Search

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.

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.

Quality assessment of randomized controlled trials (RCTs) is important to prevent clinical application of erroneous results.This was an assessment of published RCTs in surgical subspecialties during 2011-2018 based on MEDLINE and EMBASE search. The primary objective of the present study was to quantitatively and qualitatively analyze the RCTs published from India based on year of publication, geographical distribution, and subspecialty using the modified Jadad score (high quality if score is ≥3; or ≥2 if blinded design was not feasible). Its secondary objective was to identify factors affecting the quality of RCTs.Among 1304 trials identified, 162 were analyzed. Of these 96 (59%) had a score of ≥3; and 104 (64.2%) were of high quality (score ≥2). Year-wise there was no significant quantitative (P = 0.329) or qualitative (P = 0.255) variation. Geographic regions had similar quantity (P = 0.206) and quality (P = 0.068). The RCTs among subspecialties too were comparable in quantity and quality. Higher impact factor of journal (P = 0.013) and assessment by Institute Review Board (IRB) (P = 0.004) were significantly associated with a better study quality. Type of institution, number of authors, centricity, assistance by a statistician, and source of funding did not affect the quality of RCTs.: The quantity and quality of surgical RCTs were stable and comparable over the years and across geographical regions and subspecialties. Higher impact factor of journal and review by IRB were significantly associated with a better study quality.

The genus Physiculus is widespread in the Indo-Pacific with more than 32 valid species, and about 12 species were recorded only from the Indian Ocean. Two new species similar to each other are collected from the outer reef drop off of Kavaratti Island, Lakshadweep, India, Arabian Sea and described. Physiculus indicus sp. nov. is characterised by its light organ situated at about the middle of pelvic-fin base and anus, a small light organ, a rather forward situated anus, no scales on the gular region, dorsal-fin rays 8‒9+56­‒60, anal-fin rays 55‒60, and 52 vertebrae. This species differs from nearest congeners in lacking gular scales, anus situated closer to the light organ and slightly fewer total vertebrae. Physiculus lakshadeepa sp. nov. is characterised by a relatively high first dorsal fin (51.7‒62.8% HL) and a light organ situated at the middle of the pelvic-fin base and anus. It has a small light organ, a rather forward situated anus, no scales on the gular region, dorsal-fin rays 8‒9+51­‒59, anal-fin rays 53‒59, and 49 vertebrae. Physiculus lakshadeepa differs from the nearest congeners in lacking gular scales, fewer anal-fin rays and fewer total vertebrae.

We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to environmental variations. Specifically, we show little-to-no degradation of pose accuracy even with extremely poor depth estimates from a lightweight depth predictor. Our framework also maintains high pose accuracy in extreme lighting variations compared to standard deep learning, even without explicit domain adaptation. By openly representing the map and intermediate feature maps (such as depth estimates), our framework also allows for faster updates and reusing intermediate predictions for other tasks, such as obstacle avoidance, resulting in much higher resource efficiency.