Scalable l-Diversity

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

Internal and External Threat Analysis of Anonymized Dataset

In recent years, data collection and data mining have emerged as fast-paced computational processes as the amount of data from different sources has increased manifold. With the advent of such technologies, major concern is exposure of an individual's self-contained information. To confront the unusual situation, anonymization of dataset is performed before being released into public for further usage. The chapter discusses various existing techniques of anonymization. Thereafter, a novel redaction technique is proposed for generalization to minimize the overall cost (penalty) of the process being inversely proportional to utility of generated dataset. To validate the proposed work, authors assume a pre-processed dataset and further compare our algorithm with existing techniques. Lastly, the proposed technique is made scalable thus ensuring further minimization of generalization cost and improving overall utility of information gain.

Capacity factors of solar photovoltaic energy facilities in California, annual mean and variability

The capacity factors of the largest solar photovoltaic (PV) energy facilities of California are computed, based on a low-frequency monthly statistic that is covering the last few years. While the best-performing facilities achieve annual capacity factors of about 32-33%, the average annual capacity factor is less than 30%, at about 26-27%. The scattered information on costs suggests a cost penalty of 35% for a capacity factor gain of 10%. Higher frequency data of 1-minute or less for every facility connected to the same grid and the grid average energy supply are needed to define the energy storage indispensable to cover a given demand. The individual facility energy production requires to account for a cost associated with an energy storage allowance to every producer of intermittent and unpredictable electricity, with this amount inversely proportional to the annual average capacity factor and directly proportional to the standard deviation of the high-frequency capacity factors.

Study on Multi-Priority and Multi-Path QoS Routing Protocol for Airborne Heterogeneous Networks

In order to guarantee the QoS requirements of multiple services in airborne all-domain heterogeneous and flexible networks, we propose a novel multi-priority and multi-path based QoS routing (MP2R) protocol in this paper. In the protocol, in terms of the route effectiveness and reliability, the route cost penalty function is constructed based on the multi-priority M/M/1 queueingqueuing system with the preemptive-resume policy. The minimum value of the function is derived through the optimization theory, and the optimum routing solution is acquired. The simulation results show that the MP2R protocol not only has the capabilities of differentiation services for different kinds of traffic and QoS provision, but also can utilize the network resource rationally, avoid congestion, achieve the load balancing, and meet the requirements of airborne all-domain heterogeneous and flexible networks effectively.

Scalable l-Diversity

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.

The Advanced Effectveness And Efficiency of New Testing Analysis

Testing is the most widely used by the many software companies to develop the product or the project with most reliable. Testing will calculate the efficiency and effectiveness of the product to improve the performance of the project and work with proper way. Based on the testing results the product can be developed more effectively without showing any complications when the product is released. In this paper, the new integrated testing is developed to calculate the testing effectiveness and testing efficiency of the product at the time of releasing and at the time of development of the project. The existing system of this testing shows the early testing which is at the time of the development and the proposed system of the testing shows the late testing of the project i,e after the completion of the project. From the proposed system the cost, penalty, and benefit of a software during its development phase for finding the expected faults and predicted faults to release the product.

Numerical Modelling and Sensitivity Analysis of Natural Draft Cooling Towers

Cooling towers are a relatively inexpensive and consistent method of ejecting heat from several industries such as thermal power plants, refineries, and food processing. In this research, an earlier model from literature was to be validated across three different case studies. Unlike previous models, this model considers the height of the fill as the discretised domain, which produces results that give it in a distribution form along the height of the tower. As there are limitations with the software used (gPROMS) where differential equations with respect to independent variables in the numerator and denominator cannot be solved, a derivative of the saturation vapour pressure with respect to the temperature of the air was presented. Results shown were in agreement with the literature and a parametric sensitivity analysis of the cooling tower design and operating parameters were undertaken. In this work the height of fill, mass flowrates of water and air were studied with respect to sensitivity analysis. Results had shown large variations in the outlet temperatures of the water and air if the mass flows of water and air were significantly reduced. However, upon high values of either variable had shown only small gains in the rejection of heat from the water stream. With respect to the height of the fill, at larger heights of the fill, the outlet water temperature had reduced significantly. From a cost perspective, it was found that a change in the water flowrate had incurred the largest cost penalty with a 1 % increase in flowrate had increased the average operating cost by 1.2 %. In comparison, a change in air flowrate where a 1 % increase in flowrate had yielded an average of 0.4 % increase in operating cost.