Bispidine as a Versatile Scaffold: From Topological Hosts to Transmembrane Transporters

The development of designer topological structures is a synthetically challenging endeavor. We present herein bispidine as a platform for the design of molecules with various topologies and functions. The bispidine-based acyclic molecule, which shows intriguing S-shape topology, is discussed. Single-crystal X-ray diffraction studies revealed that this molecule exists in the solid state as two conformational enantiomers. In addition, bispidine-based designer macrocycles were synthesized and investigated for ionophoric properties. Patch clamp experiments revealed that these macrocycles transport both anions and cations non-specifically with at least tenfold higher chloride conductance over the cations under the given experimental conditions. Ultramicroscopy and single-crystal X-ray crystallographic studies indicated that the self-assembling macrocycle forms a tubular assembly. Our design highlights the use of unconventional dihydrogen interactions in nanotube fabrication.     

EEG based personality prediction using genetic programming

The reliable correlation between personality and brain signal ensures that inferences from cognitive processes can be achieved. This research aims primarily to predict one's personality using brain signals. On grounds of Psychology, the inference of personality in this work is performed on the basis of the Myers–Briggs Type Indicator (MBTI) personality inventory. Personality consists of different types of thinking, feeling and behavior patterns. EEG signals are produced when a person is exposed to situations or scenarios via visual information and experiences various emotions or sentiments. In this study, by evaluating brain waves while a person watches personality traits elicitation materials, the identification of the personality traits of an individual is done. The elicitation materials used for the collection of the dataset comprise approximately 50 videos with the pre-defined personality of the dramatic personae and therefore, it is considered to be the ground truth for the experimental procedure of this work. For creating a dataset, sixty participants contributed and gave brain signals. The GP model with the proposed BSH crossover, known as the BSHGP model, is implemented. The maximum performance of the BSHGP model for a 10-fold partition scheme is 84.34%.     

Isolated word language identification system with hybrid features from a deep belief network

The representation of good audio features is the first and foremost requirement for improving the identification performance of any system. Most of the representation learning approaches are based on connectionist systems to learn and extract latent features from the speech data. This research work presents a hybrid feature extraction approach to integrate Mel-Frequency Cepstral Coefficients (MFCC) features with Shifted Delta Cepstral (SDC) coefficients features, which are further stacked to Deep Belief Network (DBN), for yielding new feature representations of the speech signals. DBN is utilized for unsupervised feature learning on the extracted MFCC-SDC acoustic features. A 3-layer Back Propagation Neural Network (BPNN) classifier is initialized in terms of the learning outcomes of hidden layers of DBN for identifying language from the uttered speech. The efficiency of the proposed approach is evaluated by simulating several experimental algorithms on the user-defined database of isolated words in four languages, namely, Tamil, Malayalam, Hindi, and English, in the working platform of MATLAB. The obtained results for the proposed hybrid approach MFCC-SDC-DBN are promising. The proposed approach is also compared with the baseline feature extraction approach MFCC-SDC by utilizing traditional acoustic features and BPNN classifier. The accuracy obtained with our proposed approach is 98.1% whereas that of the baseline approach is 82%, thereby providing an overall improvement of 16.1%.     

Superior Peroxidase-Like Activity of Gold Nanorattles in Ultrasensitive H2O2 Sensing and Antioxidant Screening

Nanozymes are artificial enzyme systems which are easy to produce, highly stable and cost-effective in comparison to natural enzymes. Herein, we evaluated the peroxidase-like activity of gold nanorattles (Au NRTs) having a solid gold octahedron core and thin, porous cubic gold shell. We also prepared solid gold nanocubes and nanospheres of similar sizes and surface charge as that of Au NRTs and compared their activity with standard horse radish peroxidase (HRP). All the prepared nanostructures followed Michaelis-Menten kinetics as observed from their substrate concentration vs. initial reaction velocity plot using 3,3’,5,5’-tetramethylbenzidine (TMB) as a substrate. The kinetic parameters and the catalytic efficiency for the peroxidase-like activity of the nanostructures and HRP were calculated, and it was observed that Au NRTs possess the best nanozymatic activity with lowest K_M and highest catalytic efficiency (k_cat/K_M). The better activity of Au NRTs compared with other nanostructures and HRP could be attributed to the hollow porous structure with a solid core where different surfaces are available for reaction. Au NRTs, being the best amongst the tested nanozymes were further used for the sensing of hydrogen peroxide (H₂O₂) and were found to sense H₂O₂ down to 0.5 μM. Further, two naturally occurring antioxidants, tannic acid and ascorbic acid showed inhibitory effect on the peroxidase-like activity of Au NRTs in a concentration dependent manner which can be further used for screening of antioxidants or for determining the antioxidant potential.     

Performance and Analysis of Stack Junctionless Tunnel Field Effect Transistor

Silicon - To overcome the fabrication complexity and achieve a better switching ratio is a major grave concern for applications in semiconductor devices. In this regards, a novel stack gate-oxide...