Our current understanding of lake ecosystem response to climate change: What have we really learned from the north temperate deep lakes?

Climatic change is recognized as an important factor capable of influencing the structural properties of aquatic ecosystems. Lake ecosystems are particularly sensitive to climate change. Several long time-series studies have shown close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food-web structure. Understanding the complex interplay between climate, hydrological variability, and ecosystem structure and functioning is essential to inform water resources risk assessment and fisheries management. The purpose of this paper is to present the current understanding of climate-induced changes on lake ecosystem phenology. We first review the ability of climate to modulate the interactions among lake hydrodynamics, chemical factors, and food-web structure in several north temperate deep lakes (e.g., Lake Washington, Lake Tahoe, Lake Constance, Lake Geneva, Lake Baikal, and Lake Zurich). Our aim is to assess long-term trends in the physical (e.g., temperature, timing of stratification, and duration of ice cover), chemical (e.g., nutrient concentrations), and biological (e.g., timing of the spring bloom, phytoplankton composition, and zooplankton abundance) characteristics of the lakes and to examine the signature of local weather conditions (e.g., air temperature and rainfall) and large-scale climatic variability (e.g., ENSO and PDO) on the lake physics, chemistry and biology. We also conducted modeling experiments to quantify the relative effect of climate change and nutrient loading on lake phenology. These modeling experiments focused on the relative changes to the major causal associations underlying plankton dynamics during the spring bloom and the summer stratified period. To further understand the importance of climate change on lakes, we propose two complementary directions of future research. First, additional research is needed to elucidate the wide array of in-lake processes that are likely to be affected by the climate change. Second, it is essential to examine the heterogeneity in responses among different water bodies. The rationale of this approach and its significance for dealing with the uncertainty that the climate signals cascade through lake ecosystems and shape abiotic variability and/or biotic responses have been recently advocated by several other synthesis papers.     

Qualified,But Not Choosing STEM at University: Unconscious Influences on Choice of Study

Abstract

This article offers explanations as to why good candidates for mathematics or physics degrees might opt to study subjects other than STEM (science, technology, engineering, mathematics) subjects at university. Results come from analysis, informed by psychoanalytic theory and practice, of narrative-style interviews conducted with first-year undergraduates and from survey data. It is argued that psychoanalytic interpretations have a role in educational research. Also, it is shown that unconscious forces influenced young peoples’ decision making. Implications for policy are discussed, in particular, the issues of (a) the role of commitment and (b) being good enough to study a STEM discipline.     

Linear Programming Formulation for Strategic Dynamic Traffic Assignment

This work introduces a novel formulation of system optimal dynamic traffic assignment that captures strategic route choice in users under demand uncertainty. We define strategic route choice to be that users choose a path prior to knowing the true travel demand which will be experienced (therefore users consider the full set of possible demand scenarios). The problem is formulated based on previous work by Ziliaskopoulos (Transp Sci 34(1):37–49, 2000). The resulting novel formulation requires substantial enhancement to account for path-based flows and scenario-based stochastic demands. Further, a numerical demonstration is presented on a network with different demand loading profiles. Finally, model complexity, implications on scalability and future research directions are discussed.     

Biobjective optimization for analytical target cascading: optimality vs. achievability

Analytical target cascading (ATC), a hierarchical, multilevel multidisciplinary methodology, has proved to be an effective solution strategy for complex design problems. ATC decomposition and coordination incorporates compromise between the performance of the system and the demands of the subproblems reflected in their feasibility constraints. Optimal system performance regardless of subproblem feasibility may yield targets that are not achievable by the subproblems. Compromise is needed to accept deterioration of the optimal system performance and to increase the achievability of the targets. Biobjective optimization is used to reconcile system optimality and subproblem achievability of targets while solving the ATC-decomposed problem and generating an overall optimal solution. Three algorithms are proposed for two-level ATC-decomposed problems. The effectiveness of the algorithms is evaluated on mathematical and engineering examples. For a class of ATC problems, the performance of the proposed algorithms is superior to the performance of the ATC methods currently considered to be state of the art.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-against-all support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three state-of-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified. We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.     

