Frequency dependence of the complex dielectric constant of sodium carboxymethyl cellulose

The dielectric constant, ε′, and dielectric loss, ε″, were determined for three solid carboxymethyl cellulose samples having different levels of substitution and different degrees of polymerization over a frequency range of 0.1 – 10 000 kHz at temperatures from 10–60°C. In contrast to the two relaxation processes, γ and β, previously observed in native cotton cellulose, only one relaxation process within a frequency range of 0.1 – 1 kHz was identified. It was found that the dielectric properties do not only depend on the degree of substitution, but also on the weight-average degree of polymerization and uniformity of distribution.     

Iterative interference cancellation for high spectral efficiencysatellite communications

The problem of efficient utilization of the frequency spectrum for satellite systems is investigated; one which results as a consequence of highly crowding adjacent channels. An analytical characterization of the resulting interference channel is introduced and then exploited for interference cancellation. Two classes of cancelers are investigated. The first approach does not benefit from the forward error control (FEC) coding information which limits the performance gain. This motivates the second approach where a joint implementation of interference cancellation and decoding is developed using soft-input-soft-output (SISO) modules along with the iterative structure. It is shown that iterative interference cancellation techniques can achieve significant gains compared with the single-user matched filter receiver     

Physicochemical investigation of the decomposition products of ammonium metal carboxylates: ammonium ferric citrate hydrate

The thermal decomposition up to 400 °C of ammonium ferric citrate hydrate, of unknown structure and formula weight, was studied by thermogravimetry, differential thermal analysis, infrared (IR) spectroscopy and X-ray diffractometry. The possible identities of the formula weight and the intermediate products of calcination are discussed. The results revealed that the parent material is amorphous and contains two moles of water and two moles of ammonia. Decomposition takes place via six weight-loss processes, three endothermic (90–230 °C) and three exothermic (240–298 °C), leading eventually to the formation of Fe₂O₃. The intermediate solid products are mainly unstable amorphous oxycarbonates, as indicated by X-ray and IR spectroscopies. The gas-phase decomposition products identified by IR spectroscopy are NH₃, CO₂, CO, CH₃COCH₃, CH₄ and NH₄OH. Surface area measurement and scanning electron microscopy showed that Fe₂O₃, the final product at 400 °C, hada surface area of 40 m²/g and good crystalline and porous character.     

Preparation,characterization, and some physical properties of polypropylene/poly(methyl acrylate)‐grafted glass wool composites

Polypropylene/poly(methyl acrylate)‐grafted glass wool (PMA‐g‐GW) mixes were prepared. The polymerization process was carried out using potassium persulfate (PPS) and PPS/acetone sodium bisulfite (ASBS) as a redox‐pair initiation system at 60 and 70°C. The effect of using PPS or PPS/ASBS on the grafting percent and conversion percent reveals that the conversion percent values on using PPS as an initiator are higher than those of PPS/ASBS, while in the case of grafting, the inverse is true, that is, using PPS as an initiator gives grafting percent values lower than those that can be obtained using PPS/ASBS. The dielectric properties, thermal diffusivity, specific heat capacity, and thermal conductivity of PP loaded with modified glass wool as a function of different types and concentrations of initiators—used in the grafting polymerization process, namely, PPS and the redox initiating system—were also studied. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 87: 723–732, 2003     

Modification of sandy soil using water‐borne polymer

A new polymeric system has been applied for structural modification of (noncompactable) sandy soils. The system is based on a water‐borne styrene acrylic polymeric formulation (emulsion) containing varying amounts of solid polymer. The present work deals with system optimization and measurements designed to examine the effects of polymer content on hydraulic conductivity and compressive strength. Samples were prepared from prescribed amounts of polymer, water, and sand by using two different preparations methods (mixing and spraying). Measurements of hydraulic conductivity for both sets of samples were conducted in a flexible membrane test apparatus. For the first set of samples, the permeability coefficient of the sand was noted to be reduced 10‐fold (from 10^?5 to 10^?6 m s^?1) upon the incorporation of about 2% polymer. In the second set (samples prepared with the spraying method), the hydraulic conductivity was further reduced to 7.2 × 10^?7 at a polymer concentration of about 2%. Stress–strain measurements made on dry cylindrical specimens disclosed remarkable enhancement in the mechanical behavior of the system. For both types of preparation methods, the compressive strength and modulus of elasticity increased linearly with the polymer concentration in the sample. Scanning electron microscopic examination revealed that the dramatic reduction in the permeability and the improved mechanical properties are attributed to the polymer coverage of the sand particles and the development of interconnecting ties between them. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 2484–2491, 2003     

An efficient IoT based smart farming system using machine learning algorithms

Multimedia Tools and Applications - This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop...     

Image Super Resolution and Enhancement Using E-spline

E-splines （Exponential spline） polynomials represent the best smooth transition between continuous and discrete domains. As they are constructed from convolution of exponential segments, there are many degrees of freedom to optimally choose the most convenient E-spline, suitable for a specific application. In this paper, the parameters of these E-splines were optimally chosen, to enhance the performance of image zooming and interpolation schemes. The proposed technique is based on minimizing the total variation function of the detail coefficients of the E-spline based wavelet decomposition. In zooming applications, the quality of interpolated images are further improved and sharpened by applying ICA technique to them, in order to remove any dependency. Illustrative examples are given to verify image enhancement of the proposed E-spline scheme, when compared with the existing approaches.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.