Inter layer and cooperative design strategies for green mobile networks

The promise of a truly mobile experience is to have the freedom to roam around anywhere and not be bound to a single location. However, the energy required to keep mobile devices connected to the network over extended periods of time quickly dissipates. In fact, energy is a critical resource in the design of wireless networks since wireless devices are usually powered by batteries. Furthermore, multi-standard mobile devices are allowing users to enjoy higher data rates with ubiquitous connectivity. However, the bene ts gained from multiple interfaces come at a cost in terms of energy consumption having profound e ect on the mobile battery lifetime and standby time. This concern is rea rmed by the fact that battery lifetime is one of the top reasons why consumers are deterred from using advanced multimedia services on their mobile on a frequent basis. In order to secure market penetration for next generation services energy e ciency needs to be placed at the forefront of system design. However, despite recent e orts, energy compliant features in legacy technologies are still in its infancy, and new disruptive architectures coupled with interdisciplinary design approaches are required in order to not only promote the energy gain within a single protocol layer, but to enhance the energy gain from a holistic perspective. A promising approach is cooperative smart systems, that in addition to exploiting context information, are entities that are able to form a coalition and cooperate in order to achieve a common goal. Migrating from this baseline, this thesis investigates how these technology paradigm can be applied towards reducing the energy consumption in mobile networks. In addition, we introduce an additional energy saving dimension by adopting an interlayer design so that protocol layers are designed to work in synergy with the host system, rather than independently, for harnessing energy. In this work, we exploit context information, cooperation and inter-layer design for developing new energy e cient and technology agnostic building blocks for mobile networks. These technology enablers include energy e cient node discovery and short-range cooperation for energy saving in mobile handsets, complemented by energy-aware smart scheduling for promoting energy saving on the network side. Analytical and simulations results were obtained, and veri ed in the lab on a real hardware testbed. Results have shown that up to 50% energy saving could be obtained.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Discovery of a New Retrograde Trans-Neptunian Object: Hint of a Common Orbital Plane for Low Semimajor Axis, High-inclination TNOs and Centaurs

Although the majority of Centaurs are thought to have originated in the scattered disk, with the high-inclination members coming from the Oort cloud, the origin of the high-inclination component of trans-Neptunian objects (TNOs) remains uncertain. We report the discovery of a retrograde TNO, which we nickname “Niku,” detected by the Pan-STARRS 1 Outer Solar System Survey. Our numerical integrations show that the orbital dynamics of Niku are very similar to that of 2008 KV42 (Drac), with a half-life of ˜500 Myr. Comparing similar high-inclination TNOs and Centaurs (q > 10 au, a <100 au, and i > 60°), we find that these objects exhibit a surprising clustering of ascending node, and occupy a common orbital plane. This orbital configuration has high statistical significance: 3.8-σ. An unknown mechanism is required to explain the observed clustering. This discovery may provide a pathway to investigating a possible reservoir of high-inclination objects.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

SNP technologies for drug discovery : a current review

Single nucleotide polymorphisms (SNPs) are unique genetic differences between individuals that contribute in significant ways to the determination of human variation including physical characteristics like height and appearance as well as less obvious traits such as personality, behaviour and disease susceptibility. SNPs can also significantly influence responses to pharmacotherapy and whether drugs will produce adverse reactions. The development of new drugs can be made far cheaper and more rapid by selecting participants in drug trials based on their genetically determined response to drugs. Technology that can rapidly and inexpensively genotype thousands of samples for thousands of SNPs at a time is therefore in high demand. With the completion of the human genome project, about 12 million true SNPs have been identified to date. However, most have not yet been associated with disease susceptibility or drug response. Testing for the appropriate drug response SNPs in a patient requiring treatment would enable individualised therapy with the right drug and dose administered correctly the first time. Many pharmaceutical companies are also interested in identifying SNPs associated with polygenic traits so novel therapeutic targets can be discovered. This review focuses on technologies that can be used for genotyping known SNPs as well as for the discovery of novel SNPs associated with drug response.

