Tailoring the topology of ZIF-67 metal-organic frameworks (MOFs) adsorbents to capture humic acids.

Chlorination is a versatile technique to combat water-borne pathogens. Over the last years, there has been continued research interest to abate the formation of chlorinated disinfection by-products (DBPs). To prevent hazardous DBPs in drinking water, it is decided to diminish organic precursors, among which humic acids (HA) resulting from the decomposition and transformation of biomass. Metal-organic frameworks (MOFs) such as zeolitic imidazolate frameworks (ZIFs) have recently received tremendous attention in water purification. Herein, customized ZIF-67 MOFs possessing various physicochemical properties were prepared by changing the cobalt source. The HA removal by ZIF-67-Cl, ZIF-67-OAc, ZIF-67-NO3, and ZIF-67-SO4 were 85.6%, 68.9%, 86.1%, and 87.4%, respectively, evidently affected by the specific surface area. HA uptake by ZIF-67-SO4 indicated a removal efficiency beyond 90% in 4  90% after 60 min mixing the solution with 0.3 g L-1 ZIF-67-SO4. Notably, an acceptable removal performance (∼72.3%) was obtained even at HA concentrations up to 100 mg L-1. The equilibrium data fitted well with the isotherm models in the order of Langmuir> Hill > BET> Khan > Redlich-Peterson> Jovanovic> Freundlich > and Temkin. The maximum adsorption capacity qm for HA uptake by ZIF-67-SO4 was 175.89 mg g-1, well above the majority of adsorbents. The pseudo-first-order model described the rate of HA adsorption by time. In conclusion, ZIF-67-SO4 presented promising adsorptive properties against HA. Further studies would be needed to minimize cobalt leaching from the ZIF-67-SO4 structure and improve its reusability safely, to ensure its effectiveness and the economy of adsorption system.

Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification.

Parkinson's disease (PD) has become widespread these days all over the world. PD affects the nervous system of the human and also affects a lot of human body parts that are connected via nerves. In order to make a classification for people who suffer from PD and who do not suffer from the disease, an advanced model called Bayesian Optimization-Support Vector Machine (BO-SVM) is presented in this paper for making the classification process. Bayesian Optimization (BO) is a hyperparameter tuning technique for optimizing the hyperparameters of machine learning models in order to obtain better accuracy. In this paper, BO is used to optimize the hyperparameters for six machine learning models, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Ridge Classifier (RC), and Decision Tree (DT). The dataset used in this study consists of 23 features and 195 instances. The class label of the target feature is 1 and 0, where 1 refers to the person suffering from PD and 0 refers to the person who does not suffer from PD. Four evaluation metrics, namely, accuracy, F1-score, recall, and precision were computed to evaluate the performance of the classification models used in this paper. The performance of the six machine learning models was tested on the dataset before and after the process of hyperparameter tuning. The experimental results demonstrated that the SVM model achieved the best results when compared with other machine learning models before and after the process of hyperparameter tuning, with an accuracy of 92.3% obtained using BO.

Optimizing classification of diseases through language model analysis of symptoms.

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

Semantic segmentation of microbial alterations based on SegFormer.

Introduction: Precise semantic segmentation of microbial alterations is paramount for their evaluation and treatment. This study focuses on harnessing the SegFormer segmentation model for precise semantic segmentation of strawberry diseases, aiming to improve disease detection accuracy under natural acquisition conditions. Methods: Three distinct Mix Transformer encoders - MiT-B0, MiT-B3, and MiT-B5 - were thoroughly analyzed to enhance disease detection, targeting diseases such as Angular leaf spot, Anthracnose rot, Blossom blight, Gray mold, Leaf spot, Powdery mildew on fruit, and Powdery mildew on leaves. The dataset consisted of 2,450 raw images, expanded to 4,574 augmented images. The Segment Anything Model integrated into the Roboflow annotation tool facilitated efficient annotation and dataset preparation. Results: The results reveal that MiT-B0 demonstrates balanced but slightly overfitting behavior, MiT-B3 adapts rapidly with consistent training and validation performance, and MiT-B5 offers efficient learning with occasional fluctuations, providing robust performance. MiT-B3 and MiT-B5 consistently outperformed MiT-B0 across disease types, with MiT-B5 achieving the most precise segmentation in general. Discussion: The findings provide key insights for researchers to select the most suitable encoder for disease detection applications, propelling the field forward for further investigation. The success in strawberry disease analysis suggests potential for extending this approach to other crops and diseases, paving the way for future research and interdisciplinary collaboration.

The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study.

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.

Clinical effectiveness of Liraglutide 3.0 mg and impact of weight loss in improving obesity-related comorbid conditions in King Fahad Medical City, Kingdom of Saudi Arabia: A real-world experience.

