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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.

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 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.

Freezing is the process of extending the shelf life of broiler chicken meat. Since frozen meat is getting popular among the consumers, a study was undertaken to estimate the psychrophilic count, Streptococcal count and yeast and mould count in the frozen broiler chicken meat samples stored at -12 oC, -18oC, -24oC under aerobic and vacuum packaging for one year. There is a significant difference between (P

Tuna fish is a popular food because of its nutritional value and taste. Demand for various species of tuna increases over time, necessitating the development of a system to sort tuna fish into distinct species in export sectors in order to accelerate the process. The work proposes an automated tuna classification system based on split octonion network. The images are initially preprocessed and divided into region images. Each region image is applied to a split octonion network with eleven layers. In addition, a split octonion channel attention module is presented, which is fed to the last two convolutional layers. The features from the three octonion networks are fused and applied to a series of dense layers. In the last layer, a softmax classifier is utilized for final classification. Results show that the proposed region-based split octonion network with attention module gives an accuracy of 98.01% on tuna database. The region-based tuna classification model is fine-tuned for the categorization of six species from QUT-FishBase dataset and Fish-Pak dataset. The system shows accuracies of 97.83% and 98.17% on QUT-FishBase and Fish-Pak datasets, respectively. The proposed methodology is also compared with existing approaches using a variety of evaluation criteria.

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.

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.

A total of 792 conventional healthy 1-day-old Ross 308 broilers chicks (mixed gender) with average body weight of 42.30±1.14 g (mean ± SD) were used in the experiment, which lasted for 35 days. Chicks were randomly allotted into one of four treatment diets, each one having 11 replicate cages with 18 birds each, being fed corn-soybean meal (SBM) based diets. Dietary treatments were CON (basal diet with unvaccinated birds); OA = CON + 0.1% organic acids; ACB = CON + anti-coccidium vaccine; OAACV = CON + 0.1% organic acid + anti-coccidium vaccine. Significant results were observed, with improved body weight (p=0.059; 0.064; 0.034) during days 1-7, 8-21, and overall, respectively. Significant effects were also observed on the feed conversion ratio (p=0.037) through the overall experiment, with no effects on feed intake on OAACV as compared to other treatment groups. Total track digestibility of dry matter (p=0.049) improved significantly in the OAACV treatment group. Additionally, beneficial effects were observed in the OAACV treatment group, with improvements in fecal microbial population (increased lactobacillus) and reduced NH3 gas emissions. Broilers fed the OAACV treatment tended to display reduced drip loss in the meat samples analyzed on days 5 and 7 (p=0.067, 0.072). In summary, our findings revealed that dietary inclusion of organic acid supplementation with anti-coccidium had a beneficial effect on broilers affected by coccidian infection, also improving growth performance, digestibility, fecal Lactobacillus counts, and reducing NH3.

Among the plant constituents of Clerodendrum colebrookianum Walp., acteoside, martinoside, and osmanthuside β6 interact with ROCK, a drug target for cancer. In this study, aglycone fragments of these plant constituents (caffeic acid, ferulic acid, and p-coumaric acid) along with the homopiperazine ring of fasudil (standard ROCK inhibitor) were used to design hybrid molecules. The designed molecules interact with the key hinge region residue Met156/Met157 of ROCK I/II in a stable manner according to our docking and molecular dynamics simulations. These compounds were synthesized and tested in vitro in SW480, MDA-MB-231, and A-549 cancer cell lines. The most promising compound was chemically optimized to obtain a thiourea analog, 6a (IC50 = 25 µM), which has >3-fold higher antiproliferative activity than fasudil (IC50 = 87 µM) in SW480 cells. Treatment with this molecule also inhibits the migration of colon cancer cells and induces cell apoptosis. Further, SPR experiments suggests that the binding affinity of 6a with ROCK I protein is better than that of fasudil. Hence, the drug-like natural product analog 6a constitutes a highly promising new anticancer lead. Communicated by Ramaswamy H. Sarma

Electrochemical impedance spectroscopy (EIS) has been applied to measure the H2S gas response of the sensor fabricated on reduced graphene oxide (rGO)-incorporated nano-zinc oxide (n-ZnO) composites. These nanocomposites were prepared by a facile one-step solution route at room temperature. The structural, surface morphological, and elemental analyses of the composite material have been investigated. EIS was carried out to study the H2S gas-sensing properties of fabricated sensors. The developed sensor showed an optimal H2S gas response to various concentrations ranging from 2 to 100 ppm at 90 °C. The H2S gas-sensing performances of pure n-ZnO and various concentrations of rGO-incorporated n-ZnO were evaluated. The H2S gas-sensing results showed that n-ZnO/rGO composites exhibited high response when compared to pure n-ZnO. The enhanced H2S response was speculated to be ascribed due to two factors. First, rGO creates reactive sites for H2S molecule adsorption. Second, rGO has great electrical conductivity compared to n-ZnO that enables the active transport of electrons from H2S gas on interaction with the sensing layer, resulting in enhanced gas response at 90 °C temperatures.

