Double Truncated Transmuted Fréchet Distribution: Properties and Applications

In this paper, we modify the Mahmoud and Mandouh (2013) model by adopting double truncation technique. It is referred to as Double Truncated Transmuted Fréchet (DTTF) distribution. Diverse probabilistic and reliability measures are developed and discussed. The MLEs of parameters are derived and a simulation study is also made. The DTTF distribution is modeled by two real-time datasets and supportive rationalized results provide the evidence that DTTF distribution is a reasonably better fit model than its competing models. Keywords: Fréchet Distribution, Double Truncation, Hazard Function, Moments, MLE, Quadratic Rank Transmutation Map (QRTM), Rényi entropy, Order Statistics. DOI: 10.7176/MTM/9-3-02 Publication date: March 31st 201

Study of Gc-ms And Hplc Characterized Metabolic Compounds in Guava (Psidium Guajava L.) Leaves

Psidium guajava leaves are rich source of nutrients, antioxidants, phytoconstituents and biological active compounds. The study was designed to elucidate secondary metabolites like alkaloids, saponins, flavonoids, tannins and glycosides in extracts of guava leaves through Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) by qualitative as well as quantitative procedures. These metabolites were further tested for their antimicrobial potential against two-gram positive (Bacillus subtilis and Staphylococcus aureus) and two-gram negative (Escherichia coli and Pasteurella multocida) bacteria and three pathogenic fungal strains (Asprrgillus niger, Fusarium solani and Aspergillus flavus). GC-MS analysis revealed the presence of major constituents like Ca- Caryophyllene (22.70%), α cubebene (11.2%) and alpha Humulene (5.91%). The ethyl-acetate, methanol, n-hexane and chloroform extracts were tested for antibacterial and antifungal activities against above mentioned microbes. Among all the tested solvent extracts, Chloroform and ethyl acetate extracts of P. guajava demonstrated more sensitivity towards the growth of B. subtilis and P. multocida with MIC of 230±3.02, 316. ±6.2 and 237±5.09 and 288±1.55 μg/ml, respectively. Methanolic extracts showed higher MIC against S. aureus (233±5.51 μg/ml) and E. coli (192±2.05 μg/ml), respectively. The findings of this current study would provide the way to use guava as a potential therapeutic agent to combat antimicrobial and antifungal resistance

Evaluation of Antibiotic Resistance and Virulence Genes among Clinical Isolates of Pseudomonas aeruginosa from Cancer Patients

Objectives: The objectives of this study were to evaluate P. Aeruginosa isolates from cancer patients for the phenotypic pattern of antibiotic resistance and to detect the gene responsible for virulence as well as antibiotic resistance. Methods: A total of 227 P. aeruginosa isolates were studied and 11 antibiotics were applied for susceptibility testing. PCR detection of the genes BIC, TEM, IMP, SPM, AIM, KPC, NDM, GIM, VIM, OXA, toxA and oprI was done. Finally, the carbapenem resistant isolates were tested for phenotypic identification of carbapenemase enzyme by Modified Hodge test. Results: The results showed that the isolates were resistant to imipenem (95%), cefipime (93%), meropenem (90%), polymixin B (71%), gentamicin (65%), ciprofloxacin (48%), ceftazidime (40%), levofloxacin (39%), amikacin (32%), tobramycin (28%) and tazobactum (24%). The PCR detection of the carbapenem resistant genes showed 51% isolates were positive for IMP, GIM and VIM, 38% for AIM and SPM, 30% for BIC, 20% for TEM and NDM, 17% for KPC and 15% for OXA. However, toxA and oprI genes were not detected. 154 carbapenem resistant isolates were found positive phenotypically for carbapenemase enzyme identification by Modified Hodge test. Conclusion: The co-existence of multiple drug-resistant bodies and virulent genes has important implications for the treatment of patients. This study provides information about treating drug-resistant P. Aeruginosa and the relationship of virulent genes with phenotypic resistance patterns

Potential impact of microbial consortia in biomining and bioleaching of commercial metals

Biomining is the use of microorganisms for the commercial extraction of lavish metals from ores and mines with least effect on environment. Microbes play vital role in bioleaching procedures in commercial mining. The bacterial cells are used to detoxify/replace waste cyanide, marginal biomass and activated carbon. These methods are preferred over conventional techniques due to energy efficient, low cost, environment friendly and production of useful by-products. At industrial scale, different microbial strains (Acidophilic, Sulphobacillus, Rhodococcus, Ferrimicrobium &chemolithotrophic) are deployed to boost the processes of copper and uranium bioleaching. About 20% of the world’s copper is extracted by using this technique. These extraction procedures involve oxidation of insoluble metal sulphides to soluble sulphates. The isolation of thermophilic microbes for mineral biooxidation increase the commercial extraction of minerals at industrial scale. The conventional pyrometallurgical techniques have environmental concerns as they result in depletion of high grade ores and release harmful gaseous. The microbe-assisted gold mining is expected to double the yield of gold and needs to be fully explored using diverse array of microbes. Bioleaching is simple and low cost method for the developing countries with large ore deposits. About 30 strains of microbes have been discovered so for with potential impact on bioleaching. With advances in molecular genetics, physiology and microbial genomics, more promising strains with increased bioactivities are possible. Further efforts are underway to culture diverse range of archaea and improving its genetic potential to be used as industrial tool for commercial bioleaching. The currents review enlightens the recent trends in biomining/bioleaching and implementation of modern biological approaches to engineer target microbes for commercial use

