, we evaluated the performance of two shallow machine learning algorithms of random forest (RF) and support vector machine (SVM), as well as the deep learning method of the generative adversarial network-random forest (GAN-RF) for the detection of ocean plastics in the pilot site of Mytilene Island, Greece. Based on the obtained results, the shallow algorithms of RF and SVM achieved an overall accuracy of 88% and 84%, respectively, with available training data of plastic debris. The GAN-RF classifier improved the detection of ocean plastics of the RF method by 8%, achieving an overall accuracy of 96% by generating several synthetic ocean plastic samples

Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery

The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing

Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery

The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing

Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data

The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing

A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification using limited training samples

Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of enough reference data for accurate large-scale wetland mapping. As such, the main objective of this study was to investigate the efficient deep-learning models for generating high-resolution and temporally rich training datasets for wetland mapping. The Sentinel-1 and Sentinel-2 satellites from the European Copernicus program deliver radar and optical data at a high temporal and spatial resolution. These Earth observations provide a unique source of information for more precise wetland mapping from space. The second objective was to investigate the efficiency of vision transformers for complex landscape mapping. As such, we proposed a 3D Generative Adversarial Network (3D GAN) to best achieve these two objectives of synthesizing training data and a Vision Transformer model for large-scale wetland classification. The proposed approach was tested in three different study areas of Saint John, Sussex, and Fredericton, New Brunswick, Canada. The results showed the ability of the 3D GAN to stimulate and increase the number of training data and, as a result, increase the accuracy of wetland classification. The quantitative results also demonstrated the capability of jointly using data augmentation, 3D GAN, and Vision Transformer models with overall accuracy, average accuracy, and Kappa index of 75.61%, 73.4%, and 71.87%, respectively, using a disjoint data sampling strategy. Therefore, the proposed deep learning method opens a new window for large-scale remote sensing wetland classification