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.     

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.     

Investigating the influence of the counter Si-cell on the optoelectronic performance of high-efficiency monolithically perovskites/silicon tandem cells under various optical sources

Nowadays, tandem structures have become a valuable competitor to conventional silicon solar cells, especially for perovskite over silicon, as metal halides surpassed Si with tunable bandgaps, high absorption coefficient, low deposition, and preparation costs. This led to a remarkable enhancement in the overall efficiency of the whole cell and its characteristics. Consequently, this expands the usage of photovoltaic technology in various fields of applications not only under conventional light source spectrum in outdoor areas, i.e., AM1.5G, but also under artificial light sources found indoors with broadband intensity values, such as Internet of things (IoTs) applications to name a few. We introduce a numerical model to analyze perovskite/Si tandem cells (PSSTCs) using both crystalline silicon (c-Si) and hydrogenated amorphous silicon (a-Si:H) experimentally validated as base cells. All proposed layers have been studied with J-V characteristics and energy band diagrams under AM1.5G by using SCAPS-1D software version 3.7.7. Thereupon, the proposed architectures were tested under various artificial lighting spectra. The proposed structures of Li4Ti5O12/CsPbCl3/MAPbBr3/CH3NH3PbI3/Si recorded a maximum power conversion efficiency (PCE) of 25.25% for c-Si and 17.02% for a-Si:H, with nearly 7% enhancement concerning the Si bare cell in both cases.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Effect of Initial Annealing Temperature on Microstructural Development and Microhardness in High‐Purity Copper Processed by High‐Pressure Torsion

The effect of the initial annealing temperature on the evolution of microstructure and microhardness in high purity OFHC Cu is investigated after processing by HPT. Disks of Cu are annealed for 1 h at two different annealing temperatures, 400 and 800 °C, and then processed by HPT at room temperature under a pressure of 6.0 GPa for 1/4, 1/2, 1, 5, and 10 turns. Samples are stored for 6 months after HPT processing to examine the self‐annealing effects. Electron backscattered diffraction (EBSD) measurements are recorded for each disk at three positions: center, mid‐radius, and near edge. Microhardness measurements are also recorded along the diameters of each disk. Both alloys show rapid hardening and then strain softening in the very early stages of straining due to self‐annealing with a clear delay in the onset of softening in the alloy initially annealed at 800 °C. This delay is due to the relatively larger initial grain size compared to the alloy initially annealed at 400 °C. The final microstructures consist of homogeneous fine grains having average sizes of ≈0.28 and ≈0.34 µm for the alloys initially annealed at 400 and 800 °C, respectively. A new model is proposed to describe the behavior of the hardness evolution by HPT in high purity OFHC Cu.