Obesity has emerged as one of the most challenging worldwide problems and, if left untreated, can lead to major illnesses and consequences that can impair patients' health The study's objective was to evaluate the effectiveness of Liraglutide 3.0 mg in inducing weight loss and to assess the improvement of obesity-related comorbid conditions among people living with obesity (PwO) in the Kingdom of Saudi Arabia (KSA). A retrospective cohort review of PwO with or without diabetes and taking Liraglutide 3.0 mg in combination with diet and exercise for weight management was performed and evaluated at King Fahad Medical City, Riyadh, KSA. We collected patient data from electronic medical records for different parameters. The side effects were not recorded. A cohort of 399 patients who used Liraglutide 3.0 mg for 6 months was included in the study. At baseline, the mean age of the cohort was 46.4 (±12.1) years, mean BMI was 40.4 (±7.7) kg/m2 and most patients (74.4%) were women. Their mean average weight loss was 6.5 (±9.5) kg; p < .001. In the entire cohort, 52.6% of subjects had lost ≥5% of their bodyweight, 27.8% of subjects had lost ≥10%, and 11.3% of subjects had lost ≥15% of their bodyweight. There was a significant reduction in HbA1c by 0.5% (p < .0001) at 6 months of the treatment. Liraglutide 3.0 mg treatment did not affect systolic blood pressure and alanine transferase. Liraglutide 3.0 mg resulted in clinically significant weight loss with better glycaemic control, confirming the drug's effectiveness in the real-world evidence setting.

Removal of toxic lead from aqueous solution using a low-cost adsorbent.

Valorization of waste materials and byproducts as adsorbents is a sustainable approach for water treatment systems. Pottery Granules (PG) without any chemical and thermal modification were used as a low-cost, abundant, and environmentally benign adsorbent against Pb(II), the toxic metal in drinking water. The porous structure and complex mineral composition of PG made it an efficient adsorbent material for Pb(II). The effect of key physicochemical factors was investigated to determine the significance of contact time, PG dose, pH, solution temperature, and coexisting ions, on the process. Pb(II) removal increased by PG dose in the range of 5-15 g/L, and agitation time from 5 to 60 min. Increasing Pb(II) concentration led to a drop in Pb(II) removal, however, adsorption capacity increased significantly as concentration elevated. Pb(II) removal also increased significantly from ~ 45% to ~ 97% by pH from 2 to 12. A ~ 20% improvement in Pb(II) adsorption after rising the solution temperature by 30˚C, indicated the endothermic nature of the process. The sorption was described to be a favorable process in which Pb(II) was adsorbed in a multilayer onto the heterogeneous PG surface. The qmax of 9.47 mg/g obtained by the Langmuir model was superior among many reported low-cost adsorbents. The Pb(II) adsorption was described well by the Pseudo- first-order kinetic model. Na+, Mg2+, Ca2+, Cd2+, and Zn2+ showed a negligible effect on Pb(II) adsorption. However, the presence of Mn2+ and Fe2+ significantly hindered the process efficacy. In conclusion, the use of waste material such as PG against Pb(II) is a viable option from the economic and effectiveness points of view.

Boosting the adsorptive and photocatalytic performance of MIL-101(Fe) against methylene blue dye through a thermal post-synthesis modification.

Photocatalytic degradation under ultra-low powered light is a viable advanced oxidation process technique against extensive emerging contaminants. As a new and remarkable class of nanoporous materials, metal-organic frameworks (MOFs), attract interest for the supreme adsorptive and photocatalytic functionalities. An outstanding MOF, MIL-101(Fe) chosen as a photocatalyst template for the synthesis of α-Fe2O3 by a simple thermal modification to improve the structural properties toward methylene blue (MB) eradication. Octahedron-like α-Fe2O3 photocatalyst (Modified MIL-101(Fe), M-MIL-101(Fe)) was superior in dispersion and separation properties in aqueous medium. Moreover, the adsorptive and catalytic performance was increased for modified form by ~ 7.3% and ~ 17.1% compared to pristine MIL-101(Fe), respectively. Synergistic improvement of MB removal achieved by simultaneous adsorption/degradation under 5-W LED irradiation. Parametric study indicated an 18.1% and 44.5% improvement in MB removal was observed by increasing pH from 4 to 10, and M-MIL-101(Fe) dose from 0.2 to 1 g L-1, respectively. MB removal followed the pseudo-second-order kinetics model and the process efficiency dropped by 38% as MB concentration increased from 5 to 20 mg L-1. Radical trapping tests revealed the significant role of [Formula: see text] and electron radicals as the major participants in dye degradation. A significant loss in the efficiency of M-MIL-101(Fe) was observed in the reusability tests that is good to study further. In conclusion, a simple thermal post-synthesis modification on MIL-101(Fe) improved its structural, catalytic, and adsorptive properties against MB.

Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

The Use of Polyetheretherketone (PEEK) as an Alternative Post and Core Material: Five-Year Follow-Up Report.

This clinical report demonstrated the use of polyetheretherketone (PEEK) for manufacturing of custom-made post and core in weakened endodontically treated central incisors. The PEEK structure was manufactured using computer-aided design/computer-aided manufacturing (CAD/CAM). The optimal fit of this custom-made endodontic post allowed a thinner cement layer; and removed the need to manufacture a core build-up. While supplementary clinical trials and in vitro studies are needed to totally elucidate the advantages and limitations of PEEK as an option for post and core manufacturing, this case report showed that it can be promising for a predictable and simplified treatment with five years of success.

A novel Gray-Scale spatial exploitation learning Net for COVID-19 by crawling Internet resources.

Today, the earth planet suffers from the decay of active pandemic COVID-19 which motivates scientists and researchers to detect and diagnose the infected people. Chest X-ray (CXR) image is a common utility tool for detection. Even the CXR suffers from low informative details about COVID-19 patches; the computer vision helps to overcome it through grayscale spatial exploitation analysis. In turn, it is highly recommended to acquire more CXR images to increase the capacity and ability to learn for mining the grayscale spatial exploitation. In this paper, an efficient Gray-scale Spatial Exploitation Net (GSEN) is designed by employing web pages crawling across cloud computing environments. The motivation of this work are i) utilizing a framework methodology for constructing consistent dataset by web crawling to update the dataset continuously per crawling iteration; ii) designing lightweight, fast learning, comparable accuracy, and fine-tuned parameters gray-scale spatial exploitation deep neural net; iii) comprehensive evaluation of the designed gray-scale spatial exploitation net for different collected dataset(s) based on web COVID-19 crawling verse the transfer learning of the pre-trained nets. Different experiments have been performed for benchmarking both the proposed web crawling framework methodology and the designed gray-scale spatial exploitation net. Due to the accuracy metric, the proposed net achieves 95.60% for two-class labels, and 92.67% for three-class labels, respectively compared with the most recent transfer learning Google-Net, VGG-19, Res-Net 50, and Alex-Net approaches. Furthermore, web crawling utilizes the accuracy rates improvement in a positive relationship to the cardinality of crawled CXR dataset.

Gender Differences in the Attitudes and Management of People with Obesity in Saudi Arabia: Data from the ACTION-IO Study.

Purpose: Several studies have investigated gender differences in various obesity-related outcomes. Females were found to have more accurate weight perception and reported more frequency of attempted weight loss. The objective of this study was to assess gender differences in the attitudes and management of people with obesity (PwO) in Saudi Arabia using data from the ACTION-IO study. Patients and Methods: A survey was conducted in Saudi Arabia in June and July 2018 on adults with obesity (based on self-reported body mass index of ≥30 kg/m2). Results: A total of 1000 people with obesity completed the survey; 565 (56.5%) were male (mean age of 36.9 years and mean BMI of 33.5 kg/m2) and 435 (43.5%) were female (mean age of 36.3 years and mean BMI of 34.5 kg/m2). The two most reported motivations for wanting to lose weight for both groups were to improve appearance (38%) and to have more energy (35%). Females were more likely to trust their health-care provider (HCP) advice about weight management when compared to males (87% females, 82% males, p = 0.059) and were more likely to have concerns regarding long-term safety associated with prescription weight loss medications (65% female versus 59% males, p = 0.043). On the other hand, males were more likely to seek their physician to prescribe weight loss medication if they hear of a new medication (55% males versus 46% females, p = 0.014), and more to believe that there are good options available for weight loss medications (74% males versus 67% females, p = 0.040). Also, more males prefer to take weight loss medications than to have a weight loss surgery (65% males, 59% females, p = 0.054). Conclusion: Overall, this study increases our understanding on the attitudes of both females and males towards the management of weight loss and opens the discussion for gender-specific weight loss interventions.

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQISWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R2 values in the testing phase, with R2 values of 0.9667, 0.9316, and 0.9259 for BEX, GQISWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 µS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

COVID-19: a new deep learning computer-aided model for classification.

Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset.

BACKGROUND AND PURPOSE: COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. MATERIALS AND METHODS: In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. RESULTS: Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. CONCLUSIONS: A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

Defluoridationof drinking water by metal impregnated multi-layer green graphene fabricated from trees pruning waste.

