Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

A hybrid intelligent system and its application to fault detection and diagnosis

This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.

Visualizing and classifying data using a hybrid intelligent system

In this paper, a hybrid intelligent system that integrates the SOM (Self-Organizing Map) neural network, kMER (kernel-based Maximum Entropy learning Rule), and Probabilistic Neural Network (PNN) for data visualization and classification is proposed. The rationales of this Probabilistic SOM-kMER model are explained, and its applicability is demonstrated using two benchmark data sets. The results are analyzed and compared with those from a number of existing methods. Implication of the proposed hybrid system as a useful and usable data visualization and classification tool is discussed.

Coupled RNA polymerase II transcription and 3′ end formation with yeast whole-cell extracts

 RNA polymerase II (RNAP II) transcription and pre-mRNA 3' end formation are linked through physical and functional interactions. We describe here a highly efficient yeast in vitro system that reproduces both transcription and 3' end formation in a single reaction. The system is based on simple whole-cell extracts that were supplemented with a hybrid Gal4-VP16 transcriptional activator and supercoiled plasmid DNA templates encoding G-less cassette reporters. We found that the coupling of transcription and processing in vitro enhanced pre-mRNA 3' end formation and reproduced requirements for poly(A) signals and polyadenylation factors. Unexpectedly, however, we show that in vitro transcripts lacked m⁷G-caps. Reconstitution experiments with CF IA factor assembled entirely from heterologous components suggested that the CTD interaction domain of the Pcf11 subunit was required for proper RNAP II termination but not 3' end formation. Moreover, we observed reduced termination activity associated with extracts prepared from cells carrying a mutation in the 5'-3' exonuclease Rat1 or following chemical inhibition of exonuclease activity. Thus, in vitro transcription coupled to pre-mRNA processing recapitulates hallmarks of poly(A)-dependent RNAP II termination. The in vitro transcription/processing system presented here should provide a useful tool to further define the role of factors involved in coupling.

Space-crossing : community-based data forwarding in mobile social networks under the hybrid communication architecture

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

A novel operation and control strategy for a standalone hybrid renewable power system

This paper proposes a novel operation and control strategy for a renewable hybrid power system for a standalone operation. The proposed hybrid system consists of a wind turbine, a fuel cell, an electrolyzer, a battery storage unit, and a set of loads. The overall control strategy is based on a two-level structure. The top level is the energy management and power regulation system. Depending on wind and load conditions, this system generates reference dynamic operating points to low level individual subsystems. The energy management and power regulation system also controls the load scheduling operation during unfavorable wind conditions under inadequate energy storage in order to avoid a system blackout. Based on the reference dynamic operating points of the individual subsystems, the local controllers control the wind turbine, fuel cell, electrolyzer, and battery storage units. The proposed control system is implemented in MATLAB Simpower software and tested for various wind and load conditions. Results are presented and discussed.

Application of a hybrid MBR system to treat herbicides from agricultural runoffs

Agricultural discharge of herbicides to the Great Barrier Reef (GBR) poses significant threat to the marine ecosystem. This study evaluates the performance of a hybrid treatment system consists of a membrane bioreactor (MBR), UV disinfection unit and a granular activated carbon (GAC) column in treating Ametryn which is one of the major herbicides in agricultural discharges. While the MBR alone removes only 40% of Ametryn at a hydraulic retention time of 7.8 hours, the hybrid system removed Ametryn to below detection levels.

Treatment of ametryn in wastewater by a hybrid MBR system : a lab-scale study

Agricultural discharge of herbicides to the Great Barrier Reef (GBR) poses significant threat to the marine ecosystem. This study evaluates the performance of a hybrid treatment system consists of a membrane bioreactor (MBR), UV disinfection unit and a granular activated carbon (GAC) column in treating ametryn which is one of the major herbicides in agricultural discharges. While the MBR alone removes only 40% of ametryn at a hydraulic retention time of 7.8 h, the hybrid system removed ametryn to below detection levels.

Polyglutamine tracts as modulators of transcriptional activation from yeast to mammals

 Microsatellite repeats are genetically unstable and subject to expansion and shrinkage. A subset of them, triplet repeats, can occur within the coding region and specify homomeric tracts of amino acids. Polyglutamine (polyQ) tracts are enriched in eukaryotic regulatory proteins, notably transcription factors, and we had shown before that they can contribute to transcriptional activation in mammalian cells. Here we generalize this finding by also including evolutionarily divergent organisms, namely, Drosophila and baker's yeast. In all three systems, Gal4-based model transcription factors were more active if they harbored a polyQ tract, and the activity depended on the length of the tract. By contrast, a polyserine tract was inactive. PolyQs acted from either an internal or a C-terminal position, thus ruling out a merely structural 'linker' effect. Finally, a two-hybrid assay in mammalian cells showed that polyQ tracts can interact with each other, supporting the concept that a polyQ-containing transcription factor can recruit other factors with polyQ tracts or glutamine-rich activation domains. The widespread occurrence of polyQ repeats in regulatory proteins suggests a beneficial role; in addition to the contribution to transcriptional activity, their genetic instability might help a species to adapt to changing environmental conditions in a potentially reversible manner.

