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Two water soluble Zn(II) Schiff base complexes of composition [Zn(L1)3][Zn(N3)4] (1) and [Zn(L2)2][Zn(N3)4] (2) were synthesized using bidentate N,N-donor (L1) and tridentate N,N,S-donor (L2) of Schiff base ligands viz. (E)-1-phenyl-N-(pyridin-2-ylmethylene)methanamine (L1) and (E)-2-((pyridin-2-ylmethylene)amino)benzenethiol (L2). Different spectroscopic tools such as FT-IR, UV-Vis, 1H NMR and fluorescence spectroscopy have been employed to characterize complexes 1 and 2. Efforts for getting single crystals suitable for X-ray crystal structure could not be achieved. However, from the spectroscopic data and on the basis of the structures of the related zinc(II) complexes reported earlier in the literature, the complexes could be proposed to adopt six coordinated octahedral geometries in one zinc centre coordinated to the ligands and while the other zinc centre has four azides (N3) terminally coordinated resulting in the octahedral and tetrahedral metal geometries around zinc metal atoms. The compounds behave as electrolytes in acetonitrile solution. In vitro antimicrobial activity of the Zn(II) complexes were studied against some microbes like Aspergillus niger, Fusarium ventricossum and Fusarium chlamydosporum. Effective antifungal activity of complex 2 was found against the tested fungal strains.

The sixth-generation (6G) has stricter criteria for the online learning capability and high interpretability of taught algorithms. It is anticipated that machine learning would be crucial for making network effective and flexible, however the most promising technologies are frequently considered as secret elements because of their profound designs’ major areas of strength for and. To make AI calculations more reasonable to 6G-empowered web of things (IoT) organizations, the motivation behind this paper is to analyse their translations. This article presents two different ways for acquiring translations: the free technique and the Joint strategy. Probes numerous IoT network datasets exhibit that the recommended techniques produce unrivalled execution regarding the two clarifications and forecasts.

Tuna fish is commercially important because of its popularity as both canned and raw meat. Tuna account for a significant proportion of the world fishery products. These fishes are separated based on their types in the industry for exporting raw fishes and processed fish products. An automatic system that can classify fishes into different species will ease the work in such industries. The work proposes an automated tuna classification system using textural macro features. The system uses a dual-tree network with signal matched complex wavelet transform. The signal matched wavelet obtained by the lifting scheme is used to develop a complex wavelet. The complex wavelet-based deep architecture is used to extract translation and deformation invariant features. The system performance is evaluated using different classifiers with ten-fold cross-validation. Results show that the proposed system gives an accuracy of 94.58% using cubic support vector machine (SVM) classifier. The proposed system is evaluated using an independent dataset, and the system shows an accuracy of 92.55%, the precision of 92.52%, recall of 92.03%, and F-score of 92.27%. Performance of the system is compared with the state-of-the-art systems using different performance metrics such as accuracy, precision, recall, F-score, and misclassification error.

Herein, Cu-rGO/ZnO ternary nanocomposite powder was synthesised by hydrothermal method. Analytical techniques such as XRD, FTIR, UV-Vis, SEM-EDAX, BET and XPS were used to study the morphology and structural properties of obtained nanocomposite. The photocatalytic activity of prepared nanocomposite was assessed by degradation of bromophenol blue in aqueous media. The catalytic results demonstrated that photocatalyst (0.1g) exhibited 86.2% bromophenol blue dye degradation efficiency with kinetic rate constant of 3.401x10− 2 min− 1. Antibacterial activity of nanocomposite was evaluated using agar well diffusion technique. The experimental results revealed that Cu-rGO/ZnO exhibited significant antibacterial activity against Escherichia coli, Staphylococcus aureus, Enterococcus faecium and Pseudomonas aeruginosa.

Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food. Separation of Tuna into their species is done in industries manually, and the process is tiresome. This work proposes an automated system for classifying Tuna species based on their images. An ensemble of region-based deep neural networks is used. A sub region contrast stretching operation is applied to enhance the images. Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks (CNN). After fine-tuning the models, the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor (G2DLBP). Statistical features from the descriptor are applied to different classifiers, and the best classifier for each image region model is identified. Different ensemble methods are subsequently used to combine the three CNN-G2DLBP models. Among the ensemble techniques, super learner ensemble method with random forest (RF) classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%. The performance of different ensemble methods is analyzed in terms of accuracy, precision, recall, and f-score. The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset. An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.

The networks in future generation uses the confluence of multi-media, broadband, and broadcast services, Cognitive Radio (CR) networks are located as a preferred paradigm to bring up with spectrum functionality traumatic conditions. CRS addresses the ones troubles via dynamic spectrum access. However, the precept traumatic conditions faced through manner of manner of the CR pertain to accomplishing spectrum overall performance. At the end, spectrum overall performance improvement models based on spectrum sensing and sharing models have attracted quite a few research hobby in modern-day years, which incorporates CR mastering models, network densification architectures, and Massive Multiple Input Multiple Output (MIMO), and beamforming techniques. This paper deals with a survey of modern CR spectrum overall improvement performance models and techniques which helps ultra-high reliability with low latency communications which might be resilient to surges in web page site visitors and competition for spectrum. These models and techniques, mainly speaks about permit a big form of functionality beginning from extra superb mobiliary broadband to large-scale Internet of Things (IoT) type communications. It also provides a research correlation for many of the regular periods of a spectrum block, as well as the realistic statistics rate, the models which are used in this paper are applicable in an ultra-high frequency band. This study provides a super compare of CRs and direction for future investigations into newly identified 5G research areas, such as in business enterprise and academia.

In this research work, we have found that tool wear rate of the materials of high-speed steel and Titanium alloy which are having optimum composition of carbon and other constituents. Here we have expounded the distinguishing characteristics of high-speed steel and Titanium alloys in respect to the tool wear. Subsequently, we have carried out heat treatment process, where two machined specimens were kept inside the muffle furnace once it attained required temperature, it was maintained at that temperature for a certain period of time and then cooled back to room temperature, we have conducted hardness test, tool wear test. In addition to these cutting operations have performed over mild steel and aluminum workpiece.

Aims / Objectives: Identification of fish species is essential in export industries. Among the different fish species exported, tuna forms a significant portion and hence the separation of tuna from other fishes is necessary. The work aims to develop automated systems for the separation of commercially important tuna from other fishes. Methodology: The work proposes two models for the classification of commercial fishes. The first model uses conventional feature descriptors, which extract features from both spatial and frequency domain. These features are combined and are reduced by an ensemble dimension reduction method. The combined and reduced feature sets are evaluated using different classifiers. The second proposed model uses four pre-trained convolutional neural networks, VGG16, VGG19, Xception, and MobileNet, for the classification. The models are fine-tuned for the classification process. Results: Results show that for the first model, extreme learning machine classifier with Mercer wavelet kernel gives high accuracy on combined feature set while the polynomial kernel ELM provides better performance with the reduced set. For the second model, a comparison of the performance of four CNN models is done, and results indicate that VGG19 outperforms other networks in the classification task. Conclusion: Among the two proposed models, pre-trained CNN based model shows better performance than the conventional method in the separation task. Different performance measures, accuracy, precision, recall, F-score, and misclassification error are used to evaluate the system. A comparison of performance of the proposed models with the state-of-the-art systems is also reported.