This paper attempts to prove that the Artificial Bee Colony algorithm can be used as an optimization algorithm in sparse-land setup to solve Video Summarization. The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time. By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting. A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary. The frames with higher threshold errors form the video summarization. In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm. We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS. The results are compared to the existing methods. The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries. This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization. Further studies are required to understand the performance evaluation scores as we change the threshold function.

Diabetes mellitus is a dreadful disease caused by the increase in hepatic glucose production and impaired insulin action. The usage of herbal based medicine has been increasing tremendously in both developing and developed countries over the last three decades. The present study aims to provide a comprehensive review of antidiabetic activity of following medicinal plants like Gymnema sylvestris, Rubia cardifolia, Bilberry, Green Tea, Salacia reticulate, Berberis aristata, Pterocarpus marsupium, Fenugreek, Ashwagandha, Bitter melon. The efficiency of these medicinal plants may regulate the diabetic metabolic abnormalities. This work would help researchers to choose potential herbal for diabetic treatment.

Novel reduced graphene oxide/barium niobate (RGOBN) nanocomposites were synthesized by hydrothermal method. The microstructure and morphology of graphene oxide, barium niobate and reduced graphene oxide/barium niobate was analysed by X-ray diffraction (XRD), FTIR, FTRaman, high resolution scanning electron microscopy and EDAX. XRD analysis showed that Barium Niobate samples are in perovskite phases, and the lattice parameters a, b and c almost decreased linearly with the increase of graphene nanosheets. The optical studies reveal the band gap of the nanocomposite to be 2.86 eV. Thermal studies show that RGOBN has high thermal stability than graphene. The remanant polarization and coercive electric field (0.0892 µC cm−2, −10.81 kV cm−1) of RGOBN nanocomposite superlattices calculated using P–E curve showed the strong hybrid interactions between graphene and barium niobate (BN) by decreasing leakage current density from 10−7 to 10−8 Acm−2. Graphene when incorporated into BN nanocubes increased the ferroelectric property almost two times than pure BN nanostructures. The squareness of polarization is also calculated for RGOBN and compared with that of BN.

The present study is proposed to investigate the effects of various lengths and different locations of the heater on the left sidewall in a square lid-driven porous cavity filled with nanofluid. A higher temperature is maintained on the left wall where three different lengths and three different locations of the heat source are considered for the analysis. The right wall is kept at a lower temperature while the top and bottom walls, and the remaining portions of the heated wall are adiabatic. The governing equations are solved by finite volume method. The results show that among the different lengths of the heat source, an enhancement in the heat transfer rate is observed only for the length LH = 1/3 of the heat source. In the case of location of the heat source, the overall heat transfer rate is increased when the heat source is located at the top of the hot wall. For Ri = 1 and 0.01, a better heat transfer rate is obtained when the heat source is placed at the top of the hot wall whereas for Ri = 100, it occurs when the heating portion is at the middle of the hot wall. As the solid volume fraction increases, the viscosity of the fluid is increased, which causes a reduction in the flow intensity. An addition of nanoparticles in the base fluid enhances the overall heat transfer rate significantly for all Da considered. The permeability of the porous medium plays a major role in convection of nanofluid than porosity. A high heat transfer rate (57.26%) is attained for Da = 10−1 and χ = 0.06.

The significance of water rationing has increased over the past few decades. It is hard to estimate level of water in tanks manually. When the water tank is full, the client may not realise the condition that the motor is turned ON which leads to water wastage. To tackle the issues related with the water tank, the water level pointer and regulator framework are utilized. The water level can likewise be tried utilizing a sensor, so the siphon consequently turns ON if the water goes down. The proposed system can even notify clients about the damages in the tank.

With 30 antennas and a maximum baseline length of 25 km, the Giant Metrewave Radio Telescope (GMRT) is the premier low-frequency radio interferometer today. We have carried out a study of possible expansions of the GMRT, via adding new antennas and installing focal plane arrays (FPAs), to improve its point-source sensitivity, surface brightness sensitivity, angular resolution, field of view, and U-V coverage. We have carried out array configuration studies, aimed at minimizing the number of new GMRT antennas required to obtain a well-behaved synthesized beam over a wide range of angular resolutions for full-synthesis observations. This was done via two approaches, tomographic projection and random sampling, to identify the optimal locations for the new antennas. We report results for the optimal locations of the antennas of an expanded array (the "EGMRT"), consisting of the existing 30 GMRT antennas, 30 new antennas at short distances, $\leq 2.5$ km from the array centre, and 26 new antennas at long distances, $\approx 5-25$ km from the array centre. The collecting area and the field of view of the proposed EGMRT array would be larger by factors of, respectively, $\approx 3$ and $\approx 30$, than those of the GMRT. Indeed, the EGMRT continuum sensitivity and survey speed with 550-850 MHz FPAs installed on the 45 antennas within a distance of $\approx 2.5$ km of the array centre would be far better than those of any existing interferometer, and comparable to the sensitivity and survey speed of Phase-1 of the Square Kilometre Array., 16 pages, 13 figures; accepted for publication in MNRAS

