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Abstract

Data set that was used to determine the frequency each of 4 key words (public engagement, education, outreach, or science communication) in the title or abstract of published papers in Freshwater Science (formerly the Journal of the North American Benthological Society) and oral presentations (talks) at the annual Society for Freshwater Science meetings from 1997 to 2019. Does not include any data on talks for 2013-2014 because they were not published during those years.

Methods

The dataset was collected by reviewing abstracts in the journal Freshwater Science (formerly the Journal of North American Benthological Society [JNABS]) from 1997 to 2019 as well as searching abstracts from oral presentations at the SFS Annual Meeting (available online for 1997–2012 and 2015–2019 at https://sfsannualmeeting.org/SearchAll.cfm) for key words (public engagement, science communication, education, outreach) related to PES. The dataset was processed by inputting the data collected from our search (i.e., year, type of work, keyword, and number of times the keyword appeared in that type of work during the specified year) into a .csv file using Microsoft Excel. R was used (https://www.r-project.org/) and its accompanying package ggplot2 (https://ggplot2.tidyverse.org/) to plot the data.

Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms comparedmore »to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.« less

Abstract Arrays of flexible polymer piezoelectric film cantilevers that mimic grass or leaves is a prospective idea for harvesting wind energy in urban areas, where the use of traditional technologies is problematic due to low wind velocities. Conversion of this idea into an economically attractive technology depends on various factors including the shape and dimensions of individual films to maximize generated power and to minimize associated costs of production, operation, and maintenance. The latter requirement can be satisfied with rectangular films undergoing flutter in ambient air. Flexible piezoelectric films that displace due to low forces and can convert mechanical energy into electrical energy are ideal for this application. The goal of the presented study is to determine the key dimensions of the piezoelectric film to enhance generated power within the wind range characteristic for urban areas from 1.3 to 7.6 m/s. For this purpose, experiments were conducted in a wind tunnel using piezoelectric polymer films of polyvinylidine fluoride with the length, width, and thickness varying in the ranges of 32–150, 16–22, and 40–64 μm, respectively. Voltage and power outputs for individual samples were measured at wind speeds ranging from 0.5 to 16.5 m/s. Results demonstrated that a single film could produce up tomore »0.74 nW and that the optimal film dimensions are 63 mm × 22 mm × 40 μm (from considered samples) for the wind energy harvesting in urban areas. Further improvement in power production can be expected when using films with reduced thickness, low elastic modulus, and increased length, and by assembling films in arrays.« less

New breed of applications, such as autonomous driving and their need for computation-aided quick decision making has motivated the delegation of compute-intensive services (e.g., video analytic) to the more powerful surrogate machines at the network edge–edge computing (EC). Recently, the notion of pervasive edge computing (PEC) has emerged, in which users’ devices can join the pool of the computing resources that perform edge computing. Inclusion of users’ devices increases the computing capability at the edge (adding to the infrastructure servers), but in comparison to the conventional edge ecosystems, it also introduces new challenges, such as service orchestration (i.e., service placement, discovery, and migration). We propose uDiscover, a novel user-driven service discovery and utilization framework for the PEC ecosystem. In designing uDiscover, we considered the Named-Data Networking architecture for balancing users workloads and reducing user-perceived latency. We propose proactive and reactive service discovery approaches and assess their performance in PEC and infrastructure-only ecosystems. Our simulation results show that (i) the PEC ecosystem reduces the user-perceived delays by up to 70%, and (ii) uDiscover selects the most suitable server–"accurate" delay estimates with less than 10% error–to execute any given task.

This paper gives a simple method to construct generator matrices with polynomial entries (and hence offers an alternative encoding method to the one commonly used) for all quasi-cyclic low-density parity-check (QC-LDPC) codes, even for those that are rank deficient. The approach is based on constructing a set of codewords with the desired total rank by using minors of the parity-check matrix. We exemplify the method on several well-known and standard codes. Moreover, we explore the connections between the minors of the parity-check matrix and the known upper bound on minimum distance and provide a method to compute the rank of any parity-check matrix representing a QC-LDPC code, and hence the dimension of the code, by using the minors of the corresponding polynomial parity-check matrix.

In this paper, we examine variable node (VN) doping to mitigate the error propagation problem in sliding window decoding (SWD) of spatially coupled LDPC (SC-LDPC) codes from the point of view of the encoding process. More specifically, in order to simplify the process of generating an encoded sequence with some number of doped code bits, we propose to employ systematic encoding and to limit doping to systematic bits only. Numerical results show that doping of systematic bits only achieves comparable performance to employing general (nonsystematic) encoding and full doping of all the code bits at each doping position, while benefiting from a much simpler encoding process. We then show that the inherent rate loss due to doping can be reduced by doping only a fraction of the variable nodes at each doping position with only a minor impact on performance.

In this research, data from 36 countries from 2013 to 2018 were used to examine the factors influencing CO 2 emissions in Islamic countries, focusing on the impact of Islamic financial growth. The spatial econometric technique estimation findings indicate that there is no geographical association between CO 2 emissions in the analyzed countries. The test findings establish the existence of the Kuznets hypothesis for the environment. Additionally, trade openness and increased energy usage have resulted in an increase in CO 2 emissions. The impacts of traditional financial development factors, such as financial market and financial institution variables, were examined in this research. The findings indicate that the two variables have no direct and substantial influence on CO 2 emissions and that their significant effect on CO 2 emissions appears only when their nonlinear and spillover effects on energy consumption and economic growth are included. Additionally, the growth of financial institutions is inversely proportional to the intensity of carbon emissions. The results indicate that while the development of financial markets and institutions results in a significant increase in CO 2 emissions, the negative coefficient of the interaction between financial development and energy consumption indicates that financial development ensures energy efficiency, whichmore »reduces the intensity of carbon emissions. The findings indicate that the expansion and depth of Islamic finance, as measured by total assets, asset quality, earnings, and efficiency of Islamic banks, can result in a nonlinear increase in CO 2 emissions with a U-shaped relationship. The study of spillover effects demonstrates that in addition to their direct and positive effects on CO 2 emissions, the increase in Islamic social responsibility and consumer education, and awareness about Islamic banking reduce the enhancing effects of energy consumption on greenhouse gas emissions.« less