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In this article, a domain adaptation (DA) technique using artificial neural networks based classifiers has been proposed using two-level cluster mapping technique by integrating the common data transformation and transfer learning approaches in a single framework. Here, after applying self-organizing feature mapping based clustering technique, a semi-automatic threshold selection mechanism is used to separate out most-confidently paired source–target clusters (i.e. the most similar ones) and alien target clusters (i.e. non-similar ones). Moreover, this strategy makes the proposed technique eligible to apply the transfer learning mechanism. Thereafter, the samples from the most confidently paired target clusters are transformed in terms of the corresponding source clusters using an auto-encoder. Here, the labelled samples are collected from the corresponding source clusters for the paired target clusters; whereas the transfer learning technique is used to select labelled samples from the alien target clusters. To assess the effectiveness of the proposed DA approach, experiments are conducted on the three source–target datasets and the results are compared with other state-of-the-art techniques. Results are also found to be encouraging for the proposed technique.

Wireless Body Area Networks enable a new trend of proactive health care exploiting battery-powered wearable and/or implantable sensor nodes. The transmission of biomedical signals consumes most of the scarce energy resources in a WBAN. State-of-the-art transmission mechanisms either focus on energy efficiency or timely data delivery. However, an optimal balance between the two subject to various ambient conditions is a necessity. Accordingly, in this paper, an optimal data transmission policy is formulated at the node level, considering node lifetime, node heating, and reliable data delivery for various ambient conditions. The proposed model is divided into two phases. In the pre-deployment phase, each node is stochastically modeled following the Markov Decision Process. The formulation involves a reward function that attempts a trade-off between energy consumption and reliable data delivery. Then, a genetic algorithm is applied to obtain a set of optimal transmission decisions for each node based on the reward function for a range of ambient conditions. Finally, in the deployment phase, the results of the previous phase are mapped to an algorithm to be executed at WBAN nodes. The work is experimentally evaluated via simulation and compared with the state-of-the-art approaches. The performance is also observed based on the real-life dataset. Results demonstrate that around 85–90% of data packets could be delivered to the sink with minimal energy so that the nodes hardly get heated.

The present study aimed to demonstrate the utilization of unpurified industrial CO2 with low impurities for acetate production via microbial electrosynthesis (MES) for the first time. In MES experiments with CO2-rich brewery gas, the enriched mixed culture dominated by Acetobacterium produced 1.8 ± 0.2 g/L acetic acid at 0.26 ± 0.03 g/Lcatholyte/d rate and outperformed a pure culture of Clostridium ljungdahlii (1.1 ± 0.02 g/L; 0.138 ± 0.004 g/Lcatholyte/d). The electron recovery in acetic acid was also more for mixed culture (84 ± 13%) than C. ljungdahlii (42 ± 14%). Electrochemical analysis of biocathodes suggested the role of microbial biofilm in improved hydrogen electrocatalysis. In comparative gas fermentation tests, the mixed culture outperformed C. ljungdahlii and produced acetic acid at a similar level with both industrial and pure CO2 feedstocks. These results suggest the robustness and capability of the mixed microbial community for utilizing slightly impure industrial CO2 for bioproduction and presents a major advancement in MES technology.

Cancer has remained one of the most indomitable conundrums for scientists over centuries due to its multifarious etiology. While improved therapeutic and diagnostic approaches have commendably augmented the rate of survival of cancer patients, a holistic riddance from the ailment is still implausible. Hence, further explorations to scout for novel strategies of cancer therapy and diagnosis are necessary. Theranostics (amalgamation of therapy and diagnostics) has emerged as one of the avant-garde strategies, which provides a two-pronged advantage in cancer management. This integrative approach has found immense relevance in light of nanotechnology. Nanoparticles can be customized (loaded with a mélange of therapeutic drugs and diagnostic probes) to develop theranostic properties, thereby constructing nanotheranostic agents. These nano-composites are lucrative tools for cancer cell obliteration and simultaneous monitoring of the drug action, and can also be tailored for targeted drug delivery. Nanotheranostic agents have emerged as a prudent ploy for synchronized cancer intervention and detection of the 'route and reach' of the drugs. In this review, we discuss the diversified state-of-the-art facets of theranostic nanoparticles, including various nanoparticle-based platforms as well as the plethora of reported therapeutic drugs, aptamers, markers and diagnostic molecules that have found use in the precincts of nanotheranostics.

In this article, a novel approach using ensemble of semi-supervised classifiers is proposed for change detection in remotely sensed images. Unlike the other traditional methodologies for detection of changes in land-cover, the present work uses a multiple classifier system in semi-supervised (leaning) framework instead of using a single weak classifier. Iterative learning of base classifiers is continued using the selected unlabeled patterns along with a few labeled patterns. Ensemble agreement is utilized for choosing the unlabeled patterns for the next training step. Finally, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of base classifiers using some combination rule. For the present investigation, multilayer perceptron (MLP), elliptical basis function neural network (EBFNN) and fuzzy k-nearest neighbor (k-nn) techniques are used as base classifiers. Experiments are carried out on multi-temporal and multi-spectral images and the results are compared with the change detection techniques using MLP, EBFNN, fuzzy k-nn, unsupervised modified self-organizing feature map and semi-supervised MLP. Results show that the proposed work has an edge over the other state-of-the-art techniques for change detection.

