Carpogenic ZnO nanoparticles: amplified nanophotocatalytic and antimicrobial action

This investigation has for the first time utilised environmental resource Prunus cerasifera seed extract phytochemicals for the green synthesis of carpogenic ZnO nanoparticles (NPs). Spherical morphology and size range of 56.57–107.70 nm at variable calcination temperatures without the use of any external reducing agent was obtained. The synthesised NPs exhibited hexagonal wurtzite geometry with an average crystal size 5.62 nm and a band gap of 3.4 eV. Carpogenic NPs were investigated for optical, compositional, morphological, and phytochemical make up via ultraviolet spectroscopy (UV–Vis), Fourier transform infrared analysis, X‐ray powder diffraction, scanning electron microscopy, and gas chromatography and mass spectrometry. Carpogenic NPs degraded methyl red up to 83% with pseudo‐first‐order degradation kinetics (R ²  = 0.88) in 18 min signifying their remediation role in environment in conformity with all principles of green chemistry. Photocatalytic assays were performed in direct solar irradiance. Nine pathogens of biomedical and agricultural significance having multi‐drug resistance were inhibited in vitro via the Kirby–Bauer disc diffusion assay. The enhanced photocatalytic and antimicrobial inhibition not only makes carpogenic ZnO NPs a future photo‐degradative candidate for environmental remediation but also a nanofertiliser, nanofungicide, and nanobactericide synthesised via bioinspired, biomimetic, green, and unprecedented route.Inspec keywords: visible spectra, Fourier transform spectra, nanocomposites, nanobiotechnology, transmission electron microscopy, X‐ray diffraction, biomimetics, zinc compounds, catalysis, antibacterial activity, microorganisms, chromatography, scanning electron microscopy, ultraviolet spectra, nanoparticles, photochemistry, infrared spectra, calcination, nanofabricationOther keywords: antimicrobial action, environmental resource, prunus cerasifera seed, phytochemicals, green synthesis, variable calcination temperatures, hexagonal wurtzite geometry, X‐ray powder diffraction, gas chromatography, mass spectrometry, pseudofirst‐order degradation kinetics, green chemistry, Aspergillus niger, Aspergillus flavus, Aspergillus terreus, antimicrobial inhibition, environmental remediation, photocatalytic inhibition, carpogenic nanoparticles, nanophotocatalytic action, size 5.62 nm, electron volt energy 3.4 eV, time 18.0 min, size 56.57 nm to 107.7 nm, ZnO     

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-of-the-art techniques.     

Early Detection of Diabetic Retinopathy Using Machine Intelligence through Deep Transfer and Representational Learning

Diabetic retinopathy (DR) is a retinal disease that causes irreversible blindness. DR occurs due to the high blood sugar level of the patient, and it is clumsy to be detected at an early stage as no early symptoms appear at the initial level. To prevent blindness, early detection and regular treatment are needed. Automated detection based on machine intelligence may assist the ophthalmologist in examining the patients’ condition more accurately and efficiently. The purpose of this study is to produce an automated screening system for recognition and grading of diabetic retinopathy using machine learning through deep transfer and representational learning. The artificial intelligence technique used is transfer learning on the deep neural network, Inception-v4. Two configuration variants of transfer learning are applied on Inception-v4: Fine-tune mode and fixed feature extractor mode. Both configuration modes have achieved decent accuracy values, but the fine-tuning method outperforms the fixed feature extractor configuration mode. Fine-tune configuration mode has gained 96.6% accuracy in early detection of DR and 97.7% accuracy in grading the disease and has outperformed the state of the art methods in the relevant literature.     

Organic additive assisted hydrothermal synthesis and photoluminescence properties of CeF<Subscript>3</Subscript>:Tb<Superscript>3+</Superscript> and NaCeF<Subscript>4</Subscript>:Tb<Superscript>3+</Superscript> nanoparticles

A series of Tb³⁺ doped CeF₃ and NaCeF₄ nanoparticles with different morphology and dimension were synthesized via hydrothermal method. Different organic additives, including sodium dodecyl sulfonate (SDS), polyvinylpyrrolidone (PVP), cetyltrimethyl ammonium bromide (CTAB), oleic acid (OA), polyethylene glycol (PEG), trisodium citrate (Cit) were introduced to control the crystallite size and morphology. Powder X-ray diffraction (PXRD), fourier transform infra-red spectra (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS) and down-conversion (DC) photoluminescence spectra were used to characterize the samples. The emission peaks of all the prepared samples centered at 490, 545, 585 and 621 nm which can be ascribed to the ⁵D₄→⁷F_J (J?=?6, 5, 4, 3) transitions respectively of Tb³⁺ ion. However, emission intensities are strongly controlled by morphology and particle sizes which are influenced by different organic additives used in synthesis. Moreover, the crystal growth process was monitored through a series of time-dependent experiments and a possible formation mechanism has been proposed.     