In present scenario, Glass Fiber Reinforced Plastics (GFRP) has found to be highly influential in the field of manufacture of automotive and aerospace parts due to its high strength and lightweight characteristics. Processed GFRP components are assembled to the main structure by means of mainly rivets and fasteners. So, drilling in a GFRP material becomes inevitable in manufacturing for its wide applications. The purpose of this experiment is to conduct a study of the effect of vibration on surface roughness while drilling a GFRP plate using a 10mm HSS drill. The experiment has been implemented by varying the drill speed and feed accordingly in a 10mm thick GFRP plate and the respective vibration and surface roughness results were obtained. Furthermore, the results obtained have been processed in Minitab to provide a comprehensive understanding. ANOVA has been performed to establish a relation between the vibrations developed and surface roughness of the hole.

To improve the meat quality of Japanese quail ( Coturnix coturnix japonica ), a new strain was developed by Tamilnadu Veterinary and Animal Sciences University named “Namakkal quail-1”. The purpose of the study is to know the sensory quality of the Namakkal quail-1. 24 Birds including 12 males and 12 females of 4 th and 6 th week birds were procured from Poultry farm Complex of Department of Poultry Science, Veterinary College and Research Institute, Namakkal and slaughtered as per the standard slaughter procedures after 4 hours of resting interval at the Department of Livestock Products Technology (Meat Science), Veterinary College and Research Institute, Namakkal. The sensory properties like colour, flavour, juiciness, tenderness and overall acceptability were estimated. Analysis of variance showed that age and sexes of the birds no significant effect on colour, flavour, juiciness, tenderness and overall acceptability of meat. But Namakkal quail-1 meat score higher than Japanese quail, Chicken, Duck and Geese meat in 9 point hedonic scale. It is concluded that Namakkal quail-1 meat is tastier than other poultry meat.

Gadolinium-doped ZnSe (Gd:ZnSe) quantum dots (Qds) were synthesized using ethylene glycol as a stabilizing agent. The fluorescence spectrum exhibited an emission peak at 435 nm for ZnSe, whereas the Gd-doped ZnSe exhibited a green emission at 547 nm. In vitro imaging studies of MCF-7 cancer cells were carried out using fluorescent probes constructed using Gd-ZnSe folate.

Triorganotin (IV) complexes 1–3 were synthesized by the reaction of azo-carboxylic acid viz. 2/4-(2-hydroxynaphthylazo)benzoic acids with appropriate triorganotin(IV) chlorides [R = Me (compounds 1 and 2) and Bu (compound 3)] in presence of triethyl amine base. The complexes were characterized by elemental analysis, IR and multinuclear (1H and 13C- and 119Sn)-NMR spectroscopy. The structures and mode of coordination around tin ions in the complexes were determined by single crystal X-ray crystallography. The complexes exhibit trigonal bipyramidal geometry around tin atoms where the base of the equatorial plane is being occupied by the three alkyl groups [Me or Bu] while the axial positions are occupied by carboxylate oxygen atoms in 2 and in case of 1 and 3, by carboxylate and phenoxide oxygen atoms, respectively. It has been found that, the crystal structure of 1 or 3 is a cyclic dimeric, whereas 2 exhibits a polymeric structure. The 119Sn NMR studies show that all complexes have four-coordinate structures indicating dissociation of these complexes in solution state. The complexes were also screened for their antimicrobial activity and the compound 3 was found to exhibit effective antimicrobial activity.

Maize is the third important major food crop. Breeding for low phytate maize genotypes is an effective strategy for decreasing the content of kernel phytic acid (a chelator of cations such as Ca(2+) and Fe(3+) ) and thereby increasing the bioavailability of nutritive minerals in human diet and animal feed. Previous studies have established that a mutant plant with a lpa2-2 allele accumulates less phytic acid in seeds. Therefore, the marker assisted backcross breeding (MABB), which involves introgression of lpa2-2 recessive allele (which confer low phytate trait) from a lpa2-2 mutant line into a well-adapted line using backcrosses and selection of lines possessing lpa2-2 allele in each backcross population using molecular markers, is an effective strategy for developing low phytate maize. So far, no studies have developed any lpa2-2 allele specific molecular markers for this purpose. Here, using backcross and selfed progenies, obtained by crossing low phytate mutant line 'EC 659418' (i.e. donor of lpa2-2 allele) into agronomically superior line 'UMI395', we have validated that a SSR marker 'umc2230', located 0.4 cM downstream of lpa2-2, cosegregate, in a Mendelian fashion, with low phytic acid trait. Therefore umc2230 can be dependably used in MABB for the development of low phytate maize.