A new approach to develop large-scale land-use models using publicly available data

2021 ◽  
pp. 239980832199939
Author(s):  
Ali R Samani ◽  
Sabyasachee Mishra ◽  
David J-H Lee ◽  
Mihalis M Golias ◽  
Jerry Everett
Keyword(s):  
Land Use ◽  
Large Scale ◽  
Structural Changes ◽  
Computational Time ◽  
Percentage Error ◽  
Good Prediction ◽  
Reasonable Accuracy ◽  
Land Utilization ◽  
Land Use Model ◽  
Proposed Model

Developing a land-use model for large-scale cases is a topic that has received less attention in the literature, while it is crucial for transportation engineers and urban planners to analyze the effect of various policies in multi-jurisdiction metropolitan areas and to some extent on a statewide scale. While gravity-based models are too simplistic, microsimulation models require extensive data and massive computation. This paper presents a land-use model that can be applied to large-scale geographies using publicly available data and be able to forecast demographic and socioeconomic attributes with reasonable accuracy and acceptable computational time. The proposed model incorporates Putman’s Integrated Transportation–Land-Use Package (ITLUP) and Kockelman’s Gravity-based Land-Use Model (G-LUM) fundamentals with enhanced formulation of newly added variables and structural changes. Considering the nonconvex and nonlinear nature of the proposed model, we utilize an enhanced genetic algorithm for base year calibration. Further, we assess the accuracy of the model with backcasting validation. We utilize the state of Tennessee as the case study area and utilized all open-source data available to the model application. The model results show reasonably accurate estimates of households by size, employment by industry, and land utilization by condition. As applicable, the model outperforms G-LUM by accuracy ( R2 and Percentage of Good Prediction ( PGP)) and error measures (Mean Absolute Percentage Error ( MAPE)). The proposed land-use model has the potential to be applied for medium to large-scale geographies with reasonable accuracy in predicting socioeconomic, demographic, and land condition estimates by using publicly available data.

scholarly journals A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

2018 ◽  
Author(s):  
Feng Jie Zheng ◽  
Fu Zheng Qu ◽  
Xue Guan Song
Keyword(s):  
Pressure Fluctuation ◽  
Large Scale ◽  
Three Dimensional ◽  
Industrial Process ◽  
Multiscale Model ◽  
Computational Time ◽  
Small Scale ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Proposed Model

Reservoir-pipe-valve (RPV) systems are widely used in many industrial process. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operation such as rapid valve opening/closing. To investigate the pressure especially the pressure fluctuation in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled by a zero-dimensional virtual point, the pipe is modeled by a one-dimensional MOC, and the valve is modeled by a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted, in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve are obtained. The results show that the proposed model is in good agreement with the full CFD model in both large-scale and small-scale spaces. Moreover, the proposed model is more computationally efficient than the CFD model, which provides a feasibility in the analysis of complex RPV system within an affordable computational time.

scholarly journals A Multidimensional and Multiscale Model for Pressure Analysis in a Reservoir-Pipe-Valve System

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Feng Jie Zheng ◽  
Chao Yong Zong ◽  
William Dempster ◽  
Fu Zheng Qu ◽  
Xue Guan Song
Keyword(s):  
Large Scale ◽  
Pressure Fluctuations ◽  
Three Dimensional ◽  
Multiscale Model ◽  
Computational Time ◽  
Small Scale ◽  
Dimensional System ◽  
Cfd Model ◽  
Computationally Efficient ◽  
Proposed Model

Reservoir-pipe-valve (RPV) systems are widely used in many industrial processes. The pressure in an RPV system plays an important role in the safe operation of the system, especially during the sudden operations such as rapid valve opening or closing. To investigate the pressure response, with particular interest in the pressure fluctuations in an RPV system, a multidimensional and multiscale model combining the method of characteristics (MOC) and computational fluid dynamics (CFD) method is proposed. In the model, the reservoir is modeled as a zero-dimensional virtual point, the pipe is modeled as a one-dimensional system using the MOC, and the valve is modeled using a three-dimensional CFD model. An interface model is used to connect the multidimensional and multiscale model. Based on the model, a transient simulation of the turbulent flow in an RPV system is conducted in which not only the pressure fluctuation in the pipe but also the detailed pressure distribution in the valve is obtained. The results show that the proposed model is in good agreement when compared with a high fidelity CFD model used to represent both large-scale and small-scale spaces. As expected, the proposed model is significantly more computationally efficient than the CFD model. This demonstrates the feasibility of analyzing complex RPV systems within an affordable computational time.

Resource levelling in projects considering different activity execution modes and splitting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Khalilzadeh
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Programming Model ◽  
Exact Method ◽  
Computational Time ◽  
Resource Usage ◽  
Optimal Solutions ◽  
Content Type ◽  
Proposed Model ◽  
Multi Mode

Purpose This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources. Design/methodology/approach The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time. Findings The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon. Originality/value There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

The Membrane-Computing-Based Automatic Online Marking-Management Model and its Application

2014 ◽  
Vol 1079-1080 ◽  
pp. 1245-1252
Author(s):  
Yu Zhen Zhao ◽  
Xi Yu Liu ◽  
Wen Ping Wang
Keyword(s):  
Standardized Tests ◽  
Error Control ◽  
Large Scale ◽  
Membrane Computing ◽  
Membrane System ◽  
Experimental Result ◽  
Management Model ◽  
Computational Time ◽  
P System ◽  
Proposed Model

The online marking system, which can improve the marking speed and quality, is widely applied in large-scale standardized tests. The P system had great parallelism is applied in the engineering systems just beginning. In this paper, an automatic online marking-management model (AOMMM) based on membrane computing is constructed, which can find marker had quality problems and remind the markers and the leader automatically. According to the basal rules of membrane computing, the management rules of the proposed AOMMM are designed. Through the analysis of the computational time complexity of the proposed model, we find that only three steps (O (1)) are needed to complete the feedback operation. Experimental result shows that the proposed model is appropriate for online marking management. With benefiting from the advantage of membrane system, the proposed OMMAM implements automatic and real time feedback operation.Key-Words: -standardized tests; online marking; error; control; membrane computing; P system; management automation; membrane system

scholarly journals Kebijakan Pertanahan pada Era Masyarakat Ekonomi Asean (MEA)

2016 ◽  
Vol 2 (2) ◽  
pp. 227
Author(s):  
Hadi Arnowo ◽  
Djudjuk Tri Handayani
Keyword(s):  
Land Use ◽  
Poverty Reduction ◽  
Large Scale ◽  
Small And Medium Enterprises ◽  
Community Empowerment ◽  
Land Utilization ◽  
Goods And Services ◽  
The Government ◽  
Medium Enterprises ◽  
The Impact

Abstract : ASEAN Economic Community (AEC) has the aims to transform ASEAN into a stable, prosperous, and highly competitive region through equitable economic development, poverty reduction and socio-economic disparities. As a consequence, since 2015, ASEAN becomes region with free trade of goods, services, investment, and skilled labor. The impact of the implementation of MEA is shown by the price and quality of goods and services that become competitive, affecting domestic products and services from Small and Medium Enterprises (SMEs), as well as farmers. The government has sought to support SMEs and farmers through direct and indirect assistance. Land is the sector that has the most influential effect among other sectors to provoke economic growth of SMEs and farmers, because land has the function as natural resource as well as work location. To optimize this, land policies are needed to regulate land use arrangement, land asset legalization, control of land utilization and community empowerment through access reform. Asset legalization for SMEs and farmers, as well as asset legalization for infrastructures. Land use and land utilization control was implemented to optimize large-scale agricultural area by set up cooperation scheme with local people. Community empowerment was implemented through access reform, prioritized to the regions that have leading commodities or products. Keywords : competition, land use arrangement, assets legalization, land utilization, community empowermentIntisari : Pembentukan Masyarakat Ekonomi ASEAN (MEA) bertujuan untuk mentransformasikan ASEAN menjadi kawasan yang stabil, makmur, dan sangat kompetitif melalui ekonomi pembangunan yang adil, berkurangnya kemiskinan dan kesenjangan sosial-ekonomi. Sebagai konsekuensinya, ASEAN menjadi daerah dengan pergerakan bebas barang, jasa, investasi, tenaga kerja terampil, dan aliran modal sejak tahun 2015.Dampak pemberlakuan MEA adalah terjadinya persaingan harga dan kualitas barang dan jasa yang berpengaruh terhadap produk dan jasa domestik yang umumnya dilakukan oleh pelaku Usaha Kecil dan Menengah (UKM) dan petani. Pemerintah telah berupaya membantu pelaku UKM dan petani melalui bantuan langsung dan tidak langsung. Sektor pertanahan adalah sektor yang paling berpengaruh terhadap tumbuhnya perekonomian pelaku UKM dan petani. Hal tersebut karena tanah merupakan sumberdaya alam sekaligus ruang tempat berusaha. Agar dapat membantu pelaku UKM dan petani diperlukan langkah–langkah kebijakan pertanahan berupa penatagunaan tanah, legalisasi aset, pengendalian pemanfaatan tanah, dan pemberdayaan masyarakat melalui akses reform. Legalisasi aset bagi pelaku UKM dan petani serta legalisasi aset untuk infrastruktur. Pengendalian pemanfaatan tanah adalah untuk mengoptimalkan tanah pertanian berskala besar melalui kerjasama dengan penduduk sekitar. Pemberdayaan masyarakat dilaksanakan dengan menfasilitasi UKM dan petani untuk memperoleh akses permodalan dimana prioritas ditujukan pada lokasi–lokasi yang memiliki produk atau komoditi unggulan. Kata Kunci : kompetisi, penatagunaan tanah, legalisasi aset, pemanfaatan tanah, pemberdayaan masyarakat

Combustion Driven by Fragment-based Ab Initio Molecular Dynamics Simulation

2019 ◽  
Author(s):  
Liqun Cao ◽  
Jinzhe Zeng ◽  
Mingyuan Xu ◽  
Chih-Hao Chin ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ab Initio ◽  
Large Scale ◽  
Methane Combustion ◽  
Combustion Method ◽  
Dynamics Simulation ◽  
Ab Initio Molecular Dynamics ◽  
Computational Time ◽  
Combustion Mechanism ◽  
Daily Lives

Combustion is a kind of important reaction that affects people's daily lives and the development of aerospace. Exploring the reaction mechanism contributes to the understanding of combustion and the more efficient use of fuels. Ab initio quantum mechanical (QM) calculation is precise but limited by its computational time for large-scale systems. In order to carry out reactive molecular dynamics (MD) simulation for combustion accurately and quickly, we develop the MFCC-combustion method in this study, which calculates the interaction between atoms using QM method at the level of MN15/6-31G(d). Each molecule in systems is treated as a fragment, and when the distance between any two atoms in different molecules is greater than 3.5 Å, a new fragment involved two molecules is produced in order to consider the two-body interaction. The deviations of MFCC-combustion from full system calculations are within a few kcal/mol, and the result clearly shows that the calculated energies of the different systems using MFCC-combustion are close to converging after the distance thresholds are larger than 3.5 Å for the two-body QM interactions. The methane combustion was studied with the MFCC-combustion method to explore the combustion mechanism of the methane-oxygen system.

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Enriching plant diversity in grasslands by large-scale experimental sward disturbance and seed addition along gradients of land-use intensity

2016 ◽  
pp. rtw062 ◽  
Author(s):  
Valentin H. Klaus ◽  
Deborah Schäfer ◽  
Till Kleinebecker ◽  
Markus Fischer ◽  
Daniel Prati ◽  
...  
Keyword(s):  
Land Use ◽  
Plant Diversity ◽  
Large Scale ◽  
Land Use Intensity ◽  
Seed Addition

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

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