Effect of poling on piezocatalytic removal of muti-pollutants using BaTiO3

The BaTiO₃ powder was prepared via a solid-state reaction route. It was studied for the degradation of bacterial cells, dye, and pharmaceuticals waste using ultrasonically driven piezocatalytic effect. The bacterial catalytic behavior of poled BaTiO₃ was remarkably increased during ultrasonication (10% E coli survival in 60 minutes). The structural damages were illustrated using scanning electron micrographs of bacterial cells which demonstrated morphological manifestations under different conditions. Methylene blue (MB dye), ciprofloxacin and diclofenac were also cleaned using the piezocatalytic effect associated with the poled BaTiO₃ powder. Around 92, 85, and 78% of degradations were observed within 150 minutes duration for methylene blue, ciprofloxacin, and diclofenac, respectively.     

Pyramid coding based rate control for constant bit rate video streaming

In this paper, a novel pyramid coding based rate control scheme is proposed for video streaming applications constrained by a constant channel bandwidth. To achieve the target bit rate with the best quality, the initial quantization parameter (QP) is determined by the average spatio-temporal complexity of the sequence, its resolution and the target bit rate. Simple linear estimation models are then used to predict the number of bits that would be necessary to encode a frame for a given complexity and QP. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control scheme in the Joint Model (JM) reference software in terms of Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Finally, the proposed scheme is validated through experimental evaluation over a miniature test-bed.

     

Language independent unsupervised learning of short message service dialect

Noise in textual data such as those introduced by multilinguality, misspellings, abbreviations, deletions, phonetic spellings, non-standard transliteration, etc. pose considerable problems for text-mining. Such corruptions are very common in instant messenger and short message service data and they adversely affect off-the-shelf text mining methods. Most techniques address this problem by supervised methods by making use of hand labeled corrections. But they require human generated labels and corrections that are very expensive and time consuming to obtain because of multilinguality and complexity of the corruptions. While we do not champion unsupervised methods over supervised when quality of results is the singular concern, we demonstrate that unsupervised methods can provide cost effective results without the need for expensive human intervention that is necessary to generate a parallel labeled corpora. A generative model based unsupervised technique is presented that maps non-standard words to their corresponding conventional frequent form. A hidden Markov model (HMM) over a “subsequencized” representation of words is used, where a word is represented as a bag of weighted subsequences. The approximate maximum likelihood inference algorithm used is such that the training phase involves clustering over vectors and not the customary and expensive dynamic programming (Baum–Welch algorithm) over sequences that is necessary for HMMs. A principled transformation of maximum likelihood based “central clustering” cost function of Baum–Welch into a “pairwise similarity” based clustering is proposed. This transformation makes it possible to apply “subsequence kernel” based methods that model delete and insert corruptions well. The novelty of this approach lies in that the expensive (Baum–Welch) iterations required for HMM, can be avoided through an approximation of the loglikelihood function and by establishing a connection between the loglikelihood and a pairwise distance. Anecdotal evidence of efficacy is provided on public and proprietary data.     

Axonal regeneration and physiological activity following transection and immunological disruption of myelin within the hatchling chick spinal cord

Transections of the chicken spinal cord after the developmental onset of myelination at embryonic day (E) 13 results in little or no functional regeneration. However, intraspinal injection of serum complement proteins with complement-binding GalC or 04 antibodies between E9-E12 results in a delay of the onset of myelination until E17. A subsequent transection of the spinal cord as late as E15 (i.e., during the normal restrictive period for repair) results in neuroanatomical regeneration and functional recovery. Utilizing a similar immunological protocol, we evoked a transient alteration of myelin structure in the posthatching (P) chicken spinal cord, characterized by widespread "unravelling" of myelin sheaths and a loss of MBP immunoreactivity (myelin disruption). Myelin repair began within 7 d of cessation of the myelin disruption protocol. Long term disruption of thoracic spinal cord myelin was initiated after a P2-P10 thoracic transection and maintained for > 14 d by intra-spinal infusion of serum complement proteins plus complement-binding GalC or 04 antibodies. Fourteen to 28 d later, retrograde tract tracing experiments, including double-labeling protocols, indicated that approximately 6-19% of the brainstem-spinal projections had regenerated across the transection site to lumbar levels. Even though voluntary locomotion was not observed after recovery, focal electrical stimulation of identified brainstem locomotor regions evoked peripheral nerve activity in paralyzed preparations, as well as leg muscle activity patterns typical of stepping in unparalyzed animals. This indicated that a transient alteration of myelin structure in the injured adult avian spinal cord facilitated brainstem-spinal axonal regrowth resulting in functional synaptogenesis with target neurons.     

Creep and shrinkage prediction model for analysis and design of concrete structures— model B3

RILEM Draft Recommendation107-GCS Guidelines for the Formulation of Creep and Shrinkage Prediction Models

Creep and shrinkage prediction model for analysis and design of concrete structures— model B₃     

Intrusion Detection System in Wireless Sensor Network Using Conditional Generative Adversarial Network

Wireless communication networks have much data to sense, process, and transmit. It tends to develop a security mechanism to care for these needs for such modern-day systems. An intrusion detection system (IDS) is a solution that has recently gained the researcher’s attention with the application of deep learning techniques in IDS. In this paper, we propose an IDS model that uses a deep learning algorithm, conditional generative adversarial network (CGAN), enabling unsupervised learning in the model and adding an eXtreme gradient boosting (XGBoost) classifier for faster comparison and visualization of results. The proposed method can reduce the need to deploy extra sensors to generate fake data to fool the intruder 1.2–2.6%, as the proposed system generates this fake data. The parameters were selected to give optimal results to our model without significant alterations and complications. The model learns from its dataset samples with the multiple-layer network for a refined training process. We aimed that the proposed model could improve the accuracy and thus, decrease the false detection rate and obtain good precision in the cases of both the datasets, NSL-KDD and the CICIDS2017, which can be used as a detector for cyber intrusions. The false alarm rate of the proposed model decreases by about 1.827%.

     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic

Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm³/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849.     

Effect of process parameters and optimization for photochemical machining of brass and german silver

This article focuses on parametric optimization for photochemical machining (PCM) of brass and german silver. The aim of the study is to analyze the effect of control parameters on response measures, that is, surface roughness, material removal rate, and edge deviation and optimization of parameters considering different weight percentage for each performance measure. The control parameters have been selected as etchant concentration, etching temperature, and etching time. Using full factorial method of design of experiments, PCM has been carried out using ferric chloride as etchant. Surface roughness and edge deviation should be less, while material removal rate is desired high. For satisfying this multi-objective condition, overall evaluation criteria (OEC) have been formulated by assigning different and equal weight percentage to response measures. The optimized condition for particular OEC is obtained, and analysis of variance (ANOVA) has been performed for observing effect of control parameters on response measures. Surface topography study has been performed using scanning electron microscopy, and material composition analysis has been carried out using energy dispersive spectroscopy. The surface roughness is observed lower for brass, while the edge deviation is found lesser for german silver. The material removal rate is observed higher for brass compared to german silver.