Lettuce Leaves as Biosorbent Material to Remove Heavy Metal Ions from Industerial Wastewater

The current study was designed to remove Lead, Copper and Zinc from industrial wastewater using Lettuce leaves (Lactuca sativa) within three forms (fresh, dried and powdered) under some environmental factors such as pH, temperature and contact time. Current data show that Lettuce leaves are capable of removing Lead, Copper and Zinc ions at significant capacity. Furthermore, the powder of Lettuce leaves had highest capability in removing all metal ions. The highest capacity was for Lead then Copper and finally Zinc. However, some examined factors were found to have significant impacts upon bioremoval capacity of studied ions, where best biosorption capacity was found at pH 4, at temperature 50º C and contact time of 1 hour

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities

Middle-East OBGYN graduate education (MOGGE) foundation practice guidelines: use of labor charts in management of labor. Practice guideline no. 04-O-21

Since the 50 s of the last century, labor charts have been proposed and appraised as a tool to diagnose labor abnormalities and guide decision-making. The partogram, the most widely adopted form of labor charts, has been endorsed by the world health organization (WHO) since 1994. Nevertheless, recent studies and systematic reviews did not support clinical significance of application of the WHO partogram. These results have led to further studies that investigate modifications to the structure of the partogram, or more recently, to reconstruct new labor charts to improve their clinical efficacy. This guideline appraises current evidence on use of labor charts in management of labor specially in low-resource settings

Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications

In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data

Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics

Synergistic effect of Aminoguanidine and L-Carnosine against Thioacetamide-induced Hepatic Encephalopathy in rats: Behavioral, Biochemical and Ultra Structural Evidences

Hepatic encephalopathy (HE) depicts the cluster of neurological alterations that occur during acute or chronic hepatic injury. This study was aimed to evaluate the possible synergistic effect between aminoguanidine (AG; 100 mg/kg; p.o.) and l-carnosine (CAR; 100 mg/kg; p.o.) on HE that was induced by thioacetamide (TAA; 100 mg/kg; i.p) thrice weekly for six weeks. Twenty-four hours after the last treatment; behavioral changes, biochemical parameters, histopathological analysis, immunohistochemical and ultrastructural studies were conducted. Combining AG with CAR improved TAA-induced locomotor impairment and motor incoordination evidenced by; reduced locomotor activity and decline in motor skill performance as well as ameliorated cognitive deficits. Moreover, both drugs restored the levels of serum hepatic enzymes as well as serum and brain levels of ammonia. In addition to, the combination significantly modulated hepatic and brain oxidative stress biomarkers, inflammatory cytokines and cleaved caspase-3 expression. Furthermore, they succeeded to activate nuclear erythroid 2-related factor 2 (Nrf2) expression and ameliorate markers of HE including hepatic necrosis and brain astrocyte swelling. This study depicts that combining AG with CAR exerted new intervention for hepatic and brain damage in HE due to their complementary antioxidant, anti-inflammatory effect and hypoammonemic effects via Nrf2/HO-1 activation and NO inhibition.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author