Facile modification of polysulfone hollow-fiber membranes via the incorporation of well-dispersed iron oxide nanoparticles for protein purification

Protein existence in wastewater is an important issue in wastewater management because proteins are generally present as contaminants and foulants. Hence, in this study, we focused on designing a polysulfone (PSf) hollow-fiber membrane embedded with hydrophilic iron oxide nanoparticles (IONPs) for protein purification by means of ultrafiltration. Before membrane fabrication, the dispersion stability of the IONPs was enhanced by the addition of a stabilizer, namely, citric acid (CA). Next, PSf–IONP–CA nanocomposite hollow-fiber membranes were prepared via a dry–wet spinning process and then characterized in terms of their hydrophilicity and morphology. Ultrafiltration and adsorption experiments were then conducted with bovine serum albumin as a model protein. The results that an IONP/CA weight ratio of 1:20 contributed to the most stable IONP dispersion. It was also revealed that the membrane incorporated with IONP–CA at a weight ratio of 1:20 exhibited the highest pure water permeability (58.6 L m⁻² h⁻¹ bar⁻¹) and protein rejection (98.5%) while maintaining a low protein adsorption (3.3 μg/cm²). The addition of well-dispersed IONPs enhanced the separation features of the PSf hollow-fiber membrane for protein purification. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47502.     

Simulation and Empirical Studies of Solvent Evaporation Rates in Vacuum Evaporation Crystallization

Simulation results for continuous vacuum evaporation crystallization obtained by Aspen Plus and experimental results for semi‐batch vacuum evaporation crystallization are presented. In the crystallization experiments, the fixed heat duty was used to compare the water evaporation rates and crystal properties obtained at different pressures. The solution selected was aqueous glycine. It has the ability to form a number of different crystalline polymorphs, which allows it to exhibit a variety of different physical properties while maintaining its chemical properties. X‐ray diffraction results demonstrated that mainly the γ‐crystal form is produced under the conditions applied in vacuum evaporation crystallization.     

Effects of Fluids on the Simulated Clinical-Dental Machining of a Glass Ceramic

Effects of fluids on material removal rate, chipping damage, and surface roughness in the simulated clinical-dental machining of a dental-type glass ceramic were investigated. Significant differences in removal rate were obtained among the fluids investigated, but only a 4 wt% boric acid solution gave a higher removal rate than conventionally used water. Chipping damage was substantially lower for the boric acid and an oil-emulsion coolant compared with other fluids tested. Surface roughness was independent of the fluids used. The results indicate that improvement can be achieved in both material removal rate and machining damage by the appropriate selection of coolant chemistry.     

A novel classification approach based on Extreme Learning Machine and Wavelet Neural Networks

Multimedia Tools and Applications - In this paper, we present a novel classification approach based on Extreme Learning Machine (ELM) and Wavelet Neural Networks. We introduce two novel...     

Recursive system identification method from binary input/output measurements

This paper presents two online identification algorithms of finite impulse response (FIR) systems using binary measurements both on the input and on the output. These algorithms are based on the least mean square (LMS) technique and on the estimation of the correlation functions of the input and output from binary data. Note that the second algorithm is a simplified version of the first one in the case of a white noise on the input. The convergence and variance analyses are provided. A numerical example is given to demonstrate the effectiveness of the proposed algorithms.     

Hybrid clustering analysis using improved krill herd algorithm

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.     

An Efficient Digital Confidentiality Scheme Based on Commutative Chaotic Polynomial

Multimedia Tools and Applications - One of the most critical aspects of this technologically progressive era is the propagation of information through an unsecured communication channel. The...     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.