Quaternized Diaminobutane/Poly(vinyl alcohol) Cross-Linked Membranes for Acid Recovery via Diffusion Dialysis.

Diffusion dialysis (DD) using anion exchange membranes (AEM) is an effective process for acid recovery and requires the preparation of suitable materials for AEMs, characterized by unique ions transport properties. In this work, novel AEMs composed of quaternized diaminobutane (QDAB) and poly(vinyl alcohol) (PVA) were cross-linked by tetraethoxysilane (TEOS) via the sol-gel process. The prepared AEMs were systematically characterized by Fourier-transform infrared (FTIR) spectroscopy, ion-exchange capacity (IEC) analysis, thermo gravimetric analysis (TGA), water uptake, linear expansion ratio (LER), and mechanical strength determination, scanning electron microscopy (SEM), and DD performance analysis for acid recovery using a hydrochloric acid/iron chloride (HCl/FeCl2) aqueous mixture and varying the QDAB content. The prepared AEMs exhibited IEC values between 0.86 and 1.46 mmol/g, water uptake values within 71.3 and 47.8%, moderate thermal stability, tensile strength values in the range of 26.1 to 41.7 MPa, and elongation from 68.2 to 204.6%. The dialysis coefficient values were between 0.0186 and 0.0295 m/h, whereas the separation factors range was 24.7-44.1 at 25 °C. The prepared membranes have great potential for acid recovery via diffusion dialysis.

Fabrication and dialysis performance of functionalized multiwall carbon nanotubes integrated cellulose acetate/poly(vinylpyrrolidone) membranes.

Mixed matrix membranes (MMMs) of cellulose acetate/poly(vinylpyrrolidone) (CA/PVP) infused with acid functionalized multiwall carbon nanotubes (f-MWCNTs) were fabricated by an immersion phase separation technique for hemodialysis application. Membranes were characterized using FTIR, water uptake, contact angle, TGA, DMA and SEM analysis. The FTIR was used to confirm the bonding interaction between CA/PVP membrane matrix and f-MWCNTs. Upon addition of f-MWCNTs, TGA thermograms and glass transition temperature indicated improved thermal stability of MMMs. The surface morphological analysis demonstrated revealed uniform distribution of f-MWCNTs and asymmetric membrane structure. The water uptake and contact angle confirmed that hydrophilicity was increased after incorporation of f-MWCNTs. The membranes demonstrated enhancement in water permeate flux, bovine serum albumin (BSA) rejection with the infusion of f-MWCNTs; whereas BSA based anti-fouling analysis using flux recovery ratio test shown up to 8.4% improvement. The urea and creatinine clearance performance of MMMs were evaluated by dialysis experiment. It has been found that f-MWCNTs integrated membranes demonstrated the higher urea and creatinine clearance with increase of 12.6% and 10.5% in comparison to the neat CA/PVP membrane. Thus, the prepared CA/PVP membranes embedded with f-MWCNTs can be employed for wide range of dialysis applications.

FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data.

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.

A new intuitionistic fuzzy rule-based decision-making system for an operating system process scheduler.

We present a new intuitionistic fuzzy rule-based decision-making system based on intuitionistic fuzzy sets for a process scheduler of a batch operating system. Our proposed intuitionistic fuzzy scheduling algorithm, inputs the nice value and burst time of all available processes in the ready queue, intuitionistically fuzzify the input values, triggers appropriate rules of our intuitionistic fuzzy inference engine and finally calculates the dynamic priority (dp) of all the processes in the ready queue. Once the dp of every process is calculated the ready queue is sorted in decreasing order of dp of every process. The process with maximum dp value is sent to the central processing unit for execution. Finally, we show complete working of our algorithm on two different data sets and give comparisons with some standard non-preemptive process schedulers.