Searching a needle in (linear) opportunistic networks

Searching content in mobile opportunistic networks is a diffi-cult problem due to the dynamically changing topology and intermittent connections. Moreover, due to the lack of global view of the network, it is arduous to determine whether the best response is discovered or search should be spread to other nodes. A node that has received a search query has to take two decisions: (i) whether to continue the search further or stop it at the current node (current search depth) and, independently of that, (ii) whether to send a response back or not. As each transmission and extra hop costs in terms of energy, bandwidth and time, a balance between the expected value of the response and the costs incurred must be sought. In order to better understand this inherent trade-off, we assume a simplified setting where both the query and response follow the same path. We formulate the problem of optimal search for the following two cases: a node holds (i) exactly matching content with some probability, and (ii) some content partially matching the query. We design static search in which the search depth is set at query initiation, dynamic search in which search depth is determined locally during query forwarding, and learning dynamic search which leverages the observations to estimate suitability of content for the query. Additionally, we show how unreliable response paths affect the optimal search depth and the correspond-ing search performance. Finally, we investigate the principal factors affecting the optimal search strategy

Hey, Influencer! Message Delivery to Social Central Nodes in Social Opportunistic Networks

This paper presents a new strategy to efficiently deliver messages to influencers in social opportunistic networks. An influencer node is an important node in the network with a high social centrality and, as a consequence, it can have some characteristics such as high reputation, trustfulness and credibility, that makes it an interesting recipient. Social network analysis has already been used to improve routing in opportunistic networking, but there are no mechanisms to efficiently route and deliver messages to these network influencers. The delivery strategy proposed in this article uses optimal stopping statistical techniques to choose among the different delivery candidate nodes in order to maximise the social centrality of the node chosen for delivery. For this decision process, we propose a routing-delivery strategy that takes into account node characteristics such as how central a node is in terms of its physical encounters. We show, by means of simulations based on real traces and message exchange datasets, that our proposal is efficient in terms of influencer selection, overhead, delivery ratio and latency time. With the proposed strategy, a new venue of applications for opportunistic networks can be devised and developed using the leading figure of social influencer

PERSISTENCE OF A VULNERABLE SEMI-AQUATIC TURTLE IN AN INTENSIVELY-MANAGED FOREST LANDSCAPE

Understanding spatial and population ecology of organisms allows land managers to predict how changes in distribution and composition of landscape features influence persistence. Our goal was to investigate body size, sex ratios, survival, individual movements, and habitat selection of a vulnerable freshwater turtle species, the spotted turtle (Clemmys guttata), in an intensively-managed forest landscape in eastern North Carolina, USA. Spotted turtles naturally occur in wetland-dominated landscapes, but this system is heavily-altered, with \u3e222,000 hectares of pine plantations and \u3e10,000 km of ditches managed by Weyerhaeuser Company. During 2012-2013, we captured and individually marked 280 turtles, and used radio-telemetry (n = 31) to investigate movements and habitat selection at multiple scales. Spotted turtle monthly survival estimates were high with an annual population growth rate \u3e1. According to a stage-based population matrix, adult and juvenile survival were the most sensitive vital rates in the population. Turtle movements and habitat selection were focused on ditch networks, which appeared to provide travel corridors between upland and aquatic sites as well as access to potential mates. At the local scale, turtles selected for greater understory closure, more pine needle substrate cover, and greater substrate temperature, suggesting scale-dependent behaviors (i.e. thermoregulation) and the importance of pine forest cover around the ditches. At the landscape scale, ditch features and middle-old aged stands were important predictors of turtle locations, which may provide important habitat for imperiled species in highly-managed forest ecosystems. Also, the persistence of spotted turtles, a vulnerable, wetland-dwelling species, in an intensively-managed upland and aquatic landscape may suggest credibility of certain management regimes given the decline of the species in more natural ecosystems

Pro-Diluvian: Understanding scoped-flooding for content discovery in information-centric networking

Scoped-flooding is a technique for content discovery in a broad networking context. This paper investigates the ef-fects of scoped-flooding on various topologies in information-centric networking. Using the proposed ring model, we show that flooding can be constrained within a very small neigh-bourhood to achieve most of the gains which come from areas where the growth rate is relatively low, i.e., the net-work edge. We also study two flooding strategies and com-pare their behaviours. Given that caching schemes favour more popular items in competition for cache space, popu-lar items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers ’ topological properties to fine tune the flooding radius. Our results shed light on designing ecient con-tent discovery mechanism for future information-centric net-works

Information discovery in multi-dimensional autonomous wireless sensor networks

 The thesis proposed four novel algorithms of information discovery for Multidimensional Autonomous Wireless Sensor Networks (WSNs) that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to Multidimensional Autonomous WSNs are deployed in complex environments (e.g., mission-critical applications)

The Anti-Sigma Factor MucA of Pseudomonas aeruginosa: Dramatic Differences of a mucA22 vs. a ΔmucA Mutant in Anaerobic Acidified Nitrite Sensitivity of Planktonic and Biofilm Bacteria in vitro and During Chronic Murine Lung Infection

Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified nitrite (A-NO2-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO2- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO2- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO2- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO2- than a truncated ΔmucA deletion (Δ157–194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO2--treated bacteria revealed restoration of near wild-type transcript levels of protective NO2- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO2--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO2- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO2-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO2- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases

Machine learning predicts electrospray particle size

Electrospraying (ES) is a state-of-the-art processing technique with the promise of achieving key nanotechnology and contemporary manufacturing needs. As a versatile technique, ES can produce particles with different sizes, morphologies, and porosities by tuning a list of experiment parameters. However, this level of precision demands an exhaustive trial-and-error approach, at high costs and heavily relies on processing expertise. The present study demonstrates how machine learning (ML) can expedite the optimization process by accurately predicting particle diameter, for both nano- and micron-sized particles. This was achieved by constructing an informative electrospraying database containing 445 records from the literature, followed by the development of predictive ML models. Feature engineering techniques were explored, where ultimately it was found that solvent physiochemical properties as the molecular representation and data with imputation provided models the highest performance. The top two models were XGBoost and Random Forest (RF), which yielded root-mean-squared errors (RMSE) of 3.91 μm and 6.19 μm evaluated by 5-fold cross-validation (CV), respectively. These models were experimentally validated in-house with different combinations of experiment parameters, where RMSE between the predicted and actual particle size was found to be 1.30 μm for the XGBoost model and 1.62 μm for the RF model. Therefore, it was concluded that data generated by the ES literature, in addition to being both cost- and material-free, can yield high-performing ML models for predicting particle size. The ML models were also consulted to determine the key processing parameters that govern particle size, where it was concluded that the models learnt similar attributes identified by scaling laws