Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data.This paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies.We provide a technical explanation of the proposed cloud architecture and its usage.In addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings.Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage.The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

Predicting suitability of different scale-dependent dispersivities for reactive solute transport through stratified porous media

In this paper,the behavior of breakthrough curves(BTCs) for reactive solute transport through stratified porous media is investigated.A physical laboratory model for layered porous media was constructed,in which thin layer of gravel was sandwiched in between two thick layers of natural soil.Gravel layer and natural soil layers were hydraulically connected as single porous continuum.A constant source of tracer was connected through gravel layer and elucidated at different sampling points in the direction of flow.Flexible multiprocess non-equilibrium(MPNE) transport equation with scale-dependent dispersivity function was used to simulate experimental BTCs of reactive solute transport through layered porous media.The values of equilibrium sorption coefficient and other input parameters were obtained experimentally.The simulation of BTC was performed using MPNE model with scale-dependent dispersivity.The simulation of different scale-dependent dispersivities was then compared and it was found that for field scale of estimation of dispersivity,asymptotic and exponential dispersivity functions performed better.In continuation to the comparison of simulated BTCs obtained using different models,spatial moment analysis of each aforesaid scale-dependent dispersivity model was also done.Spatial moment analysis provides the information related to mean solute mass,rate of mass travel,and mean plume dispersion.Linear and constant dispersivities showed higher variance as compared to asymptotic and exponential dispersion functions.This supports the field applicability of asymptotic and exponential dispersivity functions.The BTCs were also found to elucidate a nonzero concentration with time,which was clearly affected by physical non-equilibrium.In natural condition,such information is required in effective aquifer remediation process.

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the web.So,task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user’s services demand modification dynamically.The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions.In heterogeneous multiprocessor systems,task assignments and schedules have a significant impact on system operation.Within the heuristic-based task scheduling algorithm,the different processes will lead to a different task execution time(makespan)on a heterogeneous computing system.Thus,a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce(makespan).In this paper,we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem.The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution.We evaluate our algorithm’s performance by applying it to three examples with a different number of tasks and processors.The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

A Hybrid Methodology for Short Term Temperature Forecasting

Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.

Configuration of an optical waveguide interconnect mesh network based on EOPCB

An optical waveguide interconnect mesh network scheme for parallel multiprocessor systems based on an electro-optical printed circuit board （EOPCB） with multimode polymer waveguide is proposed. The system consists of 2×2 processor element chips interconnected in a mesh network configuration. An additional layer with optical waveguide structure is embedded in a conventional printed circuit board to construct the EOPCB. Vertical cavity surface emitting laser （VCSEL）/positive intrinsic-negative （PIN） arrays are ap- plied as the optical transmitters/receivers. Three 1 ~ 12 VCSEL/PIN parallel optical transmitting/receiving modules are used to provide 32 input/output optical channels required by the 2~2 chip-to-chip optical mesh interconnect system. The data rate in each optical channel is 3.125 Gbps and thus 10 Gbps parallel optical interconnect link for each direction of a chip is obtained. The optical signals from a processor element chip can be transmitted to another chip through optical waveguide interconnect embedded in the board. Thus the optical interconnect mesh network for parallel multiprocessor system can be implemented.