A numerical investigation on the effects of uniform and non-uniform heating of bottom wall on mixed convective heat transfer in a square porous chamber filled with nanofluid in the appearance of magnetic field is carried out. Uniform or sinusoidal heat source is fixed at the bottom wall. The top wall moves in either positive or negative direction with a constant cold temperature. The vertical sidewalls are thermally insulated. The finite volume approach based on SIMPLE algorithm is followed for solving the governing equations. The different parameters connected with this study are Richardson number (0.01 ≤ Ri ≤ 100), Darcy number (10−4 ≤ Da ≤ 10−1), Hartmann number (0 ≤ Ha ≤ 70), and the solid volume fraction (0.00 ≤ χ ≤ 0.06). The results are presented graphically in the form of isotherms, streamlines, mid-plane velocities, and Nusselt numbers for the various combinations of the considered parameters. It is observed that the overall heat transfer rate is low at Ri = 100 in the positive direction of lid movement, whereas it is low at Ri = 1 in the negative direction. The average Nusselt number is lowered on growing Hartmann number for all considered moving directions of top wall with non-uniform heating. The low permeability, Da = 10−4 keeps the flow pattern same dominating the magnetic field, whereas magnetic field strongly affects the flow pattern dominating the high Darcy number Da = 10−1. The heat transfer rate increases on enhancing the solid volume fraction regardless of the magnetic field.

This work deals mainly with the influence of nano titanium dioxide with addition of nano hydroxyapatite to get more adhesive coatings on 316L stainless steel by the electrophoretic deposition method. An adherent thin nano composite coating was obtained on a metal surface at 30 V and 2 minutes sintering at 700°C. The surface morphology and surface topography were characterized by X-ray diffraction, field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy, and surface profilometry, respectively. The adhesion strength of the composite coatings was evaluated using a tape test method, and mechanical strength was measured by the Vickers microhardness test. Further, the corrosion behavior of the coated and uncoated samples was tested in Hank’s solution using electrochemical impedance spectroscopy and cyclic potentiodynamic polarisation studies. In vitro analyses were carried out with MC3T3-E1 osteoblast cells. The nano composite coated sample exhibited higher adhesion, enhanced corrosion resistance with improved cell attachment and more cells proliferation compared to those of the uncoated sample, the protective layer of the former controlling the release of metal ions into the biological system.

A single female juvenile specimen of Stethojulis bandanensis (Bleeker, 1851) was collected from Kavaratti Island. The species showed a circumglobal distribution with a gap from the Bay of Bengal to the Western Indian Ocean. The present report fills a wide gap in the distributional record of the species to confirmation with reference voucher specimen and barcode. We generated the partial COI sequence of S. bandanensis to develop Neighbor-Joining and Maximum Likelihood trees to assess the phylogenetic relationship with other allied species and to understand the genetic uniqueness of this fish. Genetically S. bandanensis clade is closer with S. balteata and less with S. terina. The present article provides morphological features, distributional records, and phylogenetic affinity of the species to elucidate its occurrence along the coast of Lakshadweep in light of its ecology.

Phenacoscorpius adenensis Norman 1939, a rare deepwater scorpionfish was collected from Kavaratti Island, Lakshadweep Archipelago, and reported for the first time from the Central Indian Ocean and Indian waters. P. adenensis, which is morphologically similar to many closely related species, is described, and the specimen is preserved, catalogued and kept in reference museum.

Development of biocompatible dental implants has attained significant impact in dental industry. In the present work, we report for the development of biocompatible nano-Yttria-stabilized Zirconia (YSZ) coatings on 316L stainless steel (316L SS) obtained by EPD process followed by sintering at air atmosphere. The surface morphology and electrochemical performance of nano-YSZ-coated 316L SS were investigated to evaluate its corrosion behavior in artificial saliva medium. The surface morphology and composition of the coatings were characterized by XRD, Raman, and FESEM with EDAX, respectively. The electrochemical performance of the uncoated metal and nano-YSZ-coated 316L SS samples were evaluated in artificial saliva medium using electrochemical techniques such as Open Circuit Potential – time measurement (OCP), Electrochemical Impedance Spectroscopy (EIS), and Cyclic Potentiodynamic Polarization (CPP). The nano-YSZ-coated 316L SS thin film possess significant electrochemical and biocompatibility properties compared to uncoated metal. Nano-YSZ-coated 316L SS (70 V, 5 min, 800 °C) acts as an active and also as a good barrier against corrosive ions and resists ions penetration from artificial saliva to the metal surface.