Temporal shifts in the biodiversity of nearshore small fishes in Lake Simcoe

Nearshore small fish species represent a large proportion of fish biodiversity in Lake Simcoe, a large inland lake in southern Ontario, Canada. Over the past 30 years, Lake Simcoe has experienced several changes to its aquatic habitat, benthic invertebrate communities and predatory fish populations. This study compared samples of the nearshore small fish community in three geographic areas of Lake Simcoe. Fish community data were grouped into two time periods: a contemporary period (2007–2009) and a historical period (1982–1995). The fish community was compared across time periods for each area to assess if observed ecological changes had an impact on the small fish community. Species richness significantly declined between time periods in two areas (Cook's Bay and the southeast shoreline), the number of individuals captured declined between time periods in one area of the lake (Kempenfelt Bay) and Simpson's diversity index declined between time periods in one area of the lake (southeast shoreline). There were no significant differences in the Shannon–Weiner evenness index between time periods in any of the study areas. Additional analyses of intra- and inter-annual variation in fish sampling results generally supported the findings that shifts in the fish community occurred between time periods. Overall, this study suggests that the nearshore small fish biodiversity of Lake Simcoe has shifted over time but these shifts are not clearly related to recent increases in water clarity, macrophyte growth and nearshore benthic invertebrate densities.     

A comparative study of GPU programming models and architectures using neural networks

Recently, General Purpose Graphical Processing Units (GP-GPUs) have been identified as an intriguing technology to accelerate numerous data-parallel algorithms. Several GPU architectures and programming models are beginning to emerge and establish their niche in the High-Performance Computing (HPC) community. New massively parallel architectures such as the Nvidia??s Fermi and AMD/ATi??s Radeon pack tremendous computing power in their large number of multiprocessors. Their performance is unleashed using one of the two GP-GPU programming models: Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL). Both of them offer constructs and features that have direct bearing on the application runtime performance. In this paper, we compare the two GP-GPU architectures and the two programming models using a two-level character recognition network. The two-level network is developed using four different Spiking Neural Network (SNN) models, each with different ratios of computation-to-communication requirements. To compare the architectures, we have chosen the two extremes of the SNN models for implementation of the aforementioned two-level network. An architectural performance comparison of the SNN application running on Nvidia??s Fermi and AMD/ATi??s Radeon is done using the OpenCL programming model exhausting all of the optimization strategies plausible for the two architectures. To compare the programming models, we implement the two-level network on Nvidia??s Tesla C2050 based on the Fermi architecture. We present a hierarchy of implementations, where we successively add optimization techniques associated with the two programming models. We then compare the two programming models at these different levels of implementation and also present the effect of the network size (problem size) on the performance. We report significant application speed-up, as high as 1095× for the most computation intensive SNN neuron model, against a serial implementation on the Intel Core 2 Quad host. A comprehensive study presented in this paper establishes connections between programming models, architectures and applications.     

Sex Differences and Individual Differences in Cognitive Performance and Their Relationship to Endogenous Gonadal Hormones and Gonadotropins.

Sexually dimorphic cognitive performance in men (n=42) and women (n=42) was related to testosterone, estradiol, progesterone, luteinizing hormone, follicle-stimulating hormone, and sex hormone binding globulin, measured in 10-ml blood samples collected between 0900 and 1030 and, among women, during the follicular phase of the menstrual cycle. Significant sex differences favored men on spatial tasks (Mental Rotation and Judgment of Line Orientation) and on an inhibition task and favored women on a verbal task (category fluency). However, there were no significant relationships between any of the hormones and cognitive performance, suggesting that there are few, if any, consistent, substantial relationships between endogenous, nonfluctuating levels of gonadal hormones or gonadotropins and these cognitive abilities in men or women. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Differential Fos Expression Following Aspiration, Electrolytic, or Excitotoxic Lesions of the Perirhinal Cortex in Rats.

The authors explored the possibility that there are different neural consequences, beyond the primary site of brain damage, following perirhinal cortex (PRh) lesions made in different ways. Fos expression was used as a marker for neuronal activation and compared across the forebrains of rats that underwent the different types of surgery. Electrolytic and excitotoxic PRh lesions produced dramatic increases in Fos expression in the cortex, and excitotoxic and aspiration PRh lesions increased Fos expression in the dentate gyrus. These data are consistent with the hypothesis that different lesion methods have separable effects on neural function in regions outside the lesion site that could account for inconsistencies in the literature regarding the behavioral effects of PRh lesions on tests of spatial memory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)