Implementation of an integrated, technology-based, discovery mode assessment item involving an incubation period to enhance learning outcomes for engineering maths students

In this paper we discuss our current efforts to develop and implement an exploratory, discovery mode assessment item into the total learning and assessment profile for a target group of about 100 second level engineering mathematics students. The assessment item under development is composed of 2 parts, namely, a set of "pre-lab" homework problems (which focus on relevant prior mathematical knowledge, concepts and skills), and complementary computing laboratory exercises which are undertaken within a fixed (1 hour) time frame. In particular, the computing exercises exploit the algebraic manipulation and visualisation capabilities of the symbolic algebra package MAPLE, with the aim of promoting understanding of certain mathematical concepts and skills via visual and intuitive reasoning, rather than a formal or rigorous approach. The assessment task we are developing is aimed at providing students with a significant learning experience, in addition to providing feedback on their individual knowledge and skills. To this end, a noteworthy feature of the scheme is that marks awarded for the laboratory work are primarily based on the extent to which reflective, critical thinking is demonstrated, rather than the amount of CBE-style tasks completed by the student within the allowed time. With regard to student learning outcomes, a novel and potentially critical feature of our scheme is that the assessment task is designed to be intimately linked to the overall course content, in that it aims to introduce important concepts and skills (via individual student exploration) which will be revisited somewhat later in the pedagogically more restrictive formal lecture component of the course (typically a large group plenary format). Furthermore, the time delay involved, or "incubation period", is also a deliberate design feature: it is intended to allow students the opportunity to undergo potentially important internal re-adjustments in their understanding, before being exposed to lectures on related course content which are invariably delivered in a more condensed, formal and mathematically rigorous manner. In our presentation, we will discuss in more detail our motivation and rationale for trailing such a scheme for the targeted student group. Some of the advantages and disadvantages of our approach (as we perceived them at the initial stages) will also be enumerated. In a companion paper, the theoretical framework for our approach will be more fully elaborated, and measures of student learning outcomes (as obtained from eg. student provided feedback) will be discussed.

Knowledge discovery for adaptive negotiation agents in e-marketplaces

Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.

Establishment of a mouse model of colitis and its use to evaluate the anti-inflammatory effects of two ghrelin peptides

Ghrelin is a gut-brain peptide hormone that induces appetite, stimulates the release of growth hormone, and has recently been shown to ameliorate inflammation. Recent studies have suggested that ghrelin may play a potential role in inflammation-related diseases such as inflammatory bowel diseases (IBD). A previous study with ghrelin in the TNBS mouse model of colitis demonstrated that ghrelin treatment decreased the clinical severity of colitis and inflammation and prevented the recurrence of disease. Ghrelin may be acting at the immunological and epithelial level as the ghrelin receptor (GHSR) is expressed by immune cells and intestinal epithelial cells. The current project investigated the effect of ghrelin in a different mouse model of colitis using dextran sodium sulphate (DSS) – a luminal toxin. Two molecular weight forms of DSS were used as they give differing effects (5kDa and 40kDa). Ghrelin treatment significantly improved clinical colitis scores (p=0.012) in the C57BL/6 mouse strain with colitis induced by 2% DSS (5kDa). Treatment with ghrelin suppressed colitis in the proximal colon as indicated by reduced accumulative histopathology scores (p=0.03). Whilst there was a trend toward reduced scores in the mid and distal colon in these mice this did not reach significance. Ghrelin did not affect histopathology scores in the 40kDa model. There was no significant effect on the number of regulatory T cells or TNF-α secretion from cultured lymph node cells from these mice. The discovery of C-terminal ghrelin peptides, for example, obestatin and the peptide derived from exon 4 deleted proghrelin (Δ4 preproghrelin peptide) have raised questions regarding their potential role in biological functions. The current project investigated the effect of Δ4 peptide in the DSS model of colitis however no significant suppression of colitis was observed. In vitro epithelial wound healing assays were also undertaken to determine the effect of ghrelin on intestinal epithelial cell migration. Ghrelin did not significantly improve wound healing in these assays. In conclusion, ghrelin treatment displays a mild anti-inflammatory effect in the 5kDa DSS model. The potential mechanisms behind this effect and the disparity between these results and those published previously will be discussed.

Fast searching algorithm for candidate satellite-node set in NLMG

The Node-based Local Mesh Generation (NLMG) algorithm, which is free of mesh inconsistency, is one of core algorithms in the Node-based Local Finite Element Method (NLFEM) to achieve the seamless link between mesh generation and stiffness matrix calculation, and the seamless link helps to improve the parallel efficiency of FEM. Furthermore, the key to ensure the efficiency and reliability of NLMG is to determine the candidate satellite-node set of a central node quickly and accurately. This paper develops a Fast Local Search Method based on Uniform Bucket (FLSMUB) and a Fast Local Search Method based on Multilayer Bucket (FLSMMB), and applies them successfully to the decisive problems, i.e. presenting the candidate satellite-node set of any central node in NLMG algorithm. Using FLSMUB or FLSMMB, the NLMG algorithm becomes a practical tool to reduce the parallel computation cost of FEM. Parallel numerical experiments validate that either FLSMUB or FLSMMB is fast, reliable and efficient for their suitable problems and that they are especially effective for computing the large-scale parallel problems.