In the present article, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In the proposed methodology, training of the MSOFM network is initially performed using only a few labeled patterns. Thereafter, the membership values, in both the classes, for each unlabeled pattern are determined using the concept of fuzzy set theory. The soft class label for each of the unlabeled patterns is then estimated using the membership values of its K nearest neighbors. Here, training of the network using the unlabeled patterns along with a few labeled patterns is carried out iteratively. A heuristic method has been suggested to select some patterns from the unlabeled ones for training. To check the effectiveness of the proposed methodology, experiments are conducted on three multi-temporal and multi-spectral data sets. Performance of the proposed work is compared with that of two unsupervised techniques, a supervised technique and two semi-supervised techniques. Results are also statistically validated using paired t-test. The proposed method produced promising results.

Chloromethylated polystyrene supported ionic liquid is used as catalyst in the aldol-type coupling of a variety of aldehydes with ethyl diazoacetate in water. Moderate to excellent yields of the corresponding α-diazo-β-hydroxy esters were obtained. The catalyst was separated by filtration and reused in five consecutive cycles without any appreciable loss of activity.

Polyaniline supported CuI catalyst was prepared, characterized and effectively used in the N -arylation of N (H)-heterocycles and benzylamines with aryl halides and arylboronic acids and in aza-Michael reaction of amines with activated alkenes. The catalyst was recovered by simple filtration and reused for several cycles with consistent activity.

Glycation-modified hemoglobin in diabetes mellitus has been suggested to be a source of enhanced catalytic iron and free radicals causing pathological complications. The present study aims to verify this idea in experimental diabetes. Pelargonidin, an anthocyanidin, has been tested for its antidiabetic potential with emphasis on its role against pathological oxidative stress including hemoglobin-mediated free radical reactions. Male wistar rats were grouped as normal control, streptozotocin-induced diabetic control, normal treated with pelargonidin and diabetic treated with pelargonidin. Pelargonidin-treated rats received one time i.p injection of the flavonoid (3 mg/kg bodyweight). Biochemical parameters were assayed in blood samples of different groups of rats. Liver was used for histological examinations. Pelargonidin treatment normalized elevated blood glucose levels and improved serum insulin levels in diabetic rats. Glucose tolerance test appeared normal after treatment. Decreased serum levels of SOD and catalase, and increased levels of malondialdehyde and fructosamine in diabetic rats were reverted to their respective normal values after pelargonidin administration. Extents of hemoglobin glycation, hemoglobin-mediated iron release, iron-mediated free radical reactions and carbonyl formation in hemoglobin were pronounced in diabetic rats, indicating association between hemoglobin glycation and oxidative stress in diabetes. Pelargonidin counteracts hemoglobin glycation, iron release from the heme protein and iron-mediated oxidative damages, confirming glycated hemoglobin-associated oxidative stress in diabetes.

Layered double hydroxides supported rhodium(0) catalyst was prepared, characterized and effectively used as a catalyst in the Heck, Suzuki and Stille cross-coupling of haloarenes. The catalyst can easily be removed by simple filtration and reused for several cycles with consistent activity.

Polyaniline supported palladium catalysts were prepared from different palladium precursors and all the catalysts are well characterized using ICP-AES, FTIR, TGA–DTA, SEM-EDX and XPS analysis. All the catalysts were tested for Suzuki–Miyaura coupling of bromo- and chloroarenes in water and the catalyst prepared from PdCl2 precursor was found to be the most effective catalyst. The catalyst can easily be recovered by simple filtration and reused for several cycles with almost consistent activity.

Silica tethered copper complexes are effectively used as reusable catalysts in the N-arylation of N(H)-heterocycles and benzyl amines with aryl halides and arylboronic acids. N-Arylated products were isolated in good to excellent yields, demonstrating the versatility of the protocol. Moreover the Cat2 was easily recovered by simple filtration and reused for several cycles with consistent activity.

Polyaniline supported indium(III) trichloride (PANI-In) catalyst was prepared and thoroughly characterized by using FTIR, XPS, scanning electron microscopy-energy dispersive X-ray analysis (SEM-EDX) and ICP-AES. Thus, the synthesized catalyst was efficiently used in the aza-Michael reaction of amines with electron-deficient olefins and aza-Diels-Alder reaction of cyclic enol ethers with aromatic amines in water. Moreover, the catalyst was easily recovered by simple filtration and reused for several cycles with consistent activity.

Layered double hydroxides (LDH) supported nanoplatinum catalyst was prepared and employed for Suzuki cross coupling of aryl halides (iodides and bromides) with a broad range of arylboronic to afford the corresponding biaryls in good to excellent yields without using any external ligand. The catalyst is reused for several cycles with consistent activity.