Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

In silico exploration of Serratia sp. BRL41 genome for detecting prodigiosin Biosynthetic Gene Cluster (BGC) and in vitro antimicrobial activity assessment of secreted prodigiosin.

The raising concern of drug resistance, having substantial impacts on public health, has instigated the search of new natural compounds with substantial medicinal activity. In order to find out a natural solution, the current study has utilized prodigiosin, a linear tripyrrole red pigment, as an active ingredient to control bacterial proliferation and prevent cellular oxidation caused by ROS (Reactive Oxygen Species). A prodigiosin-producing bacterium BRL41 was isolated from the ancient Barhind soil of BCSIR Rajshahi Laboratories, Bangladesh, and its morphological and biochemical characteristics were investigated. Whole genome sequencing data of the isolate revealed its identity as Serratia sp. and conferred the presence of prodigiosin gene cluster in the bacterial genome. "Prodigiosin NRPS", among the 10 analyzed gene clusters, showed 100% similarity with query sequences where pigC, pigH, pigI, and pigJ were identified as fundamental genes for prodigiosin biosynthesis. Some other prominent clusters for synthesis of ririwpeptides, yersinopine, trichrysobactin were also found in the chromosome of BRL41, whilst the rest displayed less similarity with query sequences. Except some first-generation beta-lactam resistance genes, no virulence and resistance genes were found in the genome of BRL41. Structural illumination of the extracted red pigment by spectrophotometric scanning, Thin-Layer Chromatography (TLC), Fourier Transform Infrared Spectroscopy (FTIR), and change of color at different pH solutions verified the identity of the isolated compound as prodigiosin. Serratia sp. BRL41 attained its maximum productivity 564.74 units/cell at temperature 30˚C and pH 7.5 in two-fold diluted nutrient broth medium. The compound exhibited promising antibacterial activity against Gram-positive and Gram-negative bacteria with MIC (Minimum Inhibitory Concentration) and MBC (Minimum Bactericidal Concentration) values ranged from 3.9 to15.62 µg/mL and 7.81 to 31.25 µg/mL respectively. At concentration 500 µg/mL, except in Salmonella enterica ATCC-10708, prodigiosin significantly diminished biofilm formed by Listeria monocytogens ATCC-3193, Pseudomonas aeruginosa ATCC-9027, Escherichia coli (environmental isolate), Staphylococcus aureus (environmental isolate). Cellular glutathione level (GSH) was elevated upon application of 250 and 500 µg/mL pigment where 125 µg/mL failed to show any free radical scavenging activity. Additionally, release of cellular components in growth media of both Gram-positive and Gram-negative bacteria were facilitated by the extract that might be associated with cell membrane destabilization. Therefore, the overall findings of antimicrobial, antibiofilm and antioxidant activities suggest that in time to come prodigiosin might be a potential natural source to treat various diseases and infections.

Nutritional characteristics and antiradical activity of turmeric (Curcuma longa L.), beetroot (Beta vulgaris L.), and carrot (Daucus carota L.) grown in Bangladesh.

Curcuma longa L. (turmeric), Beta vulgaris L. (beetroot), and Daucus carota L. (carrot) grown in Bangladesh were analyzed for nutritional and phytochemical contents to reveal their comparative nutritional compositions and antiradical properties. Ash, protein, and carbohydrate content were significantly preeminent in beetroot as compared to others. Whereas fat content was found to be high in turmeric, carrots contained a great percentage of crude fiber. Beetroot was shown to have much greater potassium, calcium, and iron levels than others. Regarding amino acids, glutamic acid was found to be greater in beetroot and carrot whereas turmeric had significantly high aspartic acid content. Leucine had the highest concentration among essential amino acids in these three samples. Total antioxidant activity, total flavonoids, and phenolic contents in the methanolic extract of turmeric were found to be substantially higher than in beetroot and carrot. Furthermore, the extract of turmeric (IC50 value: 13.46 µg/mL) scavenged 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) free radicals considerably to a greater extent than beetroot (IC50 value: 380.61 µg/mL) and carrot (IC50 value: 1252.85 µg/mL). A positive correlation was found between the phytochemical contents and antiradical activity. The information from this study will help to find the potential ingredients from these plants for functional food.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework.

INTRODUCTION: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits. The researchers of this domain have already introduced different kinds of techniques, including data hiding, multiplexing, data compression, color QR Codes, and so on. However, the research on increasing the data storage capacity of the QR Codes is very limited and still operational. OBJECTIVES: The main objective of this work is to increase the data storage capacity of QR Codes in the IIoT domain. METHODS: In the first part, we have introduced a 4-State-Pattern-based encoding technique to generate the proposed 4-State QR (4SQR) Code where actual data are encoded into a 4SQR Code image which increases the data storage capacity more than the traditional 2-State QR Code. The proposed 4SQR Code consists of four types of patterns, including Black Square Box (BSB), White Square Box (WSB), Triangle, and Circle, whereas the traditional 2-State QR Codes consist of BSB and WSB. In the second part, the 4SQR Code decoding module has been introduced using the adaptive YOLO V5 algorithm where the proposed 4SQR Code image is decoded into the actual data. RESULTS: The proposed model is tested in a Digital Twin (DT) framework using randomly generated 3000 testing samples for the encoding module that converts into 4SQR Code images successfully and similarly for the decoding module that decodes the 4SQR Code images into the actual data. CONCLUSION: Experimental results show that this proposed technique offers increased data storage capacity two times than traditional 2-State QR Codes.

2D-2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS2 Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO2 Reduction.

A novel CuO-MoS2 based heterostructure catalyst model system is proposed where a CuO nanosheet with exposed {001} facet with proper termination is the active surface for the catalysis and a MoS2 nanosheet is the supporting layer. Density functional theory (DFT) calculations were performed to validate the model. The MoS2 bilayer forms a stable heterostructure with {001} faceted CuO with different terminations exposing oxygen and copper atoms (active sites) on the surface. The heterostructure active sites with a low oxidation state of the copper atoms and subsurface oxygen atoms provide a suitable chemical environment for the selective production of multicarbon products from CO2 electrocatalytic reduction. Furthermore, our heterostructure model exhibits good electrical conductivity, efficient electron transport to active surface sites, and less interfacial resistance compared to similar heterostructure systems. Additionally, we propose a photoenhanced electrocatalysis mechanism due to the photoactive nature of MoS2. We suggest that the photogenerated carrier separation occurs because of the interface-induced dipole. Moreover, we utilized a machine learning model trained on a 2D DFT materials database to predict selected properties and compared them with the DFT results. Overall, our study provides insights into the structure-property relationship of a MoS2 supported 2D CuO nanosheet based bifunctional catalyst and highlights the advantages of heterostructure formation with selective morphology and properly terminated surface in tuning the catalytic performance of nanocomposite materials.

Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM.

The recent outbreaks of the COVID-19 forced people to work from home. All the educational institutes run their academic activities online. The online meeting app the "Zoom Cloud Meeting" provides the most entire supports for this purpose. For providing proper functionalities require in this situation of online supports the developers need the frequent release of new versions of the application. Which makes the chances to have lots of bugs during the release of new versions. To fix those bugs introduce developer needs users' feedback based on the new release of the application. But most of the time the ratings and reviews are created contraposition between them because of the users' inadvertent in giving ratings and reviews. And it has been the main problem to fix those bugs using user ratings for software developers. For this reason, we conduct this average rating calculation process based on the sentiment of user reviews to help software developers. We use BERT-based sentiment annotation to create unbiased datasets and hybridize RNN with LSTM to find calculated ratings based on the unbiased reviews dataset. Out of four models trained on four different datasets, we found promising performance in two datasets containing a necessarily large amount of unbiased reviews. The results show that the reviews have more positive sentiments than the actual ratings. Our results found an average of 3.60 stars rating, where the actual average rating found in dataset is 3.08 stars. We use reviews of more than 250 apps from the Google Play app store. The results of our can provide more promising if we can use a large dataset only containing the reviews of the Zoom Cloud Meeting app.

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images.

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

Vertical profile of dissolved oxygen and associated water variables in the Pasur-Rupsha estuary of Bangladesh.

Vertical profile of dissolved oxygen (DO) and associated water variables were measured in the head of the Pasur-Rupsha estuary of Bangladesh. Water samples were collected from two stations at 0.60 m depth intervals during high and low tides from July, 2016 to January, 2017. DO concentration was ranged from 6.0 to 8.6 mg/l with maximum two units of variation in the profiles of the stations and demonstrated an inconsistent stratification pattern. The observed stratification pattern was remarkably triggered by a relatively high concentration of DO in the bottom layer than that of the surface layer during both the high and low tides in most of the study periods. High rate of freshwater flow at the surface; existence of estuarine gravitational circulation with 5-15 m depth profile; and lack of consumers in the bottom due to the high deposition of sediment particles were found responsible for the relatively high concentration of DO at bottom and low in surface layer of the estuary. Water temperature, salinity, pH, phytoplankton density and transparency were ranged from 22 to 32 °C, 0-6 ppt, 7.0 to 8.5, 1000-23000 ind/l and 13-17 cm respectively considering the depth profiles in the study periods. Temperature and plankton density were found significantly (P < 0.000) correlated with DO while the pH and salinity did not show any significant (P > 0.3) correlation. The ecosystem process associated with the vertical profile of DO explained in this study will provide considerable advances in understanding the ecosystem dynamics of the Pasur-Rupsha Estuary of Bangladesh.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic.

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

Hybrid EuIII Coordination Luminophore Standing on Two Legs on Silica Nanoparticles for Enhanced Luminescence.

In this study, we have demonstrated a two-legged, upright molecular design method for monochromatic and bright red luminescent LnIII -silica nanomaterials. A novel EuIII -silica hybrid nanoparticle was developed by using a doubly binding TPPO-Si(OEt)3 (TPPO: triphenyl phosphine oxide) linker. The TPPO-Si(OEt)3 was confirmed by 1 H, 31 P, 29 Si NMR spectroscopy and single-crystal X-ray analysis. Luminescent Eu(hfa)3 and Eu(tfc)3 moieties (hfa: hexafluoroacetylacetonate, tfc: 3-(trifluoromethylhydroxymethylene)camphorate) were fixed onto TPPO-Si(OEt)3 -modified silica nanoparticles, producing Eu(hfa)3 (TPPO-Si)2 -SiO2 and Eu(tfc)3 (TPPO-Si)2 -SiO2 , respectively. Eu(hfa)3 (TPPO-Si)2 -SiO2 exhibited the higher intrinsic luminescence quantum yield (93 %) and longer emission lifetime (0.98 ms), which is much larger than those of previously reported EuIII -based hybrid materials. Eu(tfc)3 (TPPO-Si)2 -SiO2 showed an extra-large intrinsic emission quantum yield (54 %), although the emission quantum yield for the precursor Eu(tfc)3 (TPPO-Si(OEt)3 )2 was found to be 39 %. These results confirmed that the TPPO-Si(OEt)3 linker is a promising candidate for development of EuIII -based luminescent materials.

Strong circularly polarized luminescence of mixed lanthanide coordination polymers with control of 4f electronic structures.

This paper reports chiral mixed Eu(iii)-Ln(iii) coordination polymers (Ln = Gd and Sm) with a high dissymmetry factor of circularly polarized luminescence (gCPL = 0.15) for the enhancement of the emission quantum yield (Φtot ≥ 50%), achieved via control of 4f electronic structures.

Formulation of a Hybrid Nanofertilizer for Slow and Sustainable Release of Micronutrients.

In this work, we have proposed a new formulation of a hybrid nanofertilizer (HNF) for slow and sustainable release of nutrients into soil and water. Urea-modified hydroxyapatite was synthesized, which is a rich source of nitrogen, calcium, and phosphate. Nanoparticles such as copper, iron, and zinc were incorporated into urea-modified hydroxyapatite to increase the efficiency of the proposed fertilizer. Different techniques including powder X-ray powder diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy were used to get insight into the properties, morphology, and structure of the as-prepared fertilizer. The developed HNF was used in a field experiment on the ladies' finger (Abelmoschus esculentus) plant. The slow release of HNF was observed during leaching studies and confirmed the availability of Ca2+, PO4 3-, NO2-, NO3-, Cu2+, Fe2+, and Zn2+. Furthermore, the presence of Cu2+, Fe2+, and Zn2+ nutrients in ladies' finger was confirmed by the inductively coupled plasma-optical emission spectrometry (ICP-OES) experiment. A considerable increase in the physicochemical properties such as swelling ratio and water absorption and retention capacities of the proposed fertilizer was observed, which makes the fertilizer more attractive and beneficial compared with the commercial fertilizer. The composition of the proposed HNF was functionally valuable for slow and sustainable release of plant nutrients. The dose of prepared HNF applied was 50 mg/week, whereas the commercial fertilizer was applied at a dose of 5 g/week to A. esculentus. The obtained results showed a significant increase of Cu2+, Fe2+, and Zn2+ nutrient uptake in A. esculentus as a result of slow release from HNF.

Crystallization of Methylammonium Lead Halide Perovskites by Optical Trapping.

Single crystals of organolead halide perovskites attract much attention to electrooptical and photovoltaic applications. They are usually prepared in precursor solutions incubated at controlled temperatures or under optimized vapor atmosphere conditions, and thus, multiple perovskite crystals are nucleated all over the solution. Multiple nucleation of crystals prevents efficient use of precursors in the preferential growth of large single crystals. An innovative approach is presented for spatiotemporally controlled, selective nucleation and growth of single crystals of lead halide perovskites by optical trapping with a focused laser beam. Upon such trapping in unsaturated precursor solutions, nucleation of MAPbX3 (MA=CH3 NH3 + ; X=Cl- , Br- , or I- ) is induced at the focal spot through increase in the concentration of perovskite precursors in the focal volume. The rate at which the nucleated crystal grows depends upon whether the perovskite absorbs the trapping laser or not. These findings suggest that optical trapping would be useful to prepare various perovskite single crystals and modify their optical and electronic properties; thereby, offering new methods for engineering of perovskite crystals.

Design of potential siRNA molecules for T antigen gene silencing of Merkel Cell Polyomavirus.

Merkel cell carcinoma (MCC) is the most aggressive skin cancer. Recently, it was demonstrated that human Merkel cell polyomavirus (MCV) is clonally integrated in 80% of MCC tumors. Genetic studies of MCV have shown that T antigen protein is responsible for replication of genome and play a foremost role in viral infection. Therefore, T antigen protein may be used as suitable target for disease diagnosis. Viral activity can be restrained through RNA interference (RNAi) technology, an influential method for post transcriptional gene silencing in a sequence specific manner. In current study four effective siRNA molecules for silencing of MCV were rationally designed and validated using computational methods, which may lead to knockdown the activity of virus. Thus, this approach may provide an insight for the chemical synthesis of antiviral RNA molecule for the treatment of MCC at genome level.