A formal framework for scenario development in support of environmental decision-making

Scenarios are possible future states of the world that represent alternative plausible conditions under different assumptions. Often, scenarios are developed in a context relevant to stakeholders involved in their applications since the evaluation of scenario outcomes and implications can enhance decision-making activities. This paper reviews the state-of-the-art of scenario development and proposes a formal approach to scenario development in environmental decision-making. The discussion of current issues in scenario studies includes advantages and obstacles in utilizing a formal scenario development framework, and the different forms of uncertainty inherent in scenario development, as well as how they should be treated. An appendix for common scenario terminology has been attached for clarity. Major recommendations for future research in this area include proper consideration of uncertainty in scenario studies in particular in relation to stakeholder relevant information, construction of scenarios that are more diverse in nature, and sharing of information and resources among the scenario development research community.     

Quantitative analysis of packet-processing applications regarding architectural guidelines for network-processing-engine development

This paper presents a simulation-based profile-driven quantitative analysis of packet-processing applications. In this domain, demands for increasing the performance and the ongoing development of network protocols both call for flexible and performance-optimized engines. Based on the achieved profiling results, we introduce platform-independent analysis that locates the performance bottlenecks and architectural challenges of a packet-processing engine. Finally based on these results, we extract helpful architectural guidelines for design of a flexible and high-performance embedded processor that is optimized for packet-processing operations in high-performance and cost-sensitive network embedded applications.     

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

A single-phase p-type ac–ac converter with reduced components count and high boost factor

This paper proposes a new single-phase direct step-up ac–ac converter by modifying the p-type impedance source. It provides a high boost factor as well as high efficiency, while only six parts are required to design it, involving just two bidirectional power switches. A safe commutation method has been applied to power switches to make the converter snubber-free and high efficient. Input and output harmonic filters are no longer required since input and output currents variate continuously with small ripple and low total harmonic distortion (THD). The proposed topology only modulates the output voltage amplitude, not the phase and frequency, so the output frequency is identical to the input frequency and constant. Thus, it can be utilized in step-up conversion applications, like inductive power transmission from low ac voltage sources. Input and output have the same ground, which is a good protective feature. In this paper, the operating principle of the converter is demonstrated. Experimental results have been represented to evaluate the performance of the converter. For this purpose, an experimental prototype has been fabricated. Results are investigated and compared with other previous step-up ac–ac converters. Results confirm the theory, operating principle, and performance of the converter.     

Effective participation of wind turbines in frequency control of a two-area power system using coot optimization

In this paper, load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources. A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances. An auxiliary storage controller is applied to achieve effective frequency response. The coot optimization algorithm (COA) is applied to allocate the optimum parameters of the fractional-order proportional integral derivative (FOPID), droop and auxiliary storage controllers. The fitness function is represented by the summation of integral square deviations in tie line power, and Areas 1 and 2 frequency errors. The robustness of the COA is proven by comparing the results with benchmarked optimizers including: atomic orbital search, honey badger algorithm, water cycle algorithm and particle swarm optimization. Performance assessment is confirmed in the following four scenarios: (i) optimization while including PID controllers; (ii) optimization while including FOPID controllers; (iii) validation of COA results under various load disturbances; and (iv) validation of the proposed controllers under varying weather conditions.     

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model. The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process. Besides, Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver. Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors. At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively. In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures. The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.