A Novel Blockchain and Internet of Things-Based Food Traceability System for Smart Cities.

Rapid urbanization has recently caused serious problems for cities all around the world. Smart cities have drawn much interest from researchers in the present research paradigm to manage the expanding urban population. Frameworks for smart cities are planned and implemented using platforms based on blockchain and the Internet of Things (BIOT). Smart cities may use the BIoT platform to provide improved transportation, food traceability, and healthcare services. Food safety is one of the sectors where less research has been done than the others. The importance of food safety is now more widely recognized, making it essential to improve the traceability and transparency of the food supply chain. In this paper, a novel BIOT-based layered framework using EOSIO has been proposed for effective food traceability. The proposed system first identifies the suitable traceability units to provide better transparency and traceability and then defines and implements a layered architecture using Ethereum and EOSIO blockchain platforms. The performance of the proposed EOSIO-based model is evaluated using the practicality of the consensus algorithm, block production rate, throughput, and block confirmation time. The proposed traceability system attains a block production rate of 0.5 s and a block confirmation time of 1 s, which is much lower than the Ethereum-based traceability system. Hence, from the experimental evidence, the superiority of the proposed EOSIO-based food traceability can be observed.

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

A new clustering method for the diagnosis of CoVID19 using medical images.

With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score.

Formulation and performance evaluation of polymeric mixed micelles encapsulated with baicalein for breast cancer treatment.

The present study is aimed to formulate baicalein-loaded mixed micelles to enhance the solubility and oral bioavailability. Baicalein encapsulated D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) and pluronic F127 (F127) combined micelles were prepared and investigated for anticancer effect. The optimized formulation contains 25.04 ± 0.24 nm mean particle size of micelles with a zeta potential value of -4.01 ± 0.5 mV. The calculated entrapment efficiency percentage of baicalein within the micellar structure was 83.43 ± 0.13% and the in vitro release of baicalein from micelles displayed a sustained release profile at pH 7.4. The incorporation of baicalein within micelles core was also confirmed by FTIR analysis of formulation, which hardly represents the characteristic peak of baicalein, indicating successful entrapment of the drug. In vitro cell culture experiments revealed baicalein-loaded micelles significantly enhanced cellular uptake and cytotoxicity against MDAMB-231 cell lines in comparison to free baicalein. Additionally, as compared to free baicalein, baicalein micelles demonstrated greater apoptosis-inducing potential while the results of the cell cycle study exhibited arrest of cells at the G0/G1 phase of the cell cycle. Results of ROS (reactive oxygen species) and MMP (mitochondrial membrane potential) assay revealed the ROS-dependent mitochondrial-mediated apoptosis pathway utilized by developed formulation to inhibit cell proliferation. Thus, the developed nano micelles can serve as a potent carrier system for baicalein against breast cancer.

Drug Resistance Reversal Potential of Nanoparticles/Nanocomposites via Antibiotic's Potentiation in Multi Drug Resistant P. aeruginosa.

Bacteria employ numerous resistance mechanisms against structurally distinct drugs by the process of multidrug resistance. A study was planned to discover the antibacterial potential of a graphene oxide nanosheet (GO), a graphene oxide-zinc oxide nanocomposite (GO/ZnO), a graphene oxide-chitosan nanocomposite (GO-CS), a zinc oxide decorated graphene oxide-chitosan nanocomposite (GO-CS/ZnO), and zinc oxide nanoparticles (ZnO) alone and in a blend with antibiotics against a PS-2 isolate of Pseudomonas aeruginosa. These nanocomposites reduced the MIC of tetracycline (TET) from 16 folds to 64 folds against a multidrug-resistant clinical isolate. Efflux pumps were interfered, as evident by an ethidium bromide synergy study with nanocomposites, as well as inhibiting biofilm synthesis. These nanoparticles/nanocomposites also decreased the mutant prevention concentration (MPC) of TET. To the best of our knowledge, this is the first report on nanomaterials as a synergistic agent via inhibition of efflux and biofilm synthesis.

Diversity in phenotypic and nutritional traits in vegetable amaranth (Amaranthus tricolor), a nutritionally underutilised crop.

BACKGROUND: Assessment of genetic diversity in a crop-breeding programme helps in the identification of diverse parental combinations to create segregating progenies with maximum genetic variability and facilitates introgression of desirable genes from diverse germplasm into the available genetic base. RESULTS: In the present study, 39 strains of vegetable amaranth (Amaranthus tricolor) were evaluated for eight morphological and seven quality traits for two test seasons to study the extent of genetic divergence among the strains. Multivariate analysis showed that the first four principal components contributed 67.55% of the variability. Cluster analysis grouped the strains into six clusters that displayed a wide range of diversity for most of the traits. CONCLUSION: Cluster analysis has proved to be an effective method in grouping strains that may facilitate effective management and utilisation in crop-breeding programmes. The diverse strains falling in different clusters were identified, which can be utilised in different hybridisation programmes to develop high-foliage-yielding varieties rich in nutritional components.