Synthesis,sintering and microstructure of 3Y-TZP/CuO nano-powder composites

Nanocrystalline 3Y-TZP and copper-oxide powders were prepared by co-precipitation of metal chlorides and copper oxalate precipitation respectively. CuO (0.8 mol%) doped 3Y-TZP powder compacts were prepared from the nanocrystalline powders. Dilatometer measurements on these compacts were performed to investigate the sintering behaviour. Microstructure investigations of the sintered compacts were conducted. It is found that additions of the copper-oxide powders in the nanocrystalline 3Y-TZP leads to an enhancement of densification, formation of monoclinic zirconia phase and significant zirconia grain growth during sintering.     

Trichloroisocyanuric acid,a new and efficient catalyst for the synthesis of dihydropyrimidinones

An efficient three-component synthesis of 3,4-dihydropyrimidinones using trichloroisocyanuric acid (TCCA) as a mild, homogeneous and neutral catalyst for Biginelli reaction in ethanol or DMF under reflux condition and microwave irradiation is described. Yields are excellent in either of the two conditions.     

Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13–28 wt%), feed rate (1–5 mL/h), and tip-to-collector distance (10–23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results.     

Evolutionary assembled neural networks for making medical decisions with minimal regret: Application for predicting advanced bladder cancer outcome

Development of reliable medical decision support systems has been the subject of many studies among which Artificial Neural Networks (ANNs) gained increasing popularity and gave promising results. However, wider application of ANNs in clinical practice remains limited due to the lack of a standard and intuitive procedure for their configuration and evaluation which is traditionally a slow process depending on human experts. The principal contribution of this study is a novel procedure for obtaining ANN predictive models with high performances. In order to reach those considerations with minimal user effort, optimal configuration of ANN was performed automatically by Genetic Algorithms (GA). The only two user dependent tasks were selecting data (input and output variables) and evaluation of ANN threshold probability with respect to the Regret Theory (RT). The goal of the GA optimization was reaching the best prognostic performances relevant for clinicians: correctness, discrimination and calibration. After optimally configuring ANNs with respect to these criteria, the clinical usefulness was evaluated by the RT Decision Curve Analysis. The method is initially proposed for the prediction of advanced bladder cancer (BC) in patients undergoing radical cystectomy, due to the fact that it is clinically relevant problem with profound influence on health care. Testing on the data of the ten years cohort study, which included 183 evaluable patients, showed that soft max activation functions and good calibration were the most important for obtaining reliable BC predictive models for the given dataset. Extensive analysis and comparison with the solutions commonly used in literature showed that better prognostic performances were achieved while user-dependency was significantly reduced. It is concluded that presented procedure represents a suitable, robust and user-friendly framework with potential to have wide applications and influence in further development of health care decision support systems.     

A wavelet-based multiscale vector-ANN model to predict comovement of econophysical systems

The paper proposes a parsimonious nonlinear framework for modeling bivariate stochastic processes. The method is a vector autoregressive-like approach equipped with a wavelet-based feedforward neural network, allowing practitioners dealing with extremely random two-dimensional information to make predictions and plan their future more and more precisely. Artificial Neural Networks (ANN) are recognized as powerful computing devices and universal approximators that proved valuable for a wide range of univariate time series problems. We expand their coverage to handle nonlinear bivariate data. Wavelet techniques are used to strengthen the procedure, since they allow to break up processes information into a finite number of sub-signals, and subsequently extract microscopic patterns in both time and frequency fields. The proposed model can be very valuable especially when modeling nonlinear econophysical systems with high extent of volatility.     

Human–computer interaction using vision-based hand gesture recognition systems: a survey

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human--computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.     

A review of online learning in supervised neural networks

Learning in neural networks can broadly be divided into two categories, viz., off-line (or batch) learning and online (or incremental) learning. In this paper, a review of a variety of supervised neural networks with online learning capabilities is presented. Specifically, we focus on articles published in main indexed journals in the past 10 years (2003–2013). We examine a number of key neural network architectures, which include feedforward neural networks, recurrent neural networks, fuzzy neural networks, and other related networks. How the online learning methodologies are incorporated into these networks is exemplified, and how they are applied to solving problems in different domains is highlighted. A summary of the review that covers different network architectures and their applications is presented.     

BSS: block-based sharing scheme for secure data storage services in mobile cloud environment

For the last few years, academia and research organizations are continuously investigating and resolving the security and privacy issues of mobile cloud computing environment. The additional consideration in designing security services for mobile cloud computing environment should be the resource-constrained mobile devices. The execution of computationally intensive security services on mobile device consumes battery’s charging quickly. In this regard, the study presents a novel energy-efficient block-based sharing scheme that provides confidentiality and integrity services for mobile users in the cloud environment. The block-based sharing scheme is compared with the existing schemes on the basis of energy consumption, CPU utilization, memory utilization, encryption time, decryption time, and turnaround time. The experimental results show that the block-based sharing scheme consumes less energy, reduces the resources utilization, improves response time, and provides better security services to the mobile users in the presence of fully untrusted cloud server(s) as compared to the existing security schemes.     

Innovation systems in Malaysia: a perspective of university—industry R&D collaboration

Collaborative research and development (R&D) activities between public universities and industry are of importance for the sustainable development of the innovation ecosystem. However, policymakers especially in developing countries show little knowledge on the issues. In this paper, we analyse the level of university–industry collaboration in Malaysia. We further examine the fundamental conditions that hinder university–industry collaboration despite the government’s initiatives to improve such linkages. We show that the low collaboration  is a result of an R&D gap between the entities. While the universities engage in basic and fundamental R&D, the private sectors involved in incremental innovation that requires less R&D investments. The different nature of the industries’ R&D requires closer cooperation between firms namely buyers, suppliers and technical service providers and not the universities. Among others, the lack of an intermediary role, absorptive capacity and collaborative initiative by the industry also contribute to the problem. The study suggests that the collaborative activities can benefit both if deliberate and effective efforts on reducing the R&D mismatch are made between the universities and industry. Likewise, proper institutional arrangements in coordinating these activities are required. This result seems to reflect the nature of many developing countries’ national innovation systems, and therefore, lessons from Malaysia may serve as a good case study.     

A lightweight active service migration framework for computational offloading in mobile cloud computing

Cloud computing enables access to the widespread services and resources in cloud datacenters for mitigating resource limitations in low-potential client devices. Computational cloud is an attractive platform for computational offloading due to the attributes of scalability and availability of resources. Therefore, mobile cloud computing (MCC) leverages the application processing services of computational clouds for enabling computational-intensive and ubiquitous mobile applications on smart mobile devices (SMDs). Computational offloading frameworks focus on offloading intensive mobile applications at different granularity levels which involve resource-intensive mechanism of application profiling and partitioning at runtime. As a result, the energy consumption cost (ECC) and turnaround time of the application is increased. This paper proposes an active service migration (ASM) framework for computational offloading to cloud datacenters, which employs lightweight procedure for the deployment of runtime distributed platform. The proposed framework employs coarse granularity level and simple developmental and deployment procedures for computational offloading in MCC. ASM is evaluated by benchmarking prototype application on the Android devices in the real MCC environment. It is found that the turnaround time of the application reduces up to 45 % and ECC of the application reduces up to 33 % in ASM-based computational offloading as compared to traditional offloading techniques which shows the lightweight nature of the proposed framework for computational offloading.