Distribution characteristics and ecological risks of heavy metals in bottom ash, fly ash, and particulate matter released from municipal solid waste incinerators in northern Vietnam.

Residue concentrations of heavy metals, including As, Cd, Cr, Cu, Ni, Pb, and Zn, were determined in bottom ash, fly ash, and particulate matter (PM10) samples collected from five municipal incinerators in northern Vietnam to assess their occurrence, distribution characteristics, and potential risks. Concentrations and profiles of heavy metals are presented, showing the dominance of Zn in all types of samples. Highly volatile elements (Cd, Pb, and Zn) were found at elevated proportions in PM10 but not fly ash. The large difference in the heavy metal profiles could be explained by the variation of input raw materials, the absence of an appropriate cycle for the material feeding process, and post-combustion technology applied. Mass balance of heavy metals in the bottom ash, fly ash, and PM10 varied significantly between the investigated incinerators, largely due to the difference in incineration technology and air pollution control system. Emission factors and annual emissions were also estimated, indicating the highest value and amount in bottom ash, followed by PM10 and fly ash. Our results are among the first studies reporting contents and emissions of toxic elements in incinerated solid wastes in Vietnam.

Harris Hawk Optimization: A Survey onVariants and Applications.

In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.

Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising.

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model's hyper-parameters. This work proposes a novel deep learning-based Q-H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model's optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam's Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least [Formula: see text]. Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than [Formula: see text]. Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least [Formula: see text] and up to [Formula: see text] in the best case.

Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning.

Federated learning (FL) is a distributed model for deep learning that integrates client-server architecture, edge computing, and real-time intelligence. FL has the capability of revolutionizing machine learning (ML) but lacks in the practicality of implementation due to technological limitations, communication overhead, non-IID (independent and identically distributed) data, and privacy concerns. Training a ML model over heterogeneous non-IID data highly degrades the convergence rate and performance. The existing traditional and clustered FL algorithms exhibit two main limitations, including inefficient client training and static hyperparameter utilization. To overcome these limitations, we propose a novel hybrid algorithm, namely, genetic clustered FL (Genetic CFL), that clusters edge devices based on the training hyperparameters and genetically modifies the parameters clusterwise. Then, we introduce an algorithm that drastically increases the individual cluster accuracy by integrating the density-based clustering and genetic hyperparameter optimization. The results are bench-marked using MNIST handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL shows significant improvements and works well with realistic cases of non-IID and ambiguous data. An accuracy of 99.79% is observed in the MNIST dataset and 76.88% in CIFAR-10 dataset with only 10 training rounds.

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey.

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Radiotherapy Immobilization Mask Molding Through the Use of 3D-Printed Head Models.

PURPOSE: To evaluate the feasibility of a workflow free of a simulation appointment using three-dimensional-printed heads and custom immobilization devices. MATERIALS AND METHODS: Simulation computed tomography scans of 11 patients who received radiotherapy for brain tumors were used to create three-dimensional printable models of the patients' heads and neck rests. The models were three-dimensional-printed using fused deposition modeling and reassembled. Then, thermoplastic immobilization masks were molded onto them. These setups were then computed tomography-scanned and compared against the volumes from the original patient computed tomography-scans. Following translational +/- rotational coregistrations of the volumes from three-dimensional-printed models and the patients, the similarities and accuracies of the setups were evaluated using Dice similarity coefficients, Hausdorff distances, differences in centroid positions, and angular deviations. Potential dosimetric differences secondary to inaccuracies in the rotational positioning of patients were calculated. RESULTS: Mean angular deviation of the 3D-printout from the original volume for the Pitch, Yaw, and Roll were 1.1° (standard deviation = 0.77°), 0.59° (standard deviation = 0.41°), and 0.79° (standard deviation = 0.86°), respectively. Following translational + rotational shifts, the mean Dice similarity coefficients of the three-dimensional-printed and original volumes was 0.985 (standard deviation = 0.002) while the mean Hausdorff distance was 0.9 mm (standard error of the mean: 0.1 mm). The mean centroid vector displacement was 0.5 mm (standard deviation: 0.3 mm). Compared to plans that were coregistered using translational + rotational shifts, the D95 of the brain from three-dimensional-printed heads adjusted for TR shifts only differed by -0.1% (standard deviation = 0.2%). CONCLUSIONS: Patient head volumes and positions at simulation computed tomography scans can be accurately reproduced using three-dimensional-printed models, which can be used to mold radiotherapy immobilization masks onto. This strategy, if applied on diagnostic computed tomography scans, may allow symptomatic and frail patients to avoid a computed tomography-simulation and mask molding session in preparation for palliative whole brain radiotherapy.