Metacognitive listening strategies awareness in learning English as a foreign language: a comparison between university and high-school students

AbstractThe present study investigated metacognitive listening strategies awareness among Iranian university and highschool students learning English as a foreign language. To achieve this goal, one hundred and twenty-two university students and one hundred and sixteen high-school students filled in the Metacognitive Awareness Listening Questionnaire (MALQ) with five subparts including problem-solving, planning and evaluation, translation, person knowledge, and directed attention. The result of the data analysis revealed that university and high-school students were different with regard to their metacognitive listening strategies awareness in general, and in person knowledge and mental translation components

QFT Based Robust Positioning Control of the PMSM Using Automatic Loop Shaping with Teaching Learning Optimization

Automation of the robust control system synthesis for uncertain systems is of great practical interest. In this paper, the loop shaping step for synthesizing quantitative feedback theory (QFT) based controller for a two-phase permanent magnet stepper motor (PMSM) has been automated using teaching learning-based optimization (TLBO) algorithm. The QFT controller design problem has been posed as an optimization problem and TLBO algorithm has been used to minimize the proposed cost function. This facilitates designing low-order fixed-structure controller, eliminates the need of manual loop shaping step on the Nichols charts, and prevents the overdesign of the controller. A performance comparison of the designed controller has been made with the classical PID tuning method of Ziegler-Nichols and QFT controller tuned using other optimization algorithms. The simulation results show that the designed QFT controller using TLBO offers robust stability, disturbance rejection, and proper reference tracking over a range of PMSM’s parametric uncertainties as compared to the classical design techniques

Deep learning in plant phenological research: A systematic literature review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field

Development of Multi-Scale City Building Energy Model for Urban Climate Resilience

In the past decades, the world has experienced rapid urbanization that caused increasing climate change challenges, pollution, energy consumption, and greenhouse gas (GHG) emission. More frequent and more prolonged extreme weather events such as heatwave and cold-wave and urban heat island phenomena are some negative impacts of climate change. The building sector is an essential source of urban energy consumption, GHG emission, and Urban Heat Island (UHI) formation. Different energy efficiency measures can be implemented to reduce building energy consumption, such as retrofitting existing building stock and deploying new technologies. These scenarios will also contribute to the mitigation of UHI, heatwaves, and climate change. Urban building energy models are simulation tools developed to study these kinds of problems. There are several challenges with existing Urban Building Energy Modelling (UBEM) tools, including creating a 3D model of buildings, estimating buildings’ properties, and using urban microclimate data for simulation. On the other hand, accurate building energy simulation and fluxes from buildings to the atmosphere can impact forecasting accuracy by numerical weather prediction tools. Therefore, developing a multi-scale integrated urban building energy and climate simulation tool is essential for modeling both buildings’ energy performance and atmospheric fields. In this work, a new urban building energy model called City Building Energy Model (CityBEM) is developed to solve UBEMs' current challenges. First, a building-scale energy and airflow simulation model is developed for modeling a single building. It is based on a coupled thermal/airflow multi-zone network model. The multi-zone network model is then modified for calculation of urban scale buildings’ energy performance. A new method is developed to create the 3D model of buildings by integrating buildings’ footprint data obtained from OpenStreetMap and Microsoft and building height information by Google Earth Application Programming Interface (API). An archetype library is developed for the estimation of buildings’ non-geometrical properties. Buildings are classified based on usage type and age obtained from city shapefile datasets. The geometrical and non-geometrical datasets are joined using the QGIS tool and Mapbox platform. To use local microclimate data for buildings’ energy performance, CityBEM is integrated with different microclimate simulation tools. First, CityBEM is fully integrated with the CityFFD tool to model the two-way interaction between buildings and microclimate. In the second method, a multi-scale urban climate and buildings energy simulation tool is developed by one-way integration of CityBEM with 3D Global Environmental Multiscale Model (GEM) and Surface Prediction System (SPS) developed by Environment and Climate Change Canada (ECCC). The one-way multi-scale model cannot capture the impact of CityBEM on the atmospheric fields; therefore, to model this impact, the CityBEM is added as a new module to the SPS model. SPS includes a Town Energy Balance (TEB) scheme for modeling the urban surface. In this thesis, CityBEM is added to the TEB for modeling the buildings. Using the developed TEB-CityBEM model in GEM simulations, near-surface forecasting accuracy can be improved, and buildings’ energy simulation is added as a new feature to the GEM model. The multi-scale model can be used to study different mitigation strategies such as retrofitting existing buildings, modeling natural ventilation and its impact on reducing energy consumption, model new technologies to reduce energy consumption, etc. The TEB-CityBEM model can also be added to the air quality model of ECCC called GEM-MACH to study the impact of urban building modeling on air quality in urban areas. Finally, due to the importance of aerosol transmission of covid-19 in indoor spaces, it is essential to develop a model to study the impact of different mitigation strategies on reducing the risk of infection in the rooms and their corresponding energy consumption effects. In this thesis, a city-scale model (CityRPI) is developed to estimate airborne transmission of COVID-19 in indoor spaces. The CityRPI model is integrated with the CityBEM. The integrated model is applied to Montreal, and the impact of mitigation strategies on the infection risk and energy consumption is studied for different types of buildings

