Emulsion polymer derived nanocomposite: a review on design and tailored attributes

ABSTRACT

Recently, fabrication of polymeric nanocomposite via emulsion polymerization process has become a research spotlight. This review highlights the capability of emulsion polymerization technique to produce polymeric nanocomposite with tailored nanostructures, advanced properties, and high performance. Initially, overview on emulsion polymerization is presented. Afterward, advances in nanocomposites through the use of nanoparticles in emulsion polymerization have been elaborated. Main focus is given on the influence of graphene, carbon nanotube, carbon black, nanoclay, and inorganic nanoparticles on the essential features of emulsion polymerized nanocomposite. These materials have wide-ranging applications in coatings, adhesives, foaming/anti-foaming agents, packaging, paper/textiles, construction, and drug delivery fields.     

Optimization of design and operating parameters in a pilot scale Jameson cell for slime coal cleaning

The Jameson flotation cell has been commonly used to treat a variety of ores (lead, zinc, copper etc.), coal and industrial minerals at commercial scale since 1989. It is especially known to be highly efficient at fine and ultrafine coal recovery. However, although the Jameson cell has quite a simple structure, it may be largely inefficient if the design and operating parameters chosen are not appropriate. In this study, the design and operating parameters of a pilot scale Jameson cell were optimized to obtain a desired metallurgical performance in the slime coal flotation. The optimized design parameters are the nozzle type, the height of the nozzle above the pulp level, the downcomer diameter and the immersion depth of the downcomer. Among the operating parameters optimized are the collector dosage, the frother dosage, the percentage of solids and the froth height. In the optimum conditions, a clean coal with an ash content of 14.90% was obtained from the sample slime having 45.30% ash with a combustible recovery of 74.20%. In addition, a new type nozzle was developed for the Jameson cell, which led to an increase of about 9% in the combustible recovery value.     

Accuracy improving guidelines for network anomaly detection systems

An unprecedented growth in computer and communication systems in the last two decades has resulted in a proportional increase in the number and sophistication of network attacks. In particular, the number of previously-unseen attacks has increased exponentially in the last few years. Due to the rapidly evolving nature of network attacks, a considerable paradigm shift has taken place in the intrusion detection community. The main focus is now on Network Anomaly Detection Systems (NADSs) which model and flag deviations from normal/benign behavior of a network and can hence detect previously-unseen attacks. Contemporary NADS borrow concepts from a variety of theoretical fields (e.g., Information theory, stochastic and machine learning, signal processing, etc.) to model benign behavior. These NADSs, however, fall short of achieving acceptable performance levels as therefore widespread commercial deployments. Thus, in this paper, we firstly evaluate the performance of eight prominent network-based anomaly detectors under malicious portscan attacks to identify which NADSs perform better than others and why. These NADSs are evaluated on three criteria: accuracy (ROC curves), scalability (with respect to varying normal and attack traffic rates, and deployment points) and detection delay. These criteria are evaluated using two independently collected datasets with complementary strengths. We then propose novel methods and promising guidelines to improve the accuracy and scalability of existing and future anomaly detectors. Experimental analysis of the proposed guidelines is also presented for the proof of concept.     

Predicting Addiction Severity Index (ASI) interviewer severity ratings for a computer-administered ASI.

The Addiction Severity Index (ASI) is a reliable and valid measure of problem severity among addicted patients. Concerns have been raised about the reliability of the Interviewer Severity Rating (ISR), a summary score for each of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained interviewers, permitted derivation of stable regression equations predicting the ISR for each ASI domain from patients' answers to preselected interview items. The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, standardized vignettes, with "gold standard," expert-generated ISRs. Reliabilities compared well with those of intensively trained interviewers. The PSRs could provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and make possible a computer-administered version of the ASI. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Enhanced electrical and thermal conductivity of modified poly(acrylonitrile-co-butadiene)-based nanofluid containing functional carbon black-graphene oxide

Solution refluxing and high-pressure homogenization technique were reported for synthesizing nanofluids based on modified poly(acrylonitrile-co-butadiene) (M-PANB) as base fluid and carbon black (CB)/carbon black-graphene oxide (CB-GO) as filler. The physiochemical properties were studied to analyze the structure, morphology, thermal and electrical conductivity. FTIR analysis corroborated the structure of CB-GO nanobifiller and nanocomposite. Microstructure analysis of M-PANB/CB-GO revealed good dispersion of CB-GO nanosheets, while CB series showed granular distribution. XRD studies confirmed amorphous structure of M-PANB/CB-GO nanocomposite. Thermal conductivity of nanofluid was found to increase upto 1.41 W/mK for 10 wt.% CB-GO loading and electrical conductivity was increased to 2.5 × 10^?3 Scm^?1.     

