Turbulent boundary layers: is the wall falling or merely wobbling?

A quick passage through five centuries of turbulence research highlights the major milestones. The more recent cornerstones include Kolmogorov??s equilibrium theory of turbulence spectrum, the universal logarithmic law of wall-bounded flows, and the proliferation of direct numerical simulations. Evidence of recent fault lines in all three major achievements is presented, but also novel remedies as well as a few contemporary accomplishments are pointed out.     

Comments on the security of Chen’s authenticated encryption scheme

Chen (Computers and Electrical Engineering, vol. 30, 2004) illustrated that Tseng et al.’s authenticated encryption schemes, with message linkages for message flows, do not achieve their claimed integrity and authenticity properties. Furthermore, Chen presented some modified schemes to repair these flaws. In this paper, we show that the modified schemes proposed by Chen are not secure. In particular, we present an attack that allows a dishonest referee, in case of a dispute, to decrypt all the future and past authenticated ciphertext between the contended parties. We also present a simple fix to prevent this attack.     

Friction model for friction stir welding process simulation: Calibrations from welding experiments

The accurate 3D finite element simulation of the Friction Stir Welding (FSW) process requires a proper knowledge of both material and interface behaviors, but friction, the key phenomenon of this process, is quite difficult to model and identify. According to the extreme encountered conditions and the highly coupled nature of the material flow, simple tribological tests are not representative enough, so the welding process itself has been utilized in most analyses of the literature, although its complexity has led to use simplified numerical models and approaches. The recent development of more accurate 3D simulation software, which allows modeling the entire complexity of the FSW process, makes it possible to follow a much more rigorous inverse analysis (or calibration) approach. FSW trials are conducted on an Al 6061 aluminum plate with an unthreaded concave tool. Forces and tool temperatures are accurately recorded at steady welding state, for different welding speeds. The numerical simulations are based on an Arbitrary Lagrangian Eulerian (ALE) formulation that has been implemented in the Forge3^® F.E. software. The main feature of the numerical approach is to accurately compute the contact and frictional surface between the plate and the tool. A first study using Norton's friction model show the great sensitivity of welding forces and tool temperatures to friction coefficients, the need to take into account the changes brought to the contact surface by slight friction variations (thanks to the ALE formulation), the possibility to get very accurate calibrations on forces, and the impossibility to properly render the tool temperature profile. On the other hand, the use of Coulomb's friction model allows obtaining realistic temperature profiles and so calibrating a friction coefficient that offers an excellent agreement with experiments, on forces as much as on tool temperatures, for various welding speeds.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Cooking effects on fat and fatty acids composition of chicken muscles

Total lipids content of chicken breast and thigh muscles were investigated. Effects of boiling and roasting cooking treatments on these muscles were also studied to evaluate the probable changes in their fat contents, values of acid, peroxide and thiobarbituric acid (as measures for lipolysis and oxidative deterioration of fat) as well as to estimate their fatty acids (saturated and unsaturated) composition. The obtained results were tabulated and discussed.     

Optimization of black oxide coating thickness as an adhesion promoter for copper substrate in plastic integrated-circuit packages

Copper-oxide coating applied onto the copper substrate has emerged as an alternative to metallic coatings to improve adhesion with polymeric adhesives and molding compounds. The interfacial-bond strengths between the black oxide-coated Cu substrate and epoxy-based, glob-top resin were measured in button-shear tests, and the failure mechanisms were identified from the fracture-surface examination. The emphasis was to establish the correlation between the coating thickness, the surface roughness, and the interfacial adhesion with respect to treatment time. It was found that at the initial stage of treatment a thin layer of flat, cuprous oxide developed, above which fibrillar-cupric oxide was formed with further treatment until saturation with densified fibrils at about 150 sec. The interfacial-bond strength between the oxide-coated copper substrate and glob-top resin increased gradually with increasing treatment time, until the bond strength reached a plateau constant after a treatment for about 150 sec. There was a functional similarity between the oxide thickness, the surface roughness, and the interface-bond strength with respect to treatment time. A treatment time of 150 sec is considered an optimal condition that can impart the highest interface adhesion.     

An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic

Clustering is an effective approach for organizing a network into a connected hierarchy, load balancing, and prolonging the network lifetime. On the other hand, fuzzy logic is capable of wisely blending different parameters. This paper proposes an energy-aware distributed dynamic clustering protocol (ECPF) which applies three techniques: (1) non-probabilistic cluster head (CH) elections, (2) fuzzy logic, and (3) on demand clustering. The remaining energy of the nodes is the primary parameter for electing tentative CHs via a non-probabilistic fashion. A non-probabilistic CH election is implemented by introducing a delay inversely proportional to the residual energy of each node. Therefore, tentative CHs are selected based on their remaining energy. In addition, fuzzy logic is employed to evaluate the fitness (cost) of a node in order to choose a final CH from the set of neighboring tentative CHs. On the other hand, every regular (non CH) node elects to connect to the CH with the least fuzzy cost in its neighborhood. Besides, in ECPF, CH elections are performed sporadically (in contrast to performing it every round). Simulation results demonstrate that our approach performs better than well known protocols (LEACH, HEED, and CHEF) in terms of extending network lifetime and saving energy.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

Effect of Power Amplifier Nonlinearity on the Physical Layer Security of MIMO Systems

Wireless Personal Communications - This paper presents an analytical investigation on the effect of nonlinear high-power amplifiers on the physical layer security of multiple-input-multiple-output...