A Physarum-inspired multi-agent system to solve maze

Physarum Polycephalum is a primitive unicellular organism. Its foraging behavior demonstrates a unique feature to form a shortest path among food sources, which can be used to solve a maze. This paper proposes a Physarum-inspired multi-agent system to reveal the evolution of Physarum transportation networks. Two types of agents – one type for search and the other for convergence – are used in the proposed model, and three transition rules are identified to simulate the foraging behavior of Physarum. Based on the experiments conducted, the proposed multiagent system can solve the two possible routes of maze, and exhibits the reconfiguration ability when cutting down one route. This indicates that the proposed system is a new way to reveal the intelligence of Physarum during the evolution process of its transportation networks.

ANGEL: agent-based scheduling for real-time tasks in virtualized clouds

The success of cloud computing makes an increasing number of real-time applications such as signal processing and weather forecasting run in the cloud. Meanwhile, scheduling for real-time tasks is playing an essential role for a cloud provider to maintain its quality of service and enhance the system's performance. In this paper, we devise a novel agent-based scheduling mechanism in cloud computing environment to allocate real-time tasks and dynamically provision resources. In contrast to traditional contract net protocols, we employ a bidirectional announcement-bidding mechanism and the collaborative process consists of three phases, i.e., basic matching phase, forward announcement-bidding phase and backward announcement-bidding phase. Moreover, the elasticity is sufficiently considered while scheduling by dynamically adding virtual machines to improve schedulability. Furthermore, we design calculation rules of the bidding values in both forward and backward announcement-bidding phases and two heuristics for selecting contractors. On the basis of the bidirectional announcement-bidding mechanism, we propose an agent-based dynamic scheduling algorithm named ANGEL for real-time, independent and aperiodic tasks in clouds. Extensive experiments are conducted on CloudSim platform by injecting random synthetic workloads and the workloads from the last version of the Google cloud tracelogs to evaluate the performance of our ANGEL. The experimental results indicate that ANGEL can efficiently solve the real-time task scheduling problem in virtualized clouds.

A hybrid metaheuritic technique developed for hourly load forecasting

Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short-term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two-stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems.

Tanis: a link between type 2 diabetes and inflammation?

Here we describe a novel protein, which we have named Tanis, that is implicated in type 2 diabetes and inflammation. In Psammomys obesus, a unique polygenic animal model of type 2 diabetes and the metabolic syndrome, Tanis is expressed in the liver in inverse proportion to circulating glucose (P = 0.010) and insulin levels (P = 0.004) and in direct proportion with plasma triglyceride concentrations (P = 0.007). Hepatic Tanis gene expression was markedly increased (3.1-fold) after a 24-h fast in diabetic but not in nondiabetic P. obesus. In addition, glucose inhibited Tanis gene expression in cultured hepatocytes (P = 0.006) as well as in several other cell types (P = 0.001–0.011). Thus, Tanis seems to be regulated by glucose and is dysregulated in the diabetic state. Yeast-2 hybrid screening identified serum amyloid A (SAA), an acute-phase inflammatory response protein, as an interacting protein of Tanis, and this was confirmed by Biacore experiments. SAA and other acute-phase proteins have been the focus of recent attention as risk factors for cardiovascular disease, and we contend that Tanis and its interaction with SAA may provide a mechanistic link among type 2 diabetes, inflammation, and cardiovascular disease.

Hybrid methods to select informative gene sets in microarray data classification

One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)–are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.

Sport/cultural events : public and private perceptions of the hybrid event

This paper explores the public and private perceptions of events that amalgamate two different themes into one unified event. In this paper, we refer to this as a hybrid event. The paper is set within the context of Melbourne, Australia, where two hybrid events (specifically a sport/culture event) were delivered in 2006 and 2007. Media reports about the 2007 event were analyzed to capture the public perception of the sport/cultural event, and focus group data, collected from attendees of the events, were analyzed to explore the private perceptions of the hybrid event. The results indicate that there are a range of views about the sport/cultural event, which are not always consistent. The findings of this study indicate that the hybrid event has the capacity to wade through a cluttered marketplace, but that it does require strong branding to position itself in the marketplace for competitive advantage.