The nanostructures synthesized using the green chemistry method have recently attracted the attention of scientists due to their significance in many scientific domains. This work provides an overview of the biosynthesis of zinc oxide (ZnO) nanosheets (NSs) using Phyllanthus emblica plant (PEP) extract. X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Fourier transform infrared (FTIR) were used to analyze the synthesized ZnO-NSs. Evaluation of the antibacterial activity of biosynthesized ZnO-NSs was performed. ZnO-NSs exhibit effective antibacterial activity against Gram-positive (S. pyogenes and S. aureus) and Gram-negative (S. typhi and E. coli) bacterial strains. S. typhi is the most sensitive microbe towards ZnO-NSs and formed a 21 mm zone of inhibition (ZOI). ZnO-NSs are also tested as a photocatalyst in the degradation of methyl orange (MO) and rhodamine B (RB). The degradation rate of MO was 90%, and RB was 96% after being exposed to UV light for 120 min. The as-synthesized ZnO-NSs exhibited selective dye degradation and showed relatively better photocatalytic activity for positively charged (cationic) dyes. This work could lead to the fabrication of high-yield photocatalysts, which have the potential to degrade textile dyes from aqueous solution.

Biologically Reduced Zinc Oxide Nanosheets Using Phyllanthus emblica Plant Extract for Antibacterial and Dye Degradation Studies

Renewable energy can be harnessed from wastewater, whether from municipalities or industries, but this potential is often ignored. The world generates over 900 km3 of wastewater annually, which is typically treated through energy-consuming processes, despite its potential for energy production. Environmental pollution is a most important and serious issue for all and their adulterations to the aquatic system are very toxic in very low concentrations. Photocatalysis is a prominent approach to eliminating risky elements from the environment. The present study developed Zinc oxide (ZnO), Copper-doped Zinc oxide (CuZnO), and Cobalt-doped Zinc oxide (CoZnO) nanostructures (NSs) by facile hydrothermal route. The crystalline and structural stability of the synthesized nanostructures were evident from XRD and FESEM analysis. Metal, and oxygen bond and their interaction on the surfaces and their valency were explored from XPS spectra. Optical orientations and electron movements were revealed from UV-Visible analysis. After 100 min exposure time with 1 g of catalyst concentration 60%, 70%, and 89% of dye degraded, for dye concentration (5 mg/L to 50 mg/L), the huge variation observed (70% to 22%), (80% to 16%), (94% to 10%). The highest photodegradation rate (55%, 75%, 90%) was observed on pH~12 using ZnO, CoZnO, and CuZnO respectively. Photodegradation of methylene blue confirmed the largest surface area, rate of recombination, photo-excited charge carriers, photo-sensitivity range, and radical generations of ZnO, CuZnO, and CoZnO. The present study, therefore, suggested that CuZnO would be preferred to produce nanomaterials for industrial wastewater treatment like methylene.

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Structural, Optical, and Renewable Energy-Assisted Photocatalytic Dye Degradation Studies of ZnO, CuZnO, and CoZnO Nanostructures for Wastewater Treatment

The fast spread of Coronavirus (COVID-19) poses a huge risk to people all around the world. Recently, COVID-19 testing kits have been unavailable due to rise in effected people and large demand of tests. Keeping the urgency of the situation in mind, an automatic diagnosis method for early detection of COVID-19 is needed. The proposed deep learning decision support system (DSS) for COVID-19 employs MobileNet v2 Deep learning (DL) model for effective and accurate detection. Here we collected Cough auscultations through self-designed digital sensor. The primary experimental results show that the maximum accuracy for training is around 99.91%, and the maximum accuracy for validation is 98.61%, with 97.5% precision, 98.5%recall, and 98% F1-score. The Deep Learning-based model described here strives for similar performance to medical professionals and can help pulmonologist/radiologists increase their working productivity.

Deep Learning-based Decision Support System for classification of COVID-19 and Pneumonia patients

