Deposition and characterization of alumina films produced by combustion flame pyrolysis

Alumina coatings have been deposited by combustion flame pyrolysis on amorphous silica and stainless steel substrates with the objective to study the effect of solvent composition, Fe³⁺ addition and determine the thermal fatigue lifetime. The effect of solvent composition on crystallinity, transformation temperature and hardness are studied, three different solvent compositions are chosen for preparing the aluminium nitrate solution. Using 100% water, as-deposited films are amorphous and transform to α-alumina only upon annealing, while this equilibrium phase is directly obtained but with a porous microstructure by using 100% methanol. The hardness of the coatings varies with the flammability of the solvents. The effect of Fe³⁺ addition on the crystallization of alumina is studied by combustion pyrolysis of aqueous solutions of Al³⁺ and Fe³⁺ nitrates. Small amount of ferric nitrate reduced the transformation temperature by 100 °C. Thermal cycling of as-deposited amorphous alumina on stainless steel substrate is carried out at different temperatures to determine the thermal fatigue lifetime.     

Biowaste-derived hydroxyapatite reinforced with polyvinyl pyrrolidone/aloevera composite for biomedical applications

The present investigation focuses on the synthesis of crabshell-derived hydroxyapatite (CS-HAP)/ water-soluble synthetic polymer—polyvinylpyrrolidone(PVP)/aloevera(AV)—a natural biopolymer, as a composite for enhanced mechanical, antibacterial and biocompatible properties. The reinforcement of polymer has a significant function in increasing the mechanical property of the composite, whereas the incorporation of AV improves the antibacterial and biocompatibility. Phase composition, morphology, mechanical property, and hydrophilicity of CS-HAP/PVP/AV biocomposite with different concentrations of PVP and AV were examined by Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), scanning electron microscopy with energy dispersive X-ray (SEM-EDX), Vickers microhardness tests, contact angle, respectively. Furthermore, the antibacterial efficiency of the composite is assessed using Escherichia coli (E coli) and Staphylococcus aureus (S aureus). The biocompatibility of HOS MG 63 cells on the CS-HAP/PVP/AV composite is evaluated by MTT assay test. The obtained results evidence that the as-synthesized composite have appropriate mechanical, antibacterial and biocompatible properties. Overall, the combination of mechanical property of PVP, antibacterial and biocompatible property of AV in CS-HAP/PVP/AV, makes the composite a potential therapeutic material for various biomedical applications.     

Formation of Zinc–Antimony-Based Spinel Phases

Formation of spinel phases in ZnO–Sb₂O₃and ZnO–Sb₂O₃–Bi₂O₃systems is studied by the use of X-ray diffraction. The formation of nonstoichiometric Zn_2.33Sb_0.67O₄phase is observed in both the systems at ∼900°C. However, in these systems, at higher temperatures ( T ≥ 1100°C), formation of the inverse spinel phase Zn₇Sb₂O₁₂is observed. The study has been extended to understand the effect of CrO₃doping on the stability of the different spinel phases in the previously mentioned systems. Interestingly, in both the systems, samples doped with CrO₃, displayed the presence of Zn_2.33Sb_0.67O₄phase <1200°C, indicating the stabilization of the spinel phase by CrO₃.     

Dynamic Matrix Rank with Partial Lookahead

We consider the problem of maintaining information about the rank of a matrix M under changes to its entries. For an n×n matrix M, we show an amortized upper bound of O(n ^ω?1) arithmetic operations per change for this problem, where ω<2.373 is the exponent for matrix multiplication, under the assumption that there is a lookahead of up to Θ(n) locations. That is, we know up to the next Θ(n) locations (i ₁,j ₁),(i ₂,j ₂),…?, whose entries are going to change, in advance; however we do not know the new entries in these locations in advance. We get the new entries in these locations in a dynamic manner. The dynamic matrix rank problem was first studied by Frandsen and Frandsen who showed an upper bound of O(n ^1.575) and a lower bound of Ω(n) for this problem and later Sankowski showed an upper bound of O(n ^1.495) for this problem when allowing randomization and a small probability of error. These algorithms do not assume any lookahead. For the dynamic matrix rank problem with lookahead, Sankowski and Mucha showed a randomized algorithm (with a small probability of error) that is more efficient than these algorithms.     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient features from the data set, we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions. For selecting spatial and spectral features we have studied three different approaches namely overlapping window (OW-3DFS), non-overlapping window (NW-3DFS) adaptive window cube (AW-3DFS) and Pixel based technique. Fivefold Multiclass Support Vector Machine (MSVM) is used for classification purpose. Experiments conducted on Madurai LISS IV multispectral image exploited that the adaptive window approach is used to increase the classification accuracy.     

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Algorithms to Compute Minimum Cycle Basis in Directed Graphs

We consider the problem of computing a minimum cycle basis in a directed graph G with m arcs and n vertices. The arcs of G have non-negative weights assigned to them. In this problem a {-1,0,1} incidence vector is associated with each cycle and the vector space over generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of weights of the cycles is minimum is called a minimum cycle basis of G. This paper presents an algorithm, which is the first polynomial-time algorithm for computing a minimum cycle basis in G. We then improve it to an algorithm. The problem of computing a minimum cycle basis in an undirected graph has been well studied. In this problem a {0,1} incidence vector is associated with each cycle and the vector space over generated by these vectors is the cycle space of the graph. There are directed graphs in which the minimum cycle basis has lower weight than any cycle basis of the underlying undirected graph. Hence algorithms for computing a minimum cycle basis in an undirected graph cannot be used as black boxes to solve the problem in directed graphs.