Cloning and expression of truncated Spike (S-f200) Glycoprotein of Infectious Bronchitis Virus (IBV) in Escherichia coli, and its immunogenicity to mice

Complete S1 gene of the Infectious Bronchitis Virus (IBV) was amplified and cloned into transfer vector. Truncated S1 gene designated as Sf200 (containing five antigenic sites located at 24–61, 291–398 and 497–543 amino acid residues of S1 glycoprotein) were amplified by overlap PCR, cloned into prokaryotic expression vector resulting pET-Sf200 and confirmed the construct by sequencing. The recombinant plasmid was identified by restriction enzyme and sequencing analysis. The in vitro expression of the truncated protein was analyzed in E. coli with a molecular weight of 38kDa determined through SDSPAGE and confirmed by Western blotting. The recombinant truncated protein was then purified from the culture media. The immunogenicity of the protein was studied in an animal experiment on mice, in which mice were injected subcutaneously. These findings suggest that the truncated Sf200 expressed in the pET- 32a (+) prokaryotic vector can be used as antigen to detect antibodies against IBV

A context-aware encryption protocol suite for edge computing-based IoT devices

Heterogeneous devices are connected with each other through wireless links within a cyber physical system. These devices undergo resource constraints such as battery, bandwidth, memory and computing power. Moreover, the massive interconnections of these devices result in network latency and reduced speed. Edge computing offers a solution to this problem in which devices transmit the preprocessed actionable data in a formal way, resulting in reduced data traffic and improved speed. However, to provide the same level of security to each piece of information is not feasible due to limited resources. In addition, not all the data generated by Internet of things devices require a high level of security. Context-awareness principles can be employed to select an optimal algorithm based on device specifications and required information confidentiality level. For context-awareness, it is essential to consider the dynamic requirements of data confidentiality as well as device available resources. This paper presents a context-aware encryption protocol suite that selects optimal encryption algorithm according to device specifications and the level of data confidentiality. The results presented herein clearly exhibit that the devices were able to save 79% memory consumption, 56% battery consumption and 68% execution time by employing the proposed context-aware encryption protocol suite

A resilience-oriented bidirectional anfis framework for networked microgrid management

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test

Synthetic Studies towards New 22(S)-Hydroxycholesterol Analogues

Liver X receptor is a transcription factor that is important for the metabolism of fat and cholesterol in the body. Known liver X receptor activators are endogenous oxysterols (cholesterol metabolites). 22S-hydroxycholesterol is a synthetic oxysterol which acts as an antagonist reducing production of fat but also increasing glucose uptake in skeletal muscle cells. A variety of chiral and achiral compounds have been prepared in an effort to be utilized towards synthesis of new 22S-hydroxycholesterol analogues. The different syntheses have been optimized with yields ranging from 9- 91%. Two stereogenic compounds generated are new which have not heretofore been described in the literature. The developed process described in this master thesis has given the desired stereochemistry, which has been confirmed by X-ray crystallography. In this context, it has been established a method of providing high-quality crystals of the syn aldol adduct. Several attempts to synthesize two modulators of LXR have been investigated, but proven difficult and unsuccessful. Alkylation of the stereogenic diol with two naphatyl derivatives to obtain a new analogue has been studied at various temperatures and reaction conditions. Second, the synthesis of another analogue produced multiple products indicating a high reactivity of the stereogenic diol

Double Auction used Artificial Neural Network in Cloud Computing

Double auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtual machines. Analyzing the performance of DA algorithm with machine learning algorithms is to predict the bidding price for both buyers and sellers. Therefore, we have implemented several machine learning algorithms and observed their performance on the bases of accuracy, such as linear regression (83%), decision tree regressor (77%), random forest (82%), gradient boosting (81%), and support vector regressor (90%). In the end, we observed that the Artificial Neural Network (ANN) provided an astonishing result. ANN has provided 97% accuracy in predicting bidding prices in DA compared to all other learning algorithms. It reduced the wastage of resources (VMs attributes) and soared both users' profits (buyers & sellers). Different types of models were analyzed on the bases of individual parameters such as accuracy. In the end, we found that ANN is effective and valuable for bidding prices for both users