Prostheses play an important role in the rehabilitation of people who have suffered some type of amputation. However, due to its high‐cost and high complexity in performing movements of everyday tasks, users of these prostheses may encounter many difficulties. Therefore, this work proposes the development of a future artificial intelligence technology based on a low‐cost functional prosthesis prototype (manufactured in a 3D printer). In the present work, we describe an intelligent system that uses an artificial neural network to recognize patterns in muscle biopotential signals in order to control a prosthesis prototype in real time. Such a system is divided into three parts: the first that performs a human–machine integration through a graphical user interface; the second that performs the signal acquisition; the third that performs the training and generalization steps of the artificial neural network. The developed interface runs on a web application that has a database hosted in the cloud and in it the system user can: Acquisition of electromyography signals; Training phase of the artificial neural network; Sends the matrix of weights of the trained network to the microcontroller; Activates in the microcontroller, the state of action of the commands from the identified gestures. To compose the results of the present work, a search was initially carried out for the ideal parameters of the artificial neural network through signals obtained from 20 volunteers. In this step, it was possible to identify the topology that best classifies the signals of each gesture, as well as the investigation of the number of neurons in the hidden layer that causes a low generalization power due to overfitting. At the end of the project, it was possible to validate the use of the system with 15 new volunteers, and it was observed that in most cases, the performance of the commands in the prosthesis prototype were performed correctly. In addition, a project cost analysis was carried out, and it was possible to verify that the prototype developed is viable and has an affordable cost in relation to the Brazilian cost of living standards. In this way, the objective of the present work is in the development of a low cost artificial intelligence technology. Such a system is equipped with an algorithm based on neural networks that can deal with different muscle biopotential signals, in order to command a robotic prosthesis.

Development of intelligent and integrated technology for pattern recognition in EMG signals for robotic prosthesis command

A variety of artificial intelligence (AI) approaches are applied for the classification of hand movements in systems that use electromyography (EMG), which measures the electrical activity of muscles. In AI approaches, machine learning (ML) is frequently preferred and researched for this classification issue. In this study, hand gesture classification was performed with ML algorithms using EMGs of 10 hand movements. Features were extracted from the time domain (TD), frequency domain (FD), time-frequency domain (TFD) (via Wavelet-based Synchrosqueezing Transform), and Fractional Fourier Transform (FrFT) domain. After training 31 ML models with all features, Subspace k-Nearest Neighbor (kNN), which is ensemble-based learning, was determined as the best model. This model was trained with different feature and channel combinations, and the classification performances were examined as channel-based and domain-based, separately. In all cases, an accuracy of 97.10% was obtained as the highest via the TD-FD-FrFT domain feature combination, including all channels. When all the results are examined, an alternative classification approach is presented to the literature by proving that the computational load decreases while the accuracy value increases by determining and utilizing the channels and features that contain more related information about hand movement. 

Investigating the Effect of Signal Channels and Features in Various Domains on the EMG-based Hand Gesture Classification

Machines are built to give accessibility, precision, cost-effectiveness, and adaptability characteristics. This work will facilitate the recognition of hand gestures based on supervised learning. Signal processing-based techniques such as pre-processing (normalization) and segmentation (empirical mode decomposition) are employed. The Cubic-Support Vector Machine classifier is trained on four different EMG (Electromyography) based hand gestures named as wrist flexion, wrist extension, resting hand, clenched fist. Spectral domain features are extracted, which provide less variance than other extraction methods. This supervised machine learning model achieved a cumulative classification accuracy of 98.9%. This hand gesture-based system can help handicapped people in nonverbal communication and physically challenged individuals in non-invasive machine communication.

Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

In this study, successful synthesis of ZnO nanoparticles (NPs), CuO NPs, and ZnO/CuO nanocomposite through an eco-friendly method using Corriandrum sativum leaf extract as a capping agent is reported. Using XRD, FTIR, UV-Vis, and SEM techniques, the synthesized materials were characterized for structural analysis, functional groups identification, spectroscopic measurements, and morphological analysis. The percentage composition and purity of the samples were determined by using Energy Dispersive X-ray (EDX), which showed the synthesis of materials. Morphological analysis was done by Scanning Electron Microscopy (SEM) which reflected that the CuO NPs, ZnO NPs and ZnO/CuO nanocomposite were spherical, and the average size calculated by using Image J software was around 25 nm, 55 nm, and 11 nm, respectively. FTIR and UV-Vis analyses were used for synthetic confirmation through characteristic peaks of materials. The synthesized (ZnO, CuO, and CuO/ZnO) nanomaterials were evaluated for photocatalytic activity using methylene blue (MB) dye. Among all three photocatalysts, the composite showed maximum photodegradation compared to the other two materials. The present work could lead to a pathway for the decontamination of harmful dyes of wastewater released from different industries.

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Successive Photocatalytic Degradation of Methylene Blue by ZnO, CuO and ZnO/CuO Synthesized from Coriandrum sativum Plant Extract via Green Synthesis Technique

Zeeshan Abbasi

Papers

Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

Successive Photocatalytic Degradation of Methylene Blue by ZnO, CuO and ZnO/CuO Synthesized from Coriandrum sativum Plant Extract via Green Synthesis Technique

Deep Learning-based Decision Support System for classification of COVID-19 and Pneumonia patients