Effect of stabilizer type on the physicochemical properties of rigid PVC. III

The crystallinity of rigid PVC specimens, stabilized by a tin mercaptide or a lead stabilizer, has been studied. It is found that the crystallinity of rigid PVC is greatly influenced by the type of stabilizer added, and that the response of the PVC toward subsequent UV exposure was also different. The effects of unstable structures and crystallinity, after UV irradiation, on the mechanical properties of rigid PVC have been investigated.     

Experimental investigation of particle contact time at the wall of gas fluidized beds

The contact time of particles at the walls of gas fluidized beds has been studied using a radioactive particle tracking technique to monitor the position of a radioactive tracer. The solids used were sand or FCC particles fluidized by air at room temperature and atmospheric pressure at various superficial velocities, covering both bubbling and turbulent regimes of fluidization. Based on the analysis of tracer positions, the motion of individual particles near the walls of the fluidized bed was studied. The contact time, contact distance and contact frequency of the particles at the wall were evaluated from these experimental data. It was found that in a bed of sand particles, the mean wall contact time of the fluidized bed of sand particles decreases by increasing the gas velocity in the bubbling and increases in the turbulent fluidization. In other words, the particle-wall contact time is minimum at the onset of turbulent fluidization in the bed of sand particles. However, the mean wall contact time is almost constant in both regimes of fluidization in the bed of FCC particles. All the existing models in the literature predict a decreasing contact time when the gas velocity in the bed is increased. It was also shown that the contact distance increases monotonously by increasing the gas velocity in the bed of sand particles, while it is almost constant for the bed of FCC particles. Contact frequency has a trend similar to that of the contact time for both sand and FCC particles.     

Reserve estimation of an open pit mine under price uncertainty by real option approach

Reserve estimation is a key to find the correct NPV in a mining project. The most important factor in reserve estimation is the metal price. Metal price fluctuations in recent years were exaggerated, and imposed a high degree of uncertainty to the re-serve estimation, and in consequence to the whole mine planning procedure. Real option approach is an efficient method of deci-sion making in the uncertain conditions. This approach has been used for evaluation of defined natural resources projects until now. This study considering the metal price uncertainty used real option approach to prepare a methodology for reserve estimation in open pit mines. This study was done on a copper cylindrical deposit, but the achieved methodology can be adjusted for all kinds of deposits. This methodology was comprehensively described through the examples in such a manner that can be used by the mine planners.     

Synthesis and properties of new poly(amide imide)s containing trimellitic rings and hydantoin moieties in the main chain under microwave irradiation

Trimellitic anhydride was reacted with 4,4′‐diaminodiphenyl ether in a mixture of acetic acid and pyridine (3 : 2) at room temperature and was refluxed at 90–100°C, and N,N′‐(4,4′‐diphenylether) bistrimellitimide (3) was obtained in a quantitative yield. 3 was converted into N,N′‐(4,4′‐diphenylether) bistrimellitimide diacid chloride (4) by a reaction with thionyl chloride. Then, six new poly(amide imide)s were synthesized under microwave irradiation with a domestic microwave oven through the polycondensation reactions of 4 with six different derivatives of 5,5‐disubstituted hydantoin in the presence of a small amount of a polar organic medium such as o‐cresol. The polycondensation proceeded rapidly and was completed within 7–10 min, producing a series of new poly(amide imide)s in high yields with inherent viscosities of 0.27–0.66 dL/g. The resulting poly(amide imide)s were characterized by elemental analysis, viscosity measurements, differential scanning calorimetry, thermogravimetric analysis, derivative thermogravimetry, solubility testing, and Fourier transform infrared spectroscopy. All the polymers were soluble at room temperature in polar solvents such as N,N‐dimethylacetamide, N,N‐dimethylformamide, dimethyl sulfoxide, tetrahydrofuran, and N‐methyl‐2‐pyrrolidone. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3447–3453, 2004     

A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

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.     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...     

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...     

ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform

The Journal of Supercomputing - Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness...