scholarly journals ANALISIS SISTEM DINAMIK USAHATANI TANAM SAYURAN BERKELANJUTAN BERBASIS PENGENDALIAN HAMA TERPADU DI KABUPATEN TANGGAMUS, PROVINSI LAMPUNG

2019 ◽  
Vol 15 (2) ◽  
pp. 277
Author(s):  
Sudiono Sudiono ◽  
S. H. Sutjahyo ◽  
P. Hidayat ◽  
R. Kurniawan
Keyword(s):  
Dynamic Model ◽  
System Analysis ◽  
Model Simulation ◽  
Model Development ◽  
Problem Formulation ◽  
Model Parameters ◽  
Vegetable Crops ◽  
Land Area ◽  
Analysis Problem ◽  
Current Scenario

The purpose of this study was to develop a dynamic model of sustainable farming based on an integrated pest management system in upland vegetable crops in Tanggamus Regency, Lampung Province. Dynamic system analysis with powersim with the stages of model development, namely needs analysis, problem formulation, system identification, model simulation, and model testing. The results of the compilation of the dynamic model parameters, namely the current scenario (without intervention), in 2017 farmer households amounted to 104,929 households which increased in 2030 to 128,613 households farmers' income at the end of the simulation period to Rp 434,526,807 from a land area of 4,029 ha, scenario pessimistic in 2017 farmer households amounted to 100,753 households which experienced an increase in 2030 to 116,252 households with income in this scenario to Rp 470,170,405 from a land area of 4,243 ha, and an optimistic scenario for 2017 farmers' households amounting to 100,111 households that had increased in the year 2030 became 107,892 households with total farmer's income of Rp 508,916,172 on an area of 4,464 ha.

scholarly journals SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks

2017 ◽  
Author(s):  
Piero Dalle Pezze ◽  
Nicolas Le Novère
Keyword(s):  
Parameter Estimation ◽  
Model Building ◽  
Model Simulation ◽  
Profile Likelihood ◽  
Model Development ◽  
Software Tool ◽  
Model Parameters ◽  
Repeat Sequences ◽  
Parameter Estimations ◽  
Consistent Manner

AbstractBackground: The rapid growth of the number of mathematical models in Systems Biology fostered the development of many tools to simulate and analyse them. The reliability and precision of these tasks often depend on multiple repetitions and they can be optimised if executed as pipelines. In addition, new formal analyses can be performed on these repeat sequences, revealing important insights about the accuracy of model predictions.Results: Here we introduce SBpipe, an open source software tool for automating repetitive tasks in model building and simulation. Using basic configuration files, SBpipe builds a sequence of repeated model simulations or parameter estimations, performs analyses from this generated sequence, and finally generates a LaTeX/PDF report. The parameter estimation pipeline offers analyses of parameter profile likelihood and parameter correlation using samples from the computed estimates. Specific pipelines for scanning of one or two model parameters at the same time are also provided. Pipelines can run on multicore computers, Sun Grid Engine (SGE), or Load Sharing Facility (LSF) clusters, speeding up the processes of model building and simulation. SBpipe can execute models implemented in Copasi, Python or coded in any other programming language using Python as a wrapper module. Future support for other software simulators can be dynamically added without affecting the current implementation.Conclusions: SBpipe allows users to automatically repeat the tasks of model simulation and parameter estimation, and extract robustness information from these repeat sequences in a solid and consistent manner, facilitating model development and analysis. The source code and documentation of this project are freely available at the web site: https://pdp10.github.io/sbpipe/.

scholarly journals Characterization of Moisture Diffusion in Cured Concrete Slabs at Early Ages

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Zhang ◽  
Hongduo Zhao
Keyword(s):  
Process Model ◽  
Model Simulation ◽  
Moisture Diffusion ◽  
Experimental Results ◽  
Concrete Slabs ◽  
Model Parameters ◽  
Early Age ◽  
Water Curing ◽  
Early Age Concrete

The objective of this paper is to investigate the characterization of moisture diffusion inside early-age concrete slabs subjected to curing. Time-dependent relative humidity (RH) distributions of three mixture proportions subjected to three different curing methods (i.e., air curing, water curing, and membrane-forming compounds curing) and sealed condition were measured for 28 days. A one-dimensional nonlinear moisture diffusion partial differential equation (PDE) based on Fick’s second law, which incorporates the effect of curing in the Dirichlet boundary condition using a concept of curing factor, is developed to simulate the diffusion process. Model parameters are calibrated by a genetic algorithm (GA). Experimental results show that the RH reducing rate inside concrete under air curing is greater than the rates under membrane-forming compound curing and water curing. It is shown that the effect of water-to-cement (w/c) ratio on self-desiccation is significant. Lower w/c ratio tends to result in larger RH reduction. RH reduction considering both effect of diffusion and self-desiccation in early-age concrete is not sensitive to w/c ratio, but to curing method. Comparison between model simulation and experimental results indicates that the improved model is able to reflect the effect of curing on moisture diffusion in early-age concrete slabs.

AN ARTIFICIAL INTELLIGENT APPROACH TO TRAFFIC ACCIDENT ESTIMATION: MODEL DEVELOPMENT AND APPLICATION

Transport ◽  
2009 ◽  
Vol 24 (2) ◽  
pp. 135-142 ◽  
Author(s):  
Ali Payıdar Akgüngör ◽  
Erdem Doğan
Keyword(s):  
Genetic Algorithm ◽  
Model Development ◽  
Back Propagation ◽  
Linear Functions ◽  
Model Parameters ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Estimation Model ◽  
Average Growth ◽  
Feed Forward Back Propagation

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half‐fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

Dynamic model development based on experimental investigations of acoustically levitated suspension droplets

2021 ◽  
Vol 171 ◽  
pp. 121057
Author(s):  
Moritz Buchholz ◽  
Johannes Haus ◽  
Fritz Polt ◽  
Swantje Pietsch ◽  
Michael Schönherr ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Model Development ◽  
Experimental Investigations

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

scholarly journals Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

2017 ◽  
Vol 14 (12) ◽  
pp. 3129-3155 ◽  
Author(s):  
Hakase Hayashida ◽  
Nadja Steiner ◽  
Adam Monahan ◽  
Virginie Galindo ◽  
Martine Lizotte ◽  
...  
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Dimethyl Sulfide ◽  
Phytoplankton Bloom ◽  
Model Simulation ◽  
Field Measurements ◽  
Sulfur Cycle ◽  
The Arctic ◽  
Model Parameters ◽  
Ocean Ecosystem

Abstract. Sea ice represents an additional oceanic source of the climatically active gas dimethyl sulfide (DMS) for the Arctic atmosphere. To what extent this source contributes to the dynamics of summertime Arctic clouds is, however, not known due to scarcity of field measurements. In this study, we developed a coupled sea ice–ocean ecosystem–sulfur cycle model to investigate the potential impact of bottom-ice DMS and its precursor dimethylsulfoniopropionate (DMSP) on the oceanic production and emissions of DMS in the Arctic. The results of the 1-D model simulation were compared with field data collected during May and June of 2010 in Resolute Passage. Our results reproduced the accumulation of DMS and DMSP in the bottom ice during the development of an ice algal bloom. The release of these sulfur species took place predominantly during the earlier phase of the melt period, resulting in an increase of DMS and DMSP in the underlying water column prior to the onset of an under-ice phytoplankton bloom. Production and removal rates of processes considered in the model are analyzed to identify the processes dominating the budgets of DMS and DMSP both in the bottom ice and the underlying water column. When openings in the ice were taken into account, the simulated sea–air DMS flux during the melt period was dominated by episodic spikes of up to 8.1 µmol m−2 d−1. Further model simulations were conducted to assess the effects of the incorporation of sea-ice biogeochemistry on DMS production and emissions, as well as the sensitivity of our results to changes of uncertain model parameters of the sea-ice sulfur cycle. The results highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that should be better constrained by new observations.

Analytical and experimental analysis of axial force generated by a drive shaft system

2020 ◽  
Vol 234 (4) ◽  
pp. 691-706 ◽  
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja ◽  
Wen-Bin Shangguan
Keyword(s):  
Dynamic Model ◽  
Axial Force ◽  
Friction Model ◽  
Drive Shaft ◽  
Contact Model ◽  
Model Parameters ◽  
Shaft System ◽  
Multibody Dynamic ◽  
Generated Axial Force ◽  
Operational Factors

The drive shaft system with a tripod joint is known to cause lateral vibration in a vehicle due to the axial force generated by various contact pairs of the tripod joint. The magnitude of the generated axial force, however, is related to various operating factors of the drive shaft system in a complex manner. The generated axial force due to a drive shaft system with a tripod joint and a ball joint was experimentally characterized considering ranges of operational factors, namely, the input toque, the shaft rotational speed, the articulation angle, and the friction. The data were analyzed to establish an understanding of the operational factors on the generated axial force. Owing to the observed significant effects of all the factors, a multibody dynamic model of the drive shaft system was formulated for predicting generated axial force under different operating conditions. The model integrated the roller–track contact model and the velocity-based friction model. Based on a quasi-static finite element model, a new methodology was proposed for identifying the roller–track contact model parameters, namely, the contact stiffness and force index. To further enhance the calculation accuracy of the multibody dynamic model, a new methodology for identifying the friction model parameters and the force index was proposed by using the measured data. The validity of the model was demonstrated by comparing the model-predicted and measured magnitudes of generated axial force for the ranges of operating factors considered. The results showed that the generated axial force of the drive shaft system can be calculated more accurately and effectively by using the identified friction and contact parameters in the paper.

scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Model Development ◽  
Biogeochemical Cycling ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Glacier Forefield ◽  
Unconstrained Model

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

scholarly journals Training on ant sugar production to increase people's income in Ba'ka Village, Enrekang

2021 ◽  
Vol 6 (7) ◽  
pp. 1133-1138
Author(s):  
Irman Syarif ◽  
Agusriandi Agusriandi ◽  
Elihami Elihami ◽  
Ita Sarmita Samad ◽  
Sry Wahyuni R
Keyword(s):  
Community Service ◽  
Program Implementation ◽  
Problem Formulation ◽  
Needs Analysis ◽  
Sugar Production ◽  
Analysis Problem ◽  
Work Program ◽  
The People ◽  
Service Activity ◽  
Palm Sugar

Most of the people of Ba'ka Village are conventional palm sugar farmers whose whose selling prices are cheap. The purpose of this service is to provide innovation in palm sugar into ant sugar. Through training in making ant sugar, it is hoped that it can increase people's income. The stages of community service include needs analysis, problem formulation, work program formulation, and work program implementation, and evaluation. This service activity produces ant sugar products that are packaged in a modern way. Ant sugar products can attract consumers because they are more durable, hygienic, and practical.

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