Rising Crack-Growth-Resistance (R-Curve) Behavior of Toughened Alumina and Silicon Nitride

R -curves for a sinter/HIPed SiC(whisker)-reinforced alumina and a sintered silicon nitride were assessed by direct measurements of lengths of cracks associated with Vickers indentation flaws. The fracture toughness measurements based on (a) initial (as-indented) crack lengths, (b) equilibrium growth of cracks during increasing far-field loading, and (c) crack lengths corresponding to unstable fracture showed definitive trends of R -curves for both materials. The fracture mechanics analyses employed an indenter-material constant that was independently estimated using a physical model for the residual driving force and a free surface correction factor that accounted for the effects of size and shape of the cracks on stress intensity. It is shown that R -curve estimations based on crack length measurements have the intrinsic advantage that crack length dependence of fracture toughness is not assumed a priori as is done in conventional analysis based on strength. The measured fracture toughness of SiC(whisker)-reinforced alumina was in agreement with the prediction of a toughening model based on crack bridging by partially debonded whiskers.     

Energy and Trust Management Framework for MANET using Clustering Algorithm

In General, Mobile Ad-Hoc Network (MANET) has limited energy resources, and it cannot recharge itself. This research goal focuses on building a power management scheme that saves energy in the MANET. Due to power instability, there is a chance that cluster heads fail and function incorrectly in cluster-based routing. As a result, instability occurs with the cluster heads while collecting data and communicating with others effectively. This work focuses on detecting the unstable cluster heads, which are replaced by other nodes implementing the envisaged self-configurable cluster mechanism. A self-configurable cluster mechanism with a k-means protocol approach is proposed to designate cluster heads effectively. The proposed k-means procedure is based on periodic irregular cluster head rotations or altering the number of clusters. We also propose a trust management mechanism in this research to detect and avoid MANET vulnerabilities. Because of the continuously changing topology and limited resources (power, bandwidth, computing), the trust management algorithm should only use local data. Consequently, compared to traditional protocols, the proposed approach with the k-means procedure and its experimental results show lower power usage and provide an optimal system for trust management.

     

Synthesis and Characterization of Nanoparticles,Nanotubes, Nanopans,and Graphene-like Structures of Boron Nitride

By heating H₃BO₃ with urea in 1 : 6 molar ratio, nanoparticles and nanotubes of BN are obtained. The urea–boric acid reaction can also be exploited to obtain graphene analogues of BN, with the number of layers depending on the relative proportions of the two reactants. Synthesis with a high proportion of urea yields a product containing graphene analogues of BN with an average of 2 layers. The surface area of BN increases with the decreasing number of layers, and the high-surface-area BN also exhibits high CO₂ adsorption. Few-layer BN can be solubilized by interaction with Lewis bases. Nanopans and nanosheets formed by graphene-like BN are generated by the vapor phase reaction of NH₃ and BBr₃ at 1223 K. Nanopans of BN, being reported for the first time, have a bottom comprising single-layer BN and a wall of 0.7 nm height. The average inner volume of the nanopan is around 400 nm³.     

Enhanced photocatalytic activity of sulfur doped TiO2 for the decomposition of phenol: A new insight into the bulk and surface modification

Sulfur ion (S⁶⁺) was doped into the anatase TiO₂ prepared by sol–gel method (SG-TiO₂) using sulfur powder as a sulfur source (S-TiO₂) and its photoreactivity was probed for the degradation of phenol under UV/solar light illumination. The S-TiO₂ and SG-TiO₂ were characterized by PXRD, UV–vis DRS, FTIR, SEM, XPS, BET and PL techniques. It was observed that S⁶⁺ ion was incorporated into the TiO₂ crystal lattice at Ti⁴⁺ lattice site and the sulfur on the surface gets modified to ₄SO²⁻${{\text{SO}}_4}^{2-}$ due to the heat treatment under atmospheric conditions. The high photocatalytic activity of S-TiO₂ compared to SG-TiO₂ is attributed to the surface modification of sulfur as sulfate which plays a crucial role in trapping electrons. S-TiO₂ shows significant increase in the surface area, reduced crystallite size, increased surface acidity, visible light absorption and prolonged lifetime of the photogenerated charge carriers. Hole scavengers like potassium iodide and tertiary butanol suggested the surface degradation mechanism rather than the bulk degradation pathway. Addition of oxidizing agents to the degradation reaction did not show any enhancement in the degradation rates since the presence of ₄SO²⁻${{\text{SO}}_4}^{2-}$ on the TiO₂ surface itself acts as the efficient electron trapping centers. Both trapping and detrapping of the electron takes place more efficiently at ₄SO²⁻${{\text{SO}}_4}^{2-}$ centers. The enhanced activity of S-TiO₂ is attributed to the synergistic effect between S⁶⁺ dopant with surface modified ₄SO²⁻${{\text{SO}}_4}^{2-}$.     

