Enhancement of electromechanical properties of natural rubber by adding barium titanate filler: An electro-mechanical study

Natural rubber is one of the most potential electro-active polymers for sensors, actuators, and energy harvesting applications. Enhancing the characteristic properties of polymers by reinforcing with fillers that possess multifunctional attributes have attracted considerable attention. In the present study, barium titanate reinforced natural rubber composite is prepared by using two-roll mill mixing. Afterwards, mechanical, electrical, and electromechanical properties of the composites are extensively analyzed by reinforcing different amounts of barium titanate into the matrix of natural rubber. The fabricated dielectric composite shows excellent properties such as high dielectric constant, low dielectric losses, high dielectric breakdown strength, and extreme stretchability. It is observed that as the filler loading reaches the value of 11 parts per hundred rubber (phr), maximum agglomeration of the particles occurs. Maximum stretchability and highest ratio of dielectric constant to elastic modulus are obtained at 8 phr of barium titanate fillers and at the loading, a maximum actuation strain of 11.24% is achieved. This study provides a simple, economical, and effective method for preparing enhanced mechanical, electrical, and electromechanical properties of natural rubber composites, facilitating the wide applications of dielectric materials as actuators and generators.     

Binary polymer blend of ArPTU/PI with advanced comprehensive dielectric properties and ultra-high thermally stability

Flexible high-temperature polymeric dielectrics with advanced dielectric properties are urgently demanded in various applications. In this work, series of polymer blend films were prepared from aromatic polythiourea (ArPTU) and polyimide (PI). The experimental results revealed that the blend films were properly engineered to achieve higher breakdown strength, greater dielectric constant, and larger energy density than pure PI film. For instance, the optimum property was obtained from the blend film with 10 wt% ArPTU, exhibiting prominent dielectric properties (K = 4.52, E_b = 443 MV/m), enhanced energy density (4.00 J/cm³) as well as excellent heat resistance (T_g = 419°C). In addition, stable dielectric properties at broad temperature range from −50 to 250°C were also acquired. It is deduced that the good compatibility from ArPTU and PI with similar polarity are responsible for the improved properties. The superior comprehensive properties which combine the advantages of ArPTU and PI suggest the potential applications of ArPTU/PI blend film in high-temperature dielectric areas.     

Sustainability-guided life-cycle design and assessment for bio-based composite foams: Integrate flame retardancy/lightweight in usage and energy utilization after service

Sustainable development strategy has aroused a great interest in biomass resources as alternative raw materials. A kind of biomass-derived poly(butylene succinate) (PBS), has been developed as porous foams to reduce resource exhaustion and meet lightweight demands. For fire-safety in-service, graphene oxide (GO) was functionalized by 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) to combine flame-retardant elements and heat-barrier function. Hence, a very low loading level of P-containing GO as only 5 wt% could reduce peak heat release rate (pHRR) and total heat release (THR) of PBS-based foams by 58.5% and 22.3%, respectively. Meanwhile, N-/P-doped mesoporous char with a specific surface area of 136 m²/g, which derived from combustion of flame-retardant foaming PBS, contributes to a potential of energy storage applications in the capacitor or the anode of Li-ion battery with long-term stability. Overall, the sustainability of bio-based polyester could integrate lightweight of foaming, and be extended to utilization after use via facile combustion inspired by flame-retardancy design.     

Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics

This paper develops a comprehensive interpolation scheme for non-uniform rational B-spline (NURBS) curves, which does not only simultaneously meet the requirements of both constant feedrate and chord accuracy, but also real-time integrates machining dynamics in the interpolation stage. Although the existing work in this regard has realized the importance to simultaneously consider chord error and machining dynamics, none has really incorporated these in one complete interpolation scheme. In this paper, machining dynamics is considered for three aspects: sharp corners or feedrate sensitive corners on the curves, components with high frequencies or frequencies matching machine natural ones and high jerks. A look-ahead module was developed for detecting and adaptively adjusting the feedrate at the sharp corners. By Fast Fourier Transform (FFT) analysis with a moving window in the interpolation stage identified were some special frequency components such as those containing high frequencies or with frequencies matching machine natural ones. Then, the notch filtering or time spacing method was used to eliminate these components. To more completely reduce feedrate and acceleration fluctuations, the jerk-limited algorithm was also developed. Finally, the interpolated feedrate was further smoothened with B-spline fitting method and the NURBS curves were re-interpolated with the smoothened feedrate. During the interpolation, the chord error was repeatedly checked and confined in the prescribed tolerance. Two NURBS curves were used as examples to test the feasibility of the developed interpolation scheme.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.     

A secure image encryption scheme based on chaotic maps and affine transformation

Due to the interesting nonlinear dynamic properties of chaotic maps, recently chaos-based encryption algorithms have gained much attention in cryptographic communities. However, many encryption schemes do not fulfil the minimum key space requirement, which is an essential concern in many secure data applications. In this paper, an efficient chaos-based image encryption scheme with higher key space is presented. Even with a single round of encryption, a significantly larger key space can be achieved. The proposed scheme removes correlation among image pixels via random chaotic sequences, simply by XOR and addition operations. In order to resist against numerous attacks, we apply the affine transformation to get the final ciphertext image. The security of the proposed scheme is proved through histogram, contrast, PSNR, entropy, correlation, key space, key sensitivity and differential attack analysis. Many significant properties of chaotic maps, sensitivity to initial condition and control parameters, structure and attack complexity, make the anticipated scheme very reliable, practical and robust in various secure communication applications.     

A cross-layer approach for partition detection at overlay layer for structured P2P in MANETs

Due to limited radio range and mobility of nodes in mobile ad hoc networks (MANETs), the network partitioning and merging could occur frequently. When structured peer-to-peer (P2P) overlays are running over MANETs, then network partition in the physical network can also cause network partition at the overlay layer. Existing approaches for structured P2P overlay over MANETs do not detect network partition at the overlay layer. This paper proposes a cross-layer approach to detect network partition at the overlay layer for structured P2P overlay over MANETs. Simulation results show that the proposed approach is highly effective and efficient in terms of routing overhead, success ratio and false-negative ratio.     

FPGA-based IP cores implementation for face recognition using dynamic partial reconfiguration

This paper presents a combination of novel feature vectors construction approach for face recognition using discrete wavelet transform (DWT) and field programmable gate array (FPGA)-based intellectual property (IP) core implementation of transform block in face recognition systems. Initially, four experiments have been conducted including the DWT feature selection and filter choice, features optimisation by coefficient selections and feature threshold. To examine the most suitable method of feature extraction, different wavelet quadrant and scales have been evaluated, and it is followed with an evaluation of different wavelet filter choices and their impact on recognition accuracy. In this study, an approach for face recognition based on coefficient selection for DWT is presented, and the significant of DWT coefficient threshold selection is also analysed. For the hardware implementation, two architectures for two-dimensional (2-D) Haar wavelet transform (HWT) IP core with transpose-based computation and dynamic partial reconfiguration (DPR) have been synthesised using VHDL and implemented on Xilinx Virtex-5 FPGAs. Experimental results and comparisons between different configurations using partial and non-partial reconfiguration processes and a detailed performance analysis of the area, power consumption and maximum frequency are also discussed in this paper.