A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

Effects of drivers and their variations on the number of stems and aboveground carbon removals in miombo woodlands of mainland Tanzania

Background Removals caused by both natural and anthropogenic drivers such as logging and fire in miombo woodlands causes substantial carbon emissions. Here we present drivers and their effects on the variations on the number of stems and aboveground carbon (AGC) removals based on an analysis of Tanzania’s national forest inventory (NFI) data extracted from the National Forest Resources Assessment and Monitoring (NAFORMA) database using allometric models that utilize stump diameter as the sole predictor. Results Drivers of AGC removals in miombo woodlands of mainland Tanzania in order of importance were timber, fire, shifting cultivation, charcoal, natural death, firewood collection, poles, grazing by wildlife animals, carvings, grazing by domestic animals, and mining. The average number of stems and AGC removals by driver ranged from 0.006 to 16.587 stems ha−1 year−1 and 0.0–1.273 tCha−1 year−1 respectively. Furthermore, charcoal, shifting cultivation and fuelwood caused higher tree removals as opposed to timber, natural death and fire that accounted for higher AGC removals. Conclusions Drivers caused substantial effects on the number of stems and carbon removals. Increased mitigation efforts in addressing removals by timber, fires, shifting cultivation, charcoal and natural death would be effective in mitigating degradation in miombo woodlands of Tanzania. Additionally, site-specific studies need to be conducted to bring information that would be used for managing woodlands at local levels. This kind of study need to be conducted in other vegetation types like montane and Mangrove forest at national scale in Tanzania.

Automatic question generation and answer assessment: a survey

Learning through the internet becomes popular that facilitates learners to learn anything, anytime, anywhere from the web resources. Assessment is most important in any learning system. An assessment system can find the self-learning gaps of learners and improve the progress of learning. The manual question generation takes much time and labor. Therefore, automatic question generation from learning resources is the primary task of an automated assessment system. This paper presents a survey of automatic question generation and assessment strategies from textual and pictorial learning resources. The purpose of this survey is to summarize the state-of-the-art techniques for generating questions and evaluating their answers automatically.

Recent improvements to the Australian Water Resources Assessment Landscape Model (version 7)

<p>The Australian Water Resources Assessment Landscape (AWRA-L) model is a continental gridded, daily time-step, water balance model, developed over the last decade by CSIRO and the Australian Bureau of Meteorology for a range of hydrological applications. The model outputs (including soil moisture, evapotranspiration, runoff and deep drainage; available through www.bom.gov.au/water/landscape) have found wide application for monitoring purposes (e.g. for flood and fire risk, drought monitoring), water reporting (eg. National Water Accounts), and in analysing trends in water balance outputs including streamflow. In addition to these historical/monitoring applications, AWRA-L is being further used for production of 10-day forecasts, seasonal forecasts, and long-term projections of hydrological outputs out to the end of the century.</p><p> </p><p>This study details recent development of AWRA-L for improved performance across the water balance for use in monitoring through to long term projections. Changes are implemented across three broad areas: improved static and dynamic inputs, altered conceptual structure (additional urban component and baseflow ephemerality), and altered calibration approach. In particular, a new spatial calibration approach is applied across the nation using over 300 catchments. To do so model pixel output values are compared against spatially distributed satellite data for soil moisture, evapotranspiration (ET), and two new components including fraction of vegetation (F<sub>veg</sub>) and terrestrial water storage (TWS). In the previous versions of the model lumped catchment average values of evapotranspiration and soil moisture were used. In addition to comparing to a wide range of national datasets (streamflow observations, flux tower observations, soil moisture network observations, recharge observations), the model performance was compared for drought analysis (reproducing 2-state rainfall-runoff behaviour observed in parts of Australia) and flood analysis (correlating with operationally used flood forecasting parameters). Overall, the modified AWRA-L outperformed previous versions in terms of water balance estimation according to a wide range of validation data. The successful application of the spatial calibration method can potentially pave the path for more frequent application of complex calibration methods for large scale simulations. Furthermore, consideration of a terrestrial water storage component in the objective function highlights the importance of this factor in capturing more accurate simulation of other water balance components, particularly streamflow. The improved streamflow performance demonstrates the enhanced functionality of the model in capturing intermittency and streamflow shifts in seasonally dry and groundwater dependent catchments, further demonstrated in the drought analysis. Finally, the flood study demonstrates the application and value of the model for real time flood-monitoring and forecasting purposes. This study shows the potential of AWRA-L model and associated spatial calibration approach for accurate simulation of water balance variables for use in continental-scale studies.</p>