Effect of Zn and Pb as alloying elements on the electrochemical behavior of brass in NaCl solutions

The electrochemical behavior of brasses with various Zn content (5.5–38 mass%) and brass (Cu–38Zn) with different Pb contents (1–3.4 mass%) in 0.6 M NaCl was investigated. The effects of temperature, immersion time, and concentration of chloride ions on the behavior of the different alloys were studied. The pitting corrosion behavior of Cu–Zn alloys and leaded–brass alloys in 0.6 M NaCl solution was also investigated. Open-circuit potential measurements (OCP), polarization techniques and electrochemical impedance spectroscopy (EIS) were used. The results show that the increase in the Zn content increases the corrosion rate of the brass alloys in chloride solutions, while the increase of Pb content in Cu–38Zn–Pb decreases the corrosion rate of the alloy. Long immersion time of the alloys in the aqueous electrolyte improves their stability due to the formation of passive film on the alloy surface. The breakdown potential is shifted to more negative direction with increasing the Zn content, whereas it shifts towards positive values with increasing Pb content. Equivalent circuit model for the electrode/electrolyte interface under different conditions was proposed to illustrate the electrochemical processes taking place at the interface. The electrochemical behavior of the different alloys was discussed in view of the fitting results.     

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.     

Processing of an East Mediterranean phosphate ore sample by an integrated attrition scrubbing/classification scheme (part one)

ABSTRACT

In this study, an Egyptian siliceous/calcareous sedimentary phosphorite was subjected to beneficiation by unsophisticated economic means. The mineralogical and chemical examination showed that the ore sample is a typical example for the East Mediterranean phosphorites with 21.80% P₂O₅, 10.93% acid insoluble and 16.61% loss on ignition. The primary crushed sample was classified into three size fractions without any milling and they were separately subjected to an integrated attrition scrubbing study. Screening and classification on the 2.30 mm and 0.071 mm sieves were sequentially introduced with the attrition process. The effect of solid%, attrition speed and time were studied and optimized for each size fraction. Results showed that three products were produced and could be categorized according to their P₂O₅ content into the coarse tailing (+2.30 mm), the attrition concentrate (?2.30 + 0.071 mm) and the phosphate fines (?0.071 mm). The P₂O₅ contents of these products reached 12.24%, 28.72% and 18.77%, with P₂O₅ distribution reached 6.74%, 51.75% and 41.51%, respectively. A global P₂O₅ of 28.72% was obtained with a recovery of about 52%. A tentative flow-sheet for the whole process was postulated.     

Bridging value modelling to ArchiMate via transaction modelling

The ArchiMate modelling language provides a coherent and a holistic view of an enterprise in terms of its products, services, business processes, actors, business units, software applications and more. Yet, ArchiMate currently lacks (1) expressivity in modelling an enterprise from a value exchange perspective, and (2) rigour and guidelines in modelling business processes that realize the transactions relevant from a value perspective. To address these issues, we show how to connect e \(^{3}\) value, a technique for value modelling, to ArchiMate via transaction patterns from the DEMO methodology. Using ontology alignment techniques, we show a transformation between the meta models underlying e \(^{3}\) value, DEMO and ArchiMate. Furthermore, we present a step-wise approach that shows how this model transformation is achieved and, in doing so, we also show the of such a transformation. We exemplify the transformation of DEMO and e \(^{3}\) value into ArchiMate by means of a case study in the insurance industry. As a proof of concept, we present a software tool supporting our transformation approach. Finally, we discuss the functionalities and limitations of our approach; thereby, we analyze its and practical applicability.     

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%.

     

Fairness-related challenges in mobile opportunistic networking

The fundamental challenge in opportunistic networking, regardless of the application, is when and how to forward a message. Rank-based forwarding techniques currently represent one of the most promising methods for addressing this message forwarding challenge. While these techniques have demonstrated great efficiency in performance, they do not address the rising concern of fairness amongst various nodes in the network. Higher ranked nodes typically carry the largest burden in delivering messages, which creates a high potential of dissatisfaction amongst them. In this paper, we adopt a real-trace driven approach to study and analyze the trade-offs between efficiency, cost, and fairness of rank-based forwarding techniques in mobile opportunistic networks.Our work comprises three major contributions. First, we quantitatively analyze the trade-off between fair and efficient environments. Second, we demonstrate how fairness coupled with efficiency can be achieved based on real mobility traces. Third, we propose FOG, a real-time distributed framework to ensure efficiency–fairness trade-off using local information. Our framework, FOG, enables state-of-the-art rank-based opportunistic forwarding algorithms to ensure a better fairness–efficiency trade-off while maintaining a low overhead. Within FOG, we implement two real-time distributed fairness algorithms; Proximity Fairness Algorithm (PFA), and Message Context Fairness Algorithm (MCFA). Our data-driven experiments and analysis show that mobile opportunistic communication between users may fail with the absence of fairness in participating high-ranked nodes, and an absolute fair treatment of all users yields inefficient communication performance. Finally our analysis shows that FOG-based algorithms ensure relative equality in the distribution of resource usage among neighbor nodes while keeping the success rate and cost performance near optimal.     

A Replicated Survey of IT Software Project Failures

Despite various industry reports about the failure rates of software projects, there's still uncertainty about the actual figures. Researchers performed a global Web survey of IT departments in 2005 and 2007. The results suggest that the software crisis is perhaps exaggerated and that most software projects deliver. However, the overall project failure rate, including cancelled and completed but poorly performing projects, remains arguably high for an applied discipline.     

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.