A smart integrated environment for vehicular traffic simulation

Globally, urbanization has increased due to the availability of the facilities, jobs, and better standard of living in cities. Increase in urban population has caused increase in urban commute. The increased pressure on urban settlements has made design and optimization of urban civil infrastructure very challenging especially road transport infrastructure. Domain experts such as transportation managers, engineers, scientist, and academics need effective, easy, and readily usable computational modeling and simulation tools to conduct systematic inquiry for design and optimization of the transportation system. Free and open‐source software (FOSS) community has developed many vehicular traffic simulation software that are cost‐effective and extensible, however are difficult to use. Usually, domain experts do not have hands‐on software skills such as XML‐based interfaces, software source versioning, and build management. To bridge this gap, in our work, an integrated development environment (IDE), namely, integrated vehicular traffic simulation environment (ITE), has been developed. ITE is developed on top of an existing FOSS multimodal open‐source vehicular‐traffic simulator (MovSim). ITE includes a scenario building tool for domain experts. It could be used to model road infrastructure and vehicular traffic in an integrated manner. The tool could be used to build and experiment novel traffic infrastructures and policies. Our work helps in reducing learning curve of domain experts by providing a free and open source, easy and readily usable vehicular traffic simulation tool.     

A hybrid intelligent system to improve predictive accuracy for cache prefetching

Cache being the fastest medium in memory hierarchy has a vital role to play for fully exploiting available resources, concealing latencies in IO operations, languishing the impact of these latencies and hence in improving system response time. Despite plenty of efforts made, caches alone cannot comprehend larger storage requirements without prefetching. Cache prefetching is speculatively fetching data to restrain all delays. However, effective prefetching requires a strong prediction mechanism to load relevant data with higher degree of accuracy. In order to ameliorate the predictive performance of cache prefetching, we applied the hybrid of two AI approaches named case based reasoning (CBR) and artificial neural networks (ANN). CBR maintains the past experience and ANN are used in adaptation phase of CBR instead of employing static rule base. The novelty of technique in this domain is valued due to hybrid of two approaches as well as usage of suffix tree in populating the CBR’s case base. Suffix trees provide rich data patterns for populating case base and greatly enhanced the overall performance. A number of evaluations from different aspects with varying parameters are presented (along with some findings) where the efficacy of our technique is affirmed with improved predictive accuracy and reduced level of associated costs.     

EkmEx - an extended framework for labeling an unlabeled fault dataset

Multimedia Tools and Applications - Software fault prediction (SFP) is a quality assurance process that identifies if certain modules are fault-prone (FP) or not-fault-prone (NFP). Hence, it...     

Energy-efficient data centers

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide ( $\mathrm{CO}_2$ ) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.     

Aspects of burr formation in bandsaw teeth manufactured by milling operation

Steel manufacturers and stockholders prefer bandsawing for cutting off raw materials compared to other techniques as it enjoys competitive advantages of higher accuracy of cut, better surface finish, lower kerf loss, better straightness of cut, long tool life and high metal removal rate. Along with the geometries of the bandsaw tooth, bandsaw cutting edge condition (e.g., edge sharpness and burr) significantly affects the cutting performance of a bandsaw. Currently the production of bandsaw is largely done by milling operation due to the scale of manufacturing and the economics of milling compared to other processes (e.g., grinding). Ideally, the bandsaw teeth should possess sharp cutting edges with no burr. In general, two types of burr are commonly seen in the bandsaw teeth manufactured by milling operation namely tooth tip burr and side burr. Current research undertaken at Northumbria University in collaboration with a major bandsaw producer is focused on the mechanism of burr formation in the bandsaw teeth. This paper briefly outlines the factors affecting the burr formation in bimetal (High Speed Steel edge wire and soft steel backing material) bandsaw teeth manufactured by milling process and suggests the necessary steps to be considered for manufacturing burr free bandsaw with sharp cutting edges. The investigation showed that flank wear in the milling cutter has a major influence on the side burr formation in the bandsaw teeth, whereas tooth tip burr was influenced by both flank wear and “V” type notch wear found at the crossover point on the flank face. It was also concluded that TiN coating on the milling cutter could control the burr formation in bandsaw teeth to some degree.     

Cotton fabric dyeability assessment of floral extracts obtained from binary mixtures of Callistemon citrinus and Tagetes erecta L

Parameters affecting extraction of natural colorants from five binary mixtures of floral parts of Callistemon citrinus and Tagetes erecta L., like ratio of each flower in the binary mixture of powdered floral parts, amount of powdered binary mixture to liquor ratio and extraction time, were optimized for dyeing of cotton fabric. Optimum dyeing was obtained at 60°C with 70 g/L exhausting agent (Na₂SO₄), material to liquor ratio of 1:50 along with dyeing time of 100 min. The dyed fabric revealed good anti-bacterial activity and excellent ultraviolet protection factor (UPF).     

Physicochemical Characterization of the Strawberry Samples on Regional Basis Using Multivariate Analysis

Six strawberry samples, commercially grown in Pakistan, were studied for physicochemical properties. All samples have high level of water and low contents of ash and volatile compounds. The berry was found to be a good source of potassium (1.14–1.93 g/kg), magnesium (0.12–0.15 g/kg), calcium (79.59–124.11 mg/kg) and sodium (23.51–36.03 mg/kg). The lead and cadmium contents were below the detection limits (1.0 ± 0.1 μg/l for lead and 0.1 ± 0.01 μg/l for cadmium). Principal component analysis and cluster analysis were found to be good statistical tools for the evaluation of the compositional variations with respect to geographical origin.     

Network selection in heterogeneous access networks simultaneously satisfying user profile and QoS

Multimode capability empowers mobile devices to select the appropriate network to meet the requirements of user and applications. However, network selection is a challenging task owing to heterogeneous nature of network access links. At the same time, pervasiveness of mobile communication networks accompanied by the advances in wireless devices has raised the user expectations of persistent service and quality. Therefore, it is important for a mobile device to take situation‐based and timely decisions while selecting an access network to ensure both user's and operator's demand. The existing standard, called IEEE 802.21 (media‐independent handover), considers mobile device signal‐strength parameter for network selection from a list of networks. This research has proposed a ranking algorithm to rank heterogeneous networks based on a set of parameters including user profile and QoS. It selects most suitable network owing to multiple handover scenarios. The simulation results have shown that the proposed scheme has successfully satisfied apparently the contradictory requirements related to user profiles and QoS simultaneously.     

Opportunistic channel allocation decision making in cognitive radio communications

The global spread of wireless devices with mobile Internet access and the increasing demand of multimedia‐based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built‐in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd.