An Energy Efficient Architecture for IoT Based Automated Smart Micro-Grid

The concept of smart grid is getting more and more attention for efficient energy generation and distribution. There is a need to reduce the energy consumption by efficiently utilizing the resources. Smart gird originates the research in a number of associated applications. These include energy consumption, minimization, database efficiency and efficient communication infrastructure. In this article, we proposed architecture for optimizing the usage of energy resources by effectively utilizing the Renewable Energy (RE) resources. The proposed architecture utilizes Internet of Things paradigms for collecting the power consumption profile of heterogeneous devices. Based on obtained information, a schedule is generated and distributed by the Micro-Grid for certain devices. The analysis shows the efficiency of proposed architecture by reducing the cost of electricity purchased from the external sources. Finally, the realization of proposed architecture for various robotics applications is explained

IMPROVEMENT OF DATA ANALYSIS BASED ON K-MEANS ALGORITHM AND AKMCA

Data analysis is improved using the k-means algorithm and AKMCA. Data mining aims to extract information from a large data set and transform it into a functional structure. Exploratory data analysis and data mining applications rely heavily on clustering. Clustering is grouping a set of objects so that those in the same group (called a cluster) are more similar to those in other groups (clusters). There are various types of cluster models, such as connectivity models, distribution models, centroid models, and density models. Clustering is a technique in data mining in which the set of objects is classified as clusters. Clustering is the most important aspect of data mining. The algorithm makes use of the density number concept. The high-density number point set is extracted from the original data set as a new training set, and the point in the high-density number point set is chosen as the initial cluster centre point. The basic clustering technique and the most widely used algorithm is K-means clustering. K-Means, a partition-based clustering algorithm, is widely used in many fields due to its efficiency and simplicity. However, it is well known that the K-Means algorithm can produce suboptimal results depending on the initial cluster centre chosen. It is also referred to as Looking for the nearest neighbours. It simply divides the datasets into a specified number of clusters. Numerous efforts have been made to improve the K-means clustering algorithm’s performance. Advanced k-mean clustering algorithm (AKMCA) is used in data analysis to obtain useful knowledge of various optimisation and classification problems that can be used for processing massive amounts of raw and unstructured data. Knowledge discovery provides the tools needed to automate the entire data analysis and error reduction process, where their efficacy is investigated using experimental analysis of various datasets. The detailed experimental analysis and a comparison of proposed work with existing k-means clustering algorithms. Furthermore, it provides a clear and comprehensive understanding of the k-means algorithm and its various research directions

Persistent post-surgical pain following breast cancer surgery: An observational study in a tertiary care hospital

Objective: To determine the frequency of persistent pain in patients after breast cancer surgery, and to assess the distribution and characteristics of pain in such patients.Methods: The prospective observational single cohort study was conducted at the Department of Anaesthesiology and in the Breast Clinic of the Department of Surgery, Aga Khan University Hospital, Karachi, from August 2016 to January 2017, and comprised adult female patients with biopsy-proven carcinoma of breast who were scheduled for elective definitive breast cancer surgery. The patients were followed up for up to three months post-surgery and those with persistent pain were followed up for six months post-operation. Data was analysed using SPSS 19.Results: Of the 120 patients, 26(21.7%) developed persistent post-surgical pain for up to three months, while in 17(14.2%) patients, the pain continued for up to six months after the operation. Among those with persistent post-surgical pain, 11(42.3%) had burning pain, 10(38.5%) had throbbing pain, 3(11.5%) had numbness and 2(7.7%)had mixed character of pain. Also, 11(42.3%)patients developed pain at more than one site including axilla, chest wall, upper arm and surgical scar area, and the site of pain in majority patients 15(57.7%) was axilla.Conclusion: The incidence of persistent pain following breast cancer surgery was found to be 21.7%

Islamic Versus Conventional Banking: An Insight into the Malaysian Dual Banking System

This research aims at examining to match the performance of both Malaysian Islamic and conventional banking through profitability, efficiency, solvency and liquidity and risk management ratios using independent t-test and discriminant regression models. Fifteen financial ratios are applied to examine the competitiveness of the both industries created on the financial data of ten Malaysian banks, five from both industries, over the period of six financial years (2009-2015). According to the independent t-test descriptive statistics, the result finds that conventional banks perform well than Islamic banks in the context profitability and efficacy ratios. Nevertheless, in terms of solvency and liquidity & risk management ratios Islamic banks outperform conventional banks operating in Malaysia. Further, it has been revealed by the disciriminant analysis that in general conventional banks execute well than Islamic banks operating in Malaysia when it comes to the profitability, solvency, efficiency and liquidity & risk management ratios

Nanopharmaceuticals: A Boon to the Brain-Targeted Drug Delivery

Brain is well known for its multifarious nature and complicated diseases. Brain consists of natural barriers that pose difficulty for the therapeutic agents to reach the brain tissues. Blood-brain barrier is the major barrier while blood-brain tumor barrier, blood-cerebrospinal (CSF) barrier and efflux pump impart additional hindrance. Therapeutic goal is to achieve a considerable drug concentration in the brain tissues in order to obtain desired therapeutic outcomes. To overcome the barriers, nanotechnology was employed in the field of drug delivery and brain targeting. Nanopharmaceuticals are rapidly emerging sub-branch that deals with the drug-loaded nanocarriers or nanomaterials that have unique physicochemical properties and minute size range for penetrating the CNS. Additionally, nanopharmaceuticals can be tailored with functional modalities to achieve active targeting to the brain tissues. The magic behind their therapeutic success is the reduced amount of dose and lesser toxicity, whereby localizing the therapeutic agent to the specific site. Different types of nanopharmaceuticals like polymeric, lipidic and amphiphilic nanocarriers were administered into the living organisms by exploiting different routes for improved targeted therapy. Therefore, it is essential to throw light on the properties, mechanism and delivery route of the major nanopharmaceuticals that are employed for the brain-specific drug delivery

Performance Evaluation of UK Acquiring Companies in the Pre and Post-Acquisitions Periods

This paper has two objectives: first, it examines the financial performance of twenty UK based acquiring companies over the period of five years (2009-2013) using financial ratios of Liquidity, Profitability and Solvency in order to empirically determine whether there is any significant financial performance changes in the operation of the underlying companies as a result of acquisitions. Both average ratio and paired t-test analysis have been conducted. The analysis concludes that none of the ratios proved statistical significance which shows that the underlying acquisitions did not influence changes in the financial performance of the acquiring companies. The paper also examines whether shareholders make short-term gain while opting for acquisitions by analyzing stocks return over 58 days window period i.e. 29 days prior to acquisition announcement and 29 days after acquisition announcement by applying CAPM model and AAR and CAAR analysis. The analysis concludes that none of the results show statistical significance which further asserts that UK shareholders do not make gain in the short-term as a result of the acquisition activities they have undertaken

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

© 2017 Pervez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations