Influence of temperature and permeate recovery on energy consumption of a reverse osmosis system

A model permits analysis of the influence of temperature on permeate recovery and energy consumption. The proposed model is based on the following assumptions: (1) membrane morphology is temperature-independent; (2) membrane rejection and other transport characteristics of membranes are position-independent; (3) specific water permeability of membranes was based on exponential dependence of viscosity vs. temperature; (4) temperature-dependence depembrane rejection is assumed to be linear. This allows for analyzing the influence of channel geometry, feed concentration, flow rate and temperature on permeate recovery and energy consumption. Calculated data are included. The solutionpresented can be segmented andbuilt into systems for comprehensive techno-economic evaluation of the RO-based process where temperature-dependence of process characteristics has to be considered.     

Mechanical properties of gamma‐irradiated styrene‐butadiene rubber/acid‐treated vermiculite clay/maleic anhydride nanocomposites

Nanoparticle vermiculite (VMT) clay was prepared by treatment with hydrochloric acid. Styrene‐butadiene rubber (SBR) nanocomposites were prepared by mixing different contents (2.5, 5, 7.5, and 10 phr) of untreated (VMT) and acid‐treated (DVMT) vermiculite clay, respectively. In addition, different contents (3, 7, and 10 phr) of maleic anhydride (MA) as compatibilizer were mixed via direct melt compounding in internal mixer. The effect of gamma irradiation, VMT clay, and MA contents on the mechanical properties was studied. The acid‐treated VMT clay was characterized by x‐ray diffraction (XRD), transmission electron microscopy (TEM), and Fourier transform infrared (FT‐IR) spectroscopy. Meanwhile, the SBR/VMT composites, SBR/DVMT, and SBR/DVMT/MA nanocomposites were characterized via crosslinking density and tensile mechanical testing and FT‐IR spectroscopic analysis. The results indicated that good yield of nanoparticle vermiculite was achieved when the acid treatment was carried out for 120 h. In addition, the results showed that the presence of DVMT clay improved the chemical bonding in the SBR nanocomposites and hence their mechanical properties. The highest improvement was obtained when the contents of DVMT clay, MA, and irradiation dose were 10 phr, 3 phr, and 100 kGy, respectively. POLYM. ENG. SCI., 59:355–364, 2019. © 2018 Society of Plastics Engineers     

Inertia Effects on the Flow of Bingham Plastics Through Sudden Contractions in a Pipe

The present numerical study concentrates on the effects of moderate and high Reynolds numbers on the laminar flow of a non-Newtonian rigid viscoplastic (Bingham) fluid through a sudden contraction in a pipe. The flow is assumed to be steady, incompressible, and isothermal. Results are presented for a wide range of the governing Reynolds and yield numbers and the significant effects of these two parameters both on the integral and local kinematic properties of the flow field are established. Low yield numbers result in the disappearance of the recirculating flow region at the corner replacing it with a region of very low rates of deformation. The evolution of the centerline velocity in the vicinity of the contraction plane is shown to be independent of the yield number and dependent on the Reynolds number, while the concavities in the streamwise velocity profiles appearing at high Reynolds numbers are independent of the yield number. The pressure losses through the contraction increase with yield number with the effect being more pronounced at lower Reynolds numbers.     

Improved Approximations for Guarding 1.5-Dimensional Terrains

We present a 4-approximation algorithm for the problem of placing the fewest guards on a 1.5D terrain so that every point of the terrain is seen by at least one guard. This improves on the previous best approximation factor of 5 (see King in Proceedings of the 13th Latin American Symposium on Theoretical Informatics, pp. 629–640, 2006). Unlike most of the previous techniques, our method is based on rounding the linear programming relaxation of the corresponding covering problem. Besides the simplicity of the analysis, which mainly relies on decomposing the constraint matrix of the LP into totally balanced matrices, our algorithm, unlike previous work, generalizes to the weighted and partial versions of the basic problem.     

A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.      

Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization

In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a Hierarchically-distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88% accuracy.     

Coupled particle swarm optimization method with genetic algorithm for the static–dynamic performance of the magneto-electro-elastic nanosystem

As a first attempt, Fourier series expansion (FSE), particle swarm optimization (PSO), and genetic algorithm (GA) methods are coupled for analysis of the static–dynamic performance and propagated waves in the magneto-electro-elastic (MEE) nanoplate. The FSE method is presented for solving the motion equations of the MEE nanoplate. For increasing the performance of genetic algorithms for solving the problem, the particle swarm optimization technique is added as an operator of the GA. Accuracy, convergence, and applicability of the proposed mixed approach are shown in the results section. Also, we prove that for obtaining the convergence results of the PSO and GA, we should consider more than 16 iterations. Finally, it is shown that if designers consider the presented algorithm in their model, the results of phase velocity of the nanosystem will be increased by 27%. A useful suggestion is that there is a region the same as a trapezium in which there are no effects from magnetic and electric potential of the MEE face sheet on the phase velocity of the smart nanoplate, and the region will be bigger by increasing the wavenumber.

     

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.     

Benchmarking Kappa: Interrater Agreement in Software Process Assessments

Software process assessments are by now a prevalent tool for process improvement and contract risk assessment in the software industry. Given that scores are assigned to processes during an assessment, a process assessment can be considered a subjective measurement procedure. As with any subjective measurement procedure, the reliability of process assessments has important implications on the utility of assessment scores, and therefore the reliability of assessments can be taken as a criterion for evaluating an assessment's quality. The particular type of reliability of interest in this paper is interrater agreement. Thus far, empirical evaluations of the interrater agreement of assessments have used Cohen's Kappa coefficient. Once a Kappa value has been derived, the next question is “how good is it?” Benchmarks for interpreting the obtained values of Kappa are available from the social sciences and medical literature. However, the applicability of these benchmarks to the software process assessment context is not obvious. In this paper we develop a benchmark for interpreting Kappa values using data from ratings of 70 process instances collected from assessments of 19 different projects in 7 different organizations in Europe during the SPICE Trials (this is an international effort to empirically evaluate the emerging ISO/IEC 15504 International Standard for Software Process Assessment). The benchmark indicates that Kappa values below 0.45 are poor, and values above 0.62 constitute substantial agreement and should be the minimum aimed for. This benchmark can be used to decide how good an assessment's reliability is.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.