Activated flux TIG welding of Inconel 718 super alloy in presence of tri-component flux

In this study, we have explored the influence of newly developed tri-component oxide flux (Cr₂O₃, FeO, and MoO₃) on weldability, bead geometry, weld pool temperature variation, and mechanical strength of Inconel 718 welded joints. Moreover, the influence of used flux on weld pool, the surface morphology of electrode and penetration capability of tungsten inert gas (TIG) welding on Inconel 718 plates have been well elucidated. Results indicate that the flux mixture significantly increases the penetration depth as well as aspect ratio almost 200% as compared to conventional TIG welding. The arc constriction caused by newly developed oxide flux upsurges the heat density and the weld pool temperature of joints. The alloying effect caused by entrapped oxide particles greatly improves the hardness as well as the tensile strength of joints. The reported reinforcement in the welding performance may increase potential utility of the developed methods for real-world applications.     

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Multimedia Tools and Applications - The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational...     

Osteogenic Potential of Mesenchymal Stem Cells from Adipose Tissue,Bone Marrow and Hair Follicle Outer Root Sheath in a 3D Crosslinked Gelatin-Based Hydrogel

Bone transplantation is regarded as the preferred therapy to treat a variety of bone defects. Autologous bone tissue is often lacking at the source, and the mesenchymal stem cells (MSCs) responsible for bone repair mechanisms are extracted by invasive procedures. This study explores the potential of autologous mesenchymal stem cells derived from the hair follicle outer root sheath (MSCORS). We demonstrated that MSCORS have a remarkable capacity to differentiate in vitro towards the osteogenic lineage. Indeed, when combined with a novel gelatin-based hydrogel called Osteogel, they provided additional osteoinductive cues in vitro that may pave the way for future application in bone regeneration. MSCORS were also compared to MSCs from adipose tissue (ADMSC) and bone marrow (BMMSC) in a 3D Osteogel model. We analyzed gel plasticity, cell phenotype, cell viability, and differentiation capacity towards the osteogenic lineage by measuring alkaline phosphatase (ALP) activity, calcium deposition, and specific gene expression. The novel injectable hydrogel filled an irregularly shaped lesion in a porcine wound model displaying high plasticity. MSCORS in Osteogel showed a higher osteo-commitment in terms of calcium deposition and expression dynamics of OCN, BMP2, and PPARG when compared to ADMSC and BMMSC, whilst displaying comparable cell viability and ALP activity. In conclusion, autologous MSCORS combined with our novel gelatin-based hydrogel displayed a high capacity for differentiation towards the osteogenic lineage and are acquired by non-invasive procedures, therefore qualifying as a suitable and expandable novel approach in the field of bone regeneration therapy.     

Maximum-Likelihood Header Estimation: A Cross-Layer Methodology for Wireless Multimedia

We propose a novel cross-layer header estimation methodology that can be used by UDP-based wireless multimedia applications to estimate corrupted packet headers, thereby realizing significant throughput improvements. The proposed methodology requires only minor modifications to the protocol stack at the receiver while no modifications are needed to senders or intermediate nodes. We formulate header estimation as a problem of maximum-likelihood estimation of known parameters in noise. We derive likelihood functions for two wireless channel models, namely Markov and multifractal wavelet models. Our trace-driven video simulations at 2, 5.5 and 11 Mbps data rates of an 802.11b LAN demonstrate that significant improvements over normal UDP and UDP Lite can be achieved by employing header estimation with UDP.     

Morphological and Rheological Characteristics of PVP–CeO2 Blends

This work focuses on the flow behavior of the blend comprising polyvinyl pyrrolidone and cerium (IV) oxide (CeO₂) particles in submicron size, under low shear rates. The polyvinyl pyrrolidone–CeO₂ blends have been prepared and characterized by scanning electron microscopy, X-ray diffraction, and viscometry. The generation of core–shell morphology was verified from the scanning electron micrographs. Scanning electron microscopy shows that the blend formed is of porous nature. The particle size of CeO₂ increases with the concentration of both CeO₂ and polymer due to aggregation. The blend containing as high as 35?wt% of CeO₂ was found to exhibit pseudo-plastic response under low shear rate. The reasons for the observed morphology and other properties along with mechanism were explained. The main factor, which governs the properties of the end product, was van der Waals attractive forces that exist among the constituents of the system prepared.