Glycine propionyl-L-carnitine produces enhanced anaerobic work capacity with reduced lactate accumulation in resistance trained males

Background  

Recent research has indicated that short term administration of glycine propionyl-L-carnitine (GPLC) significantly elevates levels of nitric oxide metabolites at rest and in response to reactive hyperaemia. However, no scientific evidence exists that suggests such supplementation enhances exercise performance in healthy, trained individuals. The purpose of this study was to examine the effects of GPLC on the performance of repeated high intensity stationary cycle sprints with limited recovery periods in resistance trained male subjects.     

A Study on Poly(vinyl alcohol-co-ethylene)-graft-Polystyrene Reinforced with Two Functional Nanofillers

A facile route was adopted to graft polystyrene on poly(vinyl alcohol-co-ethylene) matrix. Poly(vinyl alcohol-co-ethylene)-graft-polystyrene (PVAE-g-PS) was then reinforced with two types of nanofillers, i.e., graphene oxide (GO) and nanodiamond functional graphene oxide (GO-ND). PVAE-g-PS/GO and PVAE-g-PS/GO-ND nanocomposite series reinforced with 0.1—5 wt.% nanofiller were fabricated by solution processing. Structure of nanofillers and composite was confirmed by FTIR. FESEM imaging revealed that nanodiamond functional GO platelets were fully incorporated into matrix. TGA demonstrated enhanced stability of PVAE-g-PS/GO-ND nanomposites containing GO-ND. Similarly, UL 94 and electrical conductivity measurement of GO-ND-based system were found to be superior compared to one of copolymer/GO.     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

Suppression of TRPV1/TRPM8/P2Y Nociceptors by Withametelin via Downregulating MAPK Signaling in Mouse Model of Vincristine-Induced Neuropathic Pain

Vincristine (VCR) is a widely used chemotherapy drug that induced peripheral painful neuropathy. Yet, it still lacks an ideal therapeutic strategy. The transient receptor potential (TRP) channels, purinergic receptor (P2Y), and mitogen-activated protein kinase (MAPK) signaling play a crucial role in the pathogenesis of neuropathic pain. Withametelin (WMT), a potential Phytosteroid isolated from datura innoxa, exhibits remarkable neuroprotective properties. The present investigation was designed to explore the effect of withametelin on VCR-induced neuropathic pain and its underlying molecular mechanism. Initially, the neuroprotective potential of WMT was confirmed against hydrogen peroxide (H₂O₂)-induced PC12 cells. To develop potential candidates for neuropathic pain treatment, a VCR-induced neuropathic pain model was established. Vincristine (75 μg/kg) was administered intraperitoneally (i.p.) for 10 consecutive days (day 1–10) for the induction of neuropathic pain. Gabapentin (GBP) (60 mg/kg, i.p.) and withametelin (0.1 and 1 mg/kg i.p.) treatments were given after the completion of VCR injection on the 11th day up to 21 days. The results revealed that WMT significantly reduced VCR-induced pain hypersensitivity, including mechanical allodynia, cold allodynia, and thermal hyperalgesia. It reversed the VCR-induced histopathological changes in the brain, spinal cord, and sciatic nerve. It inhibited VCR-induced changes in the biochemical composition of the myelin sheath of the sciatic nerve. It markedly downregulated the expression levels of TRPV1 (transient receptor potential vanilloid 1); TRPM8 (Transient receptor potential melastatin 8); and P2Y nociceptors and MAPKs signaling, including ERK (Extracellular Signal-Regulated Kinase), JNK (c-Jun N-terminal kinase), and p-38 in the spinal cord. It suppressed apoptosis by regulating Bax (Bcl2-associated X-protein), Bcl-2 (B-cell-lymphoma-2), and Caspase-3 expression. It considerably attenuated inflammatory cytokines, oxidative stress, and genotoxicity. This study suggests that WMT treatment suppressed vincristine-induced neuropathic pain by targeting the TRPV1/TRPM8/P2Y nociceptors and MAPK signaling.     

Evaluating the performance of a discrete manufacturing process using RFID: A case study

The material flow is a major focus point in improving productivity in today’s product diversified manufacturing organizations. Nevertheless, there is still a lack of efficient methods to control material flow through manufacturing processes in cases where multi-item tracking is difficult to achieve. This paper presents an RFID-based RTLS (Real-Time Location System) solution for obtaining multi-item work-in-process visibility within a manufacturer. It delivers detailed performance metrics through RTLS data analysis in order to evaluate workflow performance and to obtain a lean process. We pre-filter the RTLS data through the development of a middleware data collection method to acquire near real-time performance evaluation. A case study illustrates the complete process including measurements before and after a workflow redesign. The increased level of detail from RFID measurements yields new insights into shop floor actions and the real effects of redesign efforts.