A novel adsorbent with excellent adsorptive properties for fluoride was prepared through a green and cheap synthesis route. Populus caspica pruning wastes, a cheap agri-waste material, were reduced to multi-layer green graphene (MLG) and then post-modified to aluminum/iron modified multi-layer green graphene (AMLG and IMLG). Batch experiments revealed the effect of pH (3-11), contact time (0.5-12 h), and initial fluoride concentration (5-40 mg/L). The conversion of raw material to MLG increased the specific surface area about 120 times (from 4 to 475 m2/g). Furthermore, a significant improvement in zero points of charge (pHzpc) was attained for IMLG (7.1) and AMLG (8) compared with pristine MLG (4.3). Fluoride showed superior affinity to AMLG and IMLG compared with MLG. Fluoride removal increased gradually by pH from 3 to 8 and then decreased sharply up to pH 11. The study of process dynamics demonstrated the monolayer fluoride adsorption onto AMLG and IMLG controlled by the chemisorptions. The highest predicted adsorption capacities based on the Langmuir model were 31.52, 47.01, and 53.76 mg/g for MLG, IMLG, and AMLG, respectively. Considering economic and technical feasibility presents AMLG and IMLG as a promising candidate against water contamination by elevated fluoride. Graphical abstract.

HANA: A Healthy Artificial Nutrition Analysis model during COVID-19 pandemic.

BACKGROUND AND OBJECTIVE: The impact of diet on COVID-19 patients has been a global concern since the pandemic began. Choosing different types of food affects peoples' mental and physical health and, with persistent consumption of certain types of food and frequent eating, there may be an increased likelihood of death. In this paper, a regression system is employed to evaluate the prediction of death status based on food categories. METHODS: A Healthy Artificial Nutrition Analysis (HANA) model is proposed. The proposed model is used to generate a food recommendation system and track individual habits during the COVID-19 pandemic to ensure healthy foods are recommended. To collect information about the different types of foods that most of the world's population eat, the COVID-19 Healthy Diet Dataset was used. This dataset includes different types of foods from 170 countries around the world as well as obesity, undernutrition, death, and COVID-19 data as percentages of the total population. The dataset was used to predict the status of death using different machine learning regression models, i.e., linear regression (ridge regression, simple linear regularization, and elastic net regression), and AdaBoost models. RESULTS: The death status was predicted with high accuracy, and the food categories related to death were identified with promising accuracy. The Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 metrics and 20-fold cross-validation were used to evaluate the accuracy of the prediction models for the COVID-19 Healthy Diet Dataset. The evaluations demonstrated that elastic net regression was the most efficient prediction model. Based on an in-depth analysis of recent nutrition recommendations by WHO, we confirm the same advice already introduced in the WHO report1. Overall, the outcomes also indicate that the remedying effects of COVID-19 patients are most important to people which eat more vegetal products, oilcrops grains, beverages, and cereals - excluding beer. Moreover, people consuming more animal products, animal fats, meat, milk, sugar and sweetened foods, sugar crops, were associated with a higher number of deaths and fewer patient recoveries. The outcome of sugar consumption was important and the rates of death and recovery were influenced by obesity. CONCLUSIONS: Based on evaluation metrics, the proposed HANA model may outperform other algorithms used to predict death status. The results of this study may direct patients to eat particular types of food to reduce the possibility of becoming infected with the COVID-19 virus.

Modified wheat straw-derived graphene for the removal of Eriochrome Black T: characterization, isotherm, and kinetic studies.

A cost-effective and environment-benign adsorbent was prepared from an abundant agro-waste material. Wheat straw was reduced to graphene and then modified by crosslinking to epichlorohydrin. During the conversion process of wheat straw to graphene, the specific surface area increased more than 100 times (from 4 to 415 m2 g-1). The adsorption efficiency of raw wheat straw, graphene nanosheets, and modified graphene against Eriochrome Black T (EBT) were 8.0, 34.7, and 74.4%, respectively. The modified graphene was further investigated for the effect of environmental condition, i.e., pH (3 to 11), EBT concentration (25-100 mg L-1), adsorbent dosage (0.25-0.75 g L-1), contact time (5-60 min), and solution temperature (30-60 °C). The dye removal remained at a high level under a wide range of pH from 3 to 9. The EBT removal decreased from 87.3 to 54.5 by increasing dye concentration and increased from 38.2 to 85.4% by increasing adsorbent dose in the studied ranges. Dye removal also increased by mixing time from 5 to 30 min, whereas a slight drop was observed by continuing agitation up to 60 min. Conducting experiments at various temperatures revealed an endothermic process. Pseudo-first-order and pseudo-second-order models were adequate to represent the adsorption kinetics. Isotherm models suggest a multilayer adsorption of EBT molecules on heterogeneous modified graphene surface with a maximum adsorption capacity of 146.2 mg g-1. The present work demonstrated that the modified graphene obtained from available and low-cost agro-wastes could be used effectively as adsorbent against EBT from aqueous media.