P2 purinergic receptor mRNA in rat and human sinoatrial node and other heart regions.

It is known that adenosine 5'-triphosphate (ATP) is a cotransmitter in the heart. Additionally, ATP is released from ischemic and hypoxic myocytes. Therefore, cardiac-derived sources of ATP have the potential to modify cardiac function. ATP activates P2X(1-7) and P2Y(1-14) receptors; however, the presence of P2X and P2Y receptor subtypes in strategic cardiac locations such as the sinoatrial node has not been determined. An understanding of P2X and P2Y receptor localization would facilitate investigation of purine receptor function in the heart. Therefore, we used quantitative PCR and in situ hybridization to measure the expression of mRNA of all known purine receptors in rat left ventricle, right atrium and sinoatrial node (SAN), and human right atrium and SAN. Expression of mRNA for all the cloned P2 receptors was observed in the ventricles, atria, and SAN of the rat. However, their abundance varied in different regions of the heart. P2X(5) was the most abundant of the P2X receptors in all three regions of the rat heart. In rat left ventricle, P2Y(1), P2Y(2), and P2Y(14) mRNA levels were highest for P2Y receptors, while in right atrium and SAN, P2Y(2) and P2Y(14) levels were highest, respectively. We extended these studies to investigate P2X(4) receptor mRNA in heart from rats with coronary artery ligation-induced heart failure. P2X(4) receptor mRNA was upregulated by 93% in SAN (P < 0.05), while a trend towards an increase was also observed in the right atrium and left ventricle (not significant). Thus, P2X(4)-mediated effects might be modulated in heart failure. mRNA for P2X(4-7) and P2Y(1,2,4,6,12-14), but not P2X(2,3) and P2Y(11), was detected in human right atrium and SAN. In addition, mRNA for P2X(1) was detected in human SAN but not human right atrium. In human right atrium and SAN, P2X(4) and P2X(7) mRNA was the highest for P2X receptors. P2Y(1) and P2Y(2) mRNA were the most abundant for P2Y receptors in the right atrium, while P2Y(1), P2Y(2), and P2Y(14) were the most abundant P2Y receptor subtypes in human SAN. This study shows a widespread distribution of P2 receptor mRNA in rat heart tissues but a more restricted presence and distribution of P2 receptor mRNA in human atrium and SAN. This study provides further direction for the elucidation of P2 receptor modulation of heart rate and contractility.

Molecular architecture of the human sinus node: insights into the function of the cardiac pacemaker.

BACKGROUND: Although we know much about the molecular makeup of the sinus node (SN) in small mammals, little is known about it in humans. The aims of the present study were to investigate the expression of ion channels in the human SN and to use the data to predict electrical activity. METHODS AND RESULTS: Quantitative polymerase chain reaction, in situ hybridization, and immunofluorescence were used to analyze 6 human tissue samples. Messenger RNA (mRNA) for 120 ion channels (and some related proteins) was measured in the SN, a novel paranodal area, and the right atrium (RA). The results showed, for example, that in the SN compared with the RA, there was a lower expression of Na(v)1.5, K(v)4.3, K(v)1.5, ERG, K(ir)2.1, K(ir)6.2, RyR2, SERCA2a, Cx40, and Cx43 mRNAs but a higher expression of Ca(v)1.3, Ca(v)3.1, HCN1, and HCN4 mRNAs. The expression pattern of many ion channels in the paranodal area was intermediate between that of the SN and RA; however, compared with the SN and RA, the paranodal area showed greater expression of K(v)4.2, K(ir)6.1, TASK1, SK2, and MiRP2. Expression of ion channel proteins was in agreement with expression of the corresponding mRNAs. The levels of mRNA in the SN, as a percentage of those in the RA, were used to estimate conductances of key ionic currents as a percentage of those in a mathematical model of human atrial action potential. The resulting SN model successfully produced pacemaking. CONCLUSIONS: Ion channels show a complex and heterogeneous pattern of expression in the SN, paranodal area, and RA in humans, and the expression pattern is appropriate to explain pacemaking.