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Journal articles on the topic 'Input-output model analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Nakajima, Kenichi, Shinichiro Nakamura, Kazuyo Matsubae-Yokoyama, Yasushi Kondo, and Tetsuya Nagasaka. "Waste Input-Output Material Flow Analysis Model." Material Cycles and Waste Management Research 20, no. 5 (2009): 206–11. http://dx.doi.org/10.3985/mcwmr.20.206.

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2

West, Guy R. "A Stochastic Analysis of an Input-Output Model." Econometrica 54, no. 2 (1986): 363. http://dx.doi.org/10.2307/1913156.

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3

ten Raa, Thijs, and Mark F. J. Steel. "Revised stochastic analysis of an input-output model." Regional Science and Urban Economics 24, no. 3 (1994): 361–71. http://dx.doi.org/10.1016/0166-0462(93)02039-6.

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4

Holland, D. W., and R. P. Martin. "Output Change in U.S. Agriculture: An Input-Output Analysis." Journal of Agricultural and Applied Economics 25, no. 2 (1993): 69–81. http://dx.doi.org/10.1017/s1074070800018976.

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AbstractThis paper analyzes output changes in the U.S. agricultural economy from 1972 to 1977 using a 477-sector input-output framework. The empirical model is based on benchmark input-output data from the U.S. Bureau of Economic Analysis for 1972 and 1977. Output changes were decomposed into components attributable to technical change, domestic final demand change, export demand change and import substitution. A major advantage of the decomposition is its ability to identify the output change in a given sector due to general equilibrium effects in all sectors.

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5

Gibson, J. S., and Faryar Jabbari. "A digital input/output model for trace-class systems." Journal of Mathematical Analysis and Applications 144, no. 1 (1989): 89–108. http