Rainbow smelt population responses to species invasions and change in environmental condition

Invasive species can cause major disruptions in native food webs, yet the impact of species introductions and whether they will become invasive appears to be context-dependent. Rainbow smelt and alewife co-exist as invasive species in the Laurentian Great Lakes and as native species on the Atlantic coast of North America, but in Lake Champlain rainbow smelt is the dominant native forage fish and alewife are invasive. Alewife became abundant by 2007, providing an opportunity to explore the dynamics of these two species in a system where only one is invasive. We used data from a 28-year forage fish survey to compare demographics of rainbow smelt populations in three basins of Lake Champlain with different volumes, nutrient levels, and predator abundances. Rainbow smelt catch-per-unit-effort (CPUE) remained constant in the large, deep Main Lake before and after alewife invaded, but decreased in the two smaller basins. Declines were primarily a result of increased age-0 and age-1 mortality. Predation by top piscivores, system productivity, and competition for resources alone could not explain the patterns in CPUE across the basins. The mechanisms that allow alewife and rainbow smelt to co-exist could be related to system volume and oxythermal habitat availability, and may explain why the two species do not negatively affect each other in the Great Lakes. Summer hypoxia in the smaller basins could force individuals into smaller habitat volumes with higher densities of competitors and cannibalistic adult rainbow smelt. Habitat availability may mediate the impact of invasive alewife on native rainbow smelt.     

High aspect ratio gold nanopillars on microelectrodes for neural interfaces

Microelectrode arrays (MEA) have become an established tool in applied and fundamental research. Low impedance at the interface between tissue and conducting electrodes is of utmost importance for the electrical recording or stimulation of electrophysiological active cells such as cardiac myocytes and neurons. A common way to improve this interface is to increase the electrochemically active surface area of the electrode. In this paper the fabrication of microelectrodes covered with very high aspect ratio (AR > 100) gold nanopillars is presented and electrode biocompatibility is investigated using cell culture experiments. The nanopillar electrodes show decreased impedance over the entire scanned frequency range of 1 Hz–100 kHz and an impedance improvement of up to 89.5 at 1 kHz depending on nanopillar height. Neurons adhere well to the substrate and electrodes and signals with amplitudes up to ten times higher than with conventional gold electrodes were recorded in cell culture experiments.     

Uniaxial roll-drawing of isotactic polypropylene sheet

The process described as “roll-drawing” has been applied to commercial extruded sheets of isotactic polypropylene (M?_n = 70,900). Preheated billets were drawn into thin, clear, transparent sheets in a single pass, producing uniaxial orientation of the polymer molecules in the draw direction. At the maximum draw ratio of 20, the ultimate tensile strength and Young's modulus in the draw direction were 0.5 GPa and 20 GPa respectively. The mechanical properties transverse to the draw direction were virtually unchanged. The theory of fiber reinforcement for unidirectional anisotropic plates was applied to interpret the orientation dependence of the stress-strain behavior of the drawn sheets. From these results, it was estimated that the mechanical properties of biaxially laminated polypropylene sheets equaled the performance of aramid and carbon fiber composites, The roll-drawing process appears to be economically attractive for the production of ultra-high modulus crystalline thermoplastics in sheet form having excellent uniaxial or biaxial properties.     

Dense subgraph maintenance under streaming edge weight updates for real-time story identification

Recent years have witnessed an unprecedented proliferation of social media. People around the globe author, everyday, millions of blog posts, social network status updates, etc. This rich stream of information can be used to identify, on an ongoing basis, emerging stories, and events that capture popular attention. Stories can be identified via groups of tightly coupled real-world entities, namely the people, locations, products, etc, that are involved in the story. The sheer scale and rapid evolution of the data involved necessitate highly efficient techniques for identifying important stories at every point of time. The main challenge in real-time story identification is the maintenance of dense subgraphs (corresponding to groups of tightly coupled entities) under streaming edge weight updates (resulting from a stream of user-generated content). This is the first work to study the efficient maintenance of dense subgraphs under such streaming edge weight updates. For a wide range of definitions of density, we derive theoretical results regarding the magnitude of change that a single edge weight update can cause. Based on these, we propose a novel algorithm, DynDens, which outperforms adaptations of existing techniques to this setting and yields meaningful, intuitive results. Our approach is validated by a thorough experimental evaluation on large-scale real and synthetic datasets.     

Growth and the microstructural and ferroelectric characterization of oriented BaMgF(4) thin films

The growth of ferroelectric BaMgF(4) thin films on Si(100), sapphire, and other substrates under ultrahigh vacuum (UHV) conditions is reported. Microstructural characterization of the films using transmission electron microscopy (TEM) revealed that they were oriented crystalline films, although not epitaxial. Ferroelectric hysteresis measurements yielded spontaneous polarization and coercivity values of almost 1.0 muC/cm(2) and 160 kV/cm, respectively. The discrepancy with the bulk ferroelectric values were attributed to the electrical contacts, impurities in the film, and lack of polar axis orientation. Preliminary capacitance-voltage (C-V) hysteresis measurements on a 480-nm-thick BaMgF(4) film yielded a 10.8-V threshold shift (memory window) in response to a +/-10-V programming voltage for a MIS gate structure similar to that of the ferroelectric memory field-effect transistor (FEMFET).     

An efficient algorithm for partially matched services in internet of services

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides’ leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.     

The Battle Over E-commerce Sales Taxes Heats Up

The inability of states to collect sales taxes from online vendors is a salient issue during the current economic decline. Further, brick and mortar retailers, who must charge customers sales tax, have pushed for legislation requiring online vendors to also do so. States have enacted legislation requiring online vendors, such as Amazon.com LLC, to collect and pay these taxes. In this article the authors describe the issue and make recommendations for resolution.     

A greedy gradient-simulated annealing selection hyper-heuristic

Educational timetabling problem is a challenging real world problem which has been of interest to many researchers and practitioners. There are many variants of this problem which mainly require scheduling of events and resources under various constraints. In this study, a curriculum based course timetabling problem at Yeditepe University is described and an iterative selection hyper-heuristic is presented as a solution method. A selection hyper-heuristic as a high level methodology operates on the space formed by a fixed set of low level heuristics which operate directly on the space of solutions. The move acceptance and heuristic selection methods are the main components of a selection hyper-heuristic. The proposed hyper-heuristic in this study combines a simulated annealing move acceptance method with a learning heuristic selection method and manages a set of low level constraint oriented heuristics. A key goal in hyper-heuristic research is to build low cost methods which are general and can be reused on unseen problem instances as well as other problem domains desirably with no additional human expert intervention. Hence, the proposed method is additionally applied to a high school timetabling problem, as well as six other problem domains from a hyper-heuristic benchmark to test its level of generality. The empirical results show that our easy-to-implement hyper-heuristic is effective in solving the Yeditepe course timetabling problem. Moreover, being sufficiently general, it delivers a reasonable performance across different problem domains.     

Learning market prices in real-time supply chain management

This paper proposes a model for dynamic pricing that combines knowledge of production capacity and existing commitments, reasoning about uncertainty and learning of market conditions in an attempt to optimise expected profits. In particular, the market conditions are represented as a set of probabilities over the success rate of product prices, and those prices are learned online as the market develops. The dynamic pricing model is integrated into a real-time supply chain management agent using the Trading Agent Competition Supply Chain Management game as a test framework. We evaluate the agent experimentally in competition with other supply chain agents, and demonstrate the benefits of incorporating more market data into the dynamic pricing mechanism.