Evaluating Wind-driven Natural Ventilation Potential for Early Building Design

Natural ventilation is widely applied in buildings considering its potential of improving indoor air quality and saving building energy costs. However, to evaluate its viability and determine the ventilation rates quickly and relatively accurately during early design stage is challenging. This paper explores a fast and accurate evaluation approach in the form of empirical equations to estimate the ventilation rate and potential of wind-driven natural ventilation. By using computational fluid dynamics (CFD) with results validated for both cross and single natural ventilation strategies, this study conducted a series of simulations to determine critical ventilation coefficients for the empirical equations as functions of wind direction, speed and building height. The proposed evaluation approach could help architects and engineers to evaluate the viability of natural ventilation during early building design. This approach was also demonstrated to evaluate the potential of natural ventilation in 65 cities of North America so a series of natural ventilation potential maps were generated for a better understanding of natural ventilation potential in different climates and for the climate-conscious design of buildings in North America

The value of 18F-fluorodeoxyglucose positron emission tomography for prediction of treatment response in gastrointestinal stromal tumors: a systematic review and metaanalysis

Background: Early detection of response to treatment is critically important in gastrointestinal stromal tumors (GIST). Therefore, the present systematic review and meta-analysis assessed the value of 18f-fluorodeoxyglucose positron emission tomography (18FDG– PET) on prediction of therapeutic response of GIST patients to systemic treatments. Methods: The literature search was conducted using PubMed, SCOPUS, Cochrane, and Google Scholar databases, and review article references. Eligible articles were defined as studies included confirmed GIST patients who underwent 18FDG–PET as well as assessing the screening role of it. Results: Finally, 21 relevant articles were included. The analysis showed the pooled sensitivity and specificity of 18FDG–PET in evaluation of response to treatment of GIST patient were 0.90 (95% CI: 0.85–0.94; I 2 = 52.59, P = 0.001) and 0.62 (95% CI: 0.49–0.75; I 2 = 69.7, P = 0.001), respectively. In addition, the pooled prognostic odds ratio of 18FDG–PET for was 14.99 (95% CI, 6.42–34.99; I 2 = 100.0, P < 0.001). The Meta regression showed that sensitivity of 18FDG-PET was higher if the sample size of study was equal or more than 30 cases (sensitivity = 0.93; 95% CI: 0.89–0.97), when using PET/CT (sensitivity = 0.92; 95% CI: 0.89–0.97), and self-design criteria (sensitivity = 0.93; 95% CI: 0.87–1.0). Conclusion: The present meta-analysis showed 18FDG–PET has a significant value in predicting treatment response in GIST patients

An Overview on the Treatment and Management of the Desalination Brine Solution

Due to the increasing limitations of water resources, application of desalination plants is expanding. One of the constraints associated with desalination plant operation is the production of concentrated solution, which is known as brine and can lead to critical challenges in the environment due to its high level of salinity. In this regard, many different disposal options used recently to control and prevent the environmental issues may be caused by the brine. Evaporation ponds, surface water discharge, and deep well injection are considered as the most well-known options to properly dispose concentrated brine. However, the application of these methods is highly restricted by capital cost and their limited uses. The treatment methods vary in terms of their ability in organics removal and can be divided into three different conventional groups as biological, physicochemical, and oxidation. In recent years, more attention has been paid to membrane-based technologies due to their economic performance in recovering precious resources and providing potable water with high recovery rates. This book chapter provides some critical reviews on recent technologies including treatment operations and disposal options to manage concentrated solutions from desalination plants. Finally, electrodialysis, forward osmosis, and membrane distillation as emerging membrane processes are examined in this chapter

The effect of small-scale topography on patterns of endemism within islands

Topography influences evolutionary and ecological processes by isolating populations and by enhancing habitat diversity. While the effects of large-scale topography on patterns of species richness and endemism are increasingly well documented, the direct effect of local topography on endemism is less understood. This study compares different aspects of topographic isolation, namely the isolating effect of deep barrancos (ravines) and the effect of increasing isolation with elevation in influencing patterns of plant endemism within a topographically diverse oceanic island (La Palma, Canary Islands, Spain). We collected plant presence–absence data from 75 plots in 8 barrancos on the northern coast of La Palma, spanning an elevation gradient from 95 to 674m a.s.l. Using mixed-effects models, we assessed the effect of barranco depth and elevation on the percentage of single-island endemics, multi-island endemics and archipelago endemics. We found that percent endemism was not significantly correlated with barranco depth, and correlated negatively with elevation within barrancos (rather than the expected positive relationship). The topographic barriers associated with the deep island barrancos thus appear insufficient to drive speciation through isolation in oceanic island plants. The decrease in endemism with elevation contradicts findings by previous broader-scale studies and it may reflect local influences, such as high habitat heterogeneity at low elevations