(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

Service Oriented Approach State of the Art

For many years, computer systems have emerged; they now occupy an important place in our daily lives. The growing needs and ever increasing use of computer systems have made application development more and more complicated, The complexity of these applications poses problems such as reuse, installation, administration and evolution of applications. The development of applications is related to the evolution of paradigms and approaches to developing them. This paper presents different approaches and paradigms of development starting with the procedural approach, coming up for service, through the component and object-oriented approaches. Also, for each of the approaches we determine the advantages and limitations.     

An effective DeepWINet CNN model for off-line text-independent writer identification

Pattern Analysis and Applications - Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has...     

Stability and robustness of fuzzy adaptive control of nonlinear systems

In this paper, we propose a new approach that guarantees the stability and robustness of an adaptive control law of a nonlinear system.The control diagram proposed contains a Takagi–Sugeno–Kang fuzzy controller (TSK-FC) and a training block allowing the online adaptation of the FC parameters. The adaptation algorithm used is based on the gradient with minimization of the quadratic error between the system output and that desired by using the direct method of Lyapunov. However, our approach considers the gradient step of each adaptive FC parameter to be bound. This approach was applied to the control of an inverted pendulum. The results obtained confirm well the validity of such an adaptation especially the guarantee of the pendulum stability and the robustness of its control with respect to the disturbances introduced on the FC parameters and the pendulum itself.     

Distributed control and speed sensorless for the synchronisation of multi-robot systems

This paper investigates a synchronization approach to trajectory tracking of networked robotic systems while maintaining time-varying formations. The objective is to control networked robots to track a desired trajectory while synchronizing their behaviors. Combining trajectory tracking and synchronization algorithms, the developed approach uses a cross-coupling technical to create interconnections for mutual synchronization of robots. The main objective of distributed approach is to generate an emerging behavior using only local information interactions. First, a distributed scheme is developed to achieve the networked robots synchronization on undirected graph. Then, the leaderless synchronized tracking problem in the case when only position measurements are available, will be presented. For both cases: In the presence of the velocity feedback or in its absence, the controller, designed by incorporating the cross-coupling technical into a sliding mode control architecture, successfully guarantees asymptotic convergence to zero of both position tracking and synchronization errors simultaneously. The Lyapunov-based approach has been used to establish the multi-robot systems asymptotic stability. A real-time software simulator is developed to visualize the synchronized behaviors. Based on LabVIEW integrated development environment (IDE), a developed human-machine-interface (HMI) allows its user to control, in real time, the networked robots. Simulation and experimental results are provided to demonstrate performances of the proposed control schemes.     

Automated Proofs of Block Cipher Modes of Operation

We present a Hoare logic for proving semantic security and determining exact security bounds of a block cipher mode of operation. We propose a simple yet expressive programming language to specify encryption modes, semantic functions for each command (statement) in the language, an assertion language that allows to state predicates and axioms, and rules to propagate the predicates through the commands of a program. We also provide heuristics for finding loop invariants that are necessary for the application of our rule on for-loops. This enables us to prove the security of protocols that take arbitrary length messages as input. We implemented a prototype that uses this logic to automatically prove the security of block cipher modes of operation. This prototype can prove the security of many standard modes of operation, such as Cipher Block Chaining (CBC), Cipher FeedBack mode (CFB), Output FeedBack (OFB), and CounTeR mode (CTR).     

Deep multilayer multiple kernel learning

Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods.     

Study of thermal and acoustic noise interferences in low stiffness atomic force microscope cantilevers and characterization of their dynamic properties

The atomic force microscope (AFM) is a powerful tool for the measurement of forces at the micro/nano scale when calibrated cantilevers are used. Besides many existing calibration techniques, the thermal calibration is one of the simplest and fastest methods for the dynamic characterization of an AFM cantilever. This method is efficient provided that the Brownian motion (thermal noise) is the most important source of excitation during the calibration process. Otherwise, the value of spring constant is underestimated. This paper investigates noise interference ranges in low stiffness AFM cantilevers taking into account thermal fluctuations and acoustic pressures as two main sources of noise. As a result, a preliminary knowledge about the conditions in which thermal fluctuations and acoustic pressures have closely the same effect on the AFM cantilever (noise interference) is provided with both theoretical and experimental arguments. Consequently, beyond the noise interference range, commercial low stiffness AFM cantilevers are calibrated in two ways: using the thermal noise (in a wide temperature range) and acoustic pressures generated by a loudspeaker. We then demonstrate that acoustic noises can also be used for an efficient characterization and calibration of low stiffness AFM cantilevers. The accuracy of the acoustic characterization is evaluated by comparison with results from the thermal calibration.     

Siting of PV power plants on inclined terrains

Most of the studies conducted on solar energy assessment and solar system siting focus on flat terrains that are usually difficult and expensive to acquire in urban areas. This paper investigates the possibility of using inclined terrains for siting photovoltaic (PV) power plants surrounding urban area. Both analytical analysis and simulations are carried out in order to analyse and validate the effectiveness and benefits of the use of inclined land for PV siting. Geographical information system and numerical weather prediction were used to implement the suggested original approach and to select suitable inclined terrains for the PV system installation and to calculate the total generation potential for energy production. Muscat, the capital of Oman, was selected as a reference for the implementation of this approach.