A suboptimal controller for minimun sensitivity of closed loop eigenvalues to parameter variations

The design of a suboptimal controller for linear time invariant multivariable systems, which assigns the closed loop eigenvalues at desired locations and minimizes their sensitivity with respect to plant parameters, is outlined. The dominant open loop poles are shifted in groups employing a dyadic structure of state feedback for each group. Freedom available in choosing the constants of proportionality of the different rows of the dyadic matrices is used for eigenvalue sensitivity minimization. Since the final controller matrix is the sum of different dyadic matrices, the sensitivity of the final closed loop poles can be made less than that obtained by shifting all the dominant eigenvalues in one stage using unity rank feedback. An example illustrating the design method is given.     

Malicious Node Detection in Wireless Sensor Networks Using an Efficient Secure Data Aggregation Protocol

Wireless sensor networks are randomly deployed and responsible for monitoring geographical area wide. In WSN, the aggregation of data is very complex because of its limited power and computing capabilities. Issue in data aggregation is that the data may be passed on malicious node. All the existing data aggregation techniques undergo security issues because of the transfer of large amount of data. In this paper we propose a protocol named Secure Data Aggregation Protocol (SDAP) which identifies the malicious node by providing a logical group in the form of tree topology. In the tree topology the aggregation is formed by aggregating the nodes, which are non-leaf node and high level of trust is required to provide a better approximation and accuracy against the security threats. Thus the data is securely aggregated and the efficiency is achieved in data aggregation.

     

R-Curve Behavior and Flaw Insensitivity of Ce-TZP/Al2O3 Composite

A ceria-partially-stabilized zirconia-aiumina (Ce-TZP/Al₂O₃) composite optimized for transformation toughening was used to demonstrate its flaw insensitivity due to R -curve behavior. Four-point bend specimens fabricated with a controlled distribution of spherical pores showed nearly the same characteristic strength and strength variability (Weibull modulus) as specimens fabricated without the artificial pores. In situ observations confirmed stable growth of cracks initiated at pores and the crack lengths at fracture instability were much greater than the pore sizes, thus resulting in fracture strengths insensitive to the pores. The small variability in the fracture strength was found to be associated with variability in the R -curve and the instability crack lengths. An analysis based on the fracture instability criterion for rising crack growth resistance accounted for the strength variability due to variability in the R -curve. Comparable four-point bend experiments were also conducted on a sintered yttria-partially-stabilized zirconia (2Y-TZP) ceramic. This ceramic showed significant degradation of strength due to the presence of the pores. This flaw sensitivity is attributed to its steep rising R -curve over short crack lengths.     

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.     

Bulk versus surface state contributions to the efficiency of perovskite titanate photoanodes

Attempts have been made to enhance the photon efficiency of perovskite titanate electrodes during band gap illumination by increasing the bulk dielectric constant with the substitution of suitable isovalent ions in the lattice so that the Curie point is shifted to room temperature and by decreasing theN _D values with controlled chemical reduction. The photoresponse showed only marginal improvements with these changes in the bulk characteristics. In comparison, the efficiency increased considerably when the electrode surfaces are treated by exposing to dilute acids, particularly HNO₃ + HF, for extended periods of time. This is accompanied by the changes inV _on to cathodic direction, red shift in the spectral dependence of photoresponse and marginal decrease ofN _D in the space charge layer. X-ray photoelectron spectra show that the treated electrode surfaces are not uniform with respect to oxygen/titanium ratios, hydroxyls and fluoride incorporated. The enhanced photon efficiency arises from the combined effect of eliminating the undesirable electron recombination centres in the space charge layer and the presence of heterogeneous surface regions leading to non-uniform potential distribution near the electrode surface. The results also point to the limitations of applying Schottky barrier model for semiconductor/electrolyte interface with higher concentrations of surface states.