Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution is proposed. The use of advanced technologies and information security is a critical area for practitioners in the internet of things (IoT) and corresponding solutions. Therefore, this study also emphasizes information security, end-to-end solution, and experimental results. Firstly, a wearable wristband is proposed, incorporating 4G-enabled ultra-wideband (UWB) technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization (WHO). Secondly, distributed ledger technology (DLT) omits the centralized dependency for transmitting contact tracing data. Thirdly, a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism. Lastly, based on geofencing techniques, blockchain-enabled machine-to-machine (M2M) technology is proposed for quarantine management. The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management. Unlike existing research studies, the security aspect is also considered in the realm of blockchain. The practical implementation of the proposed solution also obtains the results. The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques, which could help battle pandemic situations. Researchers can also consider the 5G-enabled narrowband internet of things (NB-IoT) technologies to implement contact tracing solutions.     

Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum, Puccinia graminis, tomato yellow leaf curl. Therefore, this work uses the Support vector machine (SVM) classifier to detect and classify the plant disease using various steps like image acquisition, Pre-processing, Segmentation, feature extraction, and classification. The gray level co-occurrence matrix (GLCM) and the local binary pattern features (LBP) are used to identify the disease-affected portion of the plant leaf. According to experimental data, the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.     

Performance evaluation of submerged membrane bioreactor for municipal wastewater treatment: Experimental study and model validation with GPS-X software simulator

In this study, flat sheet submerged MBR (FS-SMBR) was investigated under 6 and 4 h hydraulic retention times (HRT) and 40 days solid retention time with real municipal wastewater. The implementation of the treatment system was evaluated in terms of physico-chemical and microbial analysis as well as sludge characterization. The permeate of FS-SMBR complied with the standards for reuse and non-detectable levels of faecal coliform was achieved at 6 and 4 h HRTs, with pore-blocking resistances (R_t) of 28 × 10¹¹ and 40 × 10¹¹/m, respectively. Examining another dominant factor, that is, sludge viscosity, the best results were acquired using HRT 4 h for domestic wastewater. Furthermore, the results obtained were validated and calibrated using the GPS-X software simulator, and the results of modelled data were completely matched with the experimental data under the same conditions.     

Experimental and Simulation Investigations of Mechanical Properties and Gamma Radiation Shielding of Lithium Cadmium Gadolinium Silicate Glasses Doped Erbium Ions

Silicon - Melt-quenching method used to fabricate SiO2-Li2O - CdO – Gd2O3 – Er2O3 glass system. The XRD diffractometer procedure is used to check the status of these samples. The...     

Heterocyclic azo dyes. II. Azo Dyes Containing the Benzotriazole and/or benzimidazole nucleus

The synthesis, some dyeing, and fastness properties of several new dispersed and acid dyes derived from the amino-benzotriazoles 1 & 3 and the aminobenzimidazoles 2a , 2b & 4 are described. The suitability of the amino derivatives 1 , 2a , 2b , 3 & 4 as bases for azoic dyeing has also been studied. Several interesting observations between the properties of some synthesised dyes and the corresponding benzene or naphthalene analogues are cited.     

Automated Artificial Intelligence Empowered White Blood Cells ClassificationModel

White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes data normalization and data augmentation process (rotation and zooming) at the initial stage. In addition, the residual network (ResNet) approach was used for feature extraction in which the initial hyperparameter values of the ResNet model are tuned by the use of AO algorithm. Finally, Bayesian neural network (BNN) classification technique has been implied for the identification of WBC images into distinct classes. The experimental validation of the AOTL-WBCC methodology is performed with the help of Kaggle dataset. The experimental results found that the AOTL-WBCC model has outperformed other techniques which are based on image processing and manual feature engineering approaches under different dimensions.     

Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

Recently, there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy (DR). DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world. Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images. This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy (DLLSHDM-DR) on Retinal Fundus Images. The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method. In the DLLSHDM-DR technique, image preprocessing is initially performed to improve the quality of the fundus image. Besides, the DLLSHDM-DR applies HybridNet for producing a collection of feature vectors. For retinal image classification, the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer (EPO) with a Deep Recurrent Neural Network (DRNN). The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our work. To assuring the improved performance of the DLLSHDM-DR model, a wide range of experiments was tested on the EyePACS dataset. The comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.     

Nitriles in Organic Synthesis: Synthesis of new pyridazine,pyridine and pyrazolo[3,4-b]pyridine derivatives

The reaction of diethyl monoiminomalonate with benzoylacetonitrile affords the ethyl 3-amino-4-cyanopentanoate derivative 1 a . Compound 1 a condensed with salicylaldehyde to yield the coumarin 5 and coupled with arenediazonium salts to yield the arenehydrazones 6a – c . Compounds 6a , b could be converted into the aminopyridazinium carboxylate 8a , b on treatment with aqueous sodium hydroxide. The reaction of 6a – c with hydrazine and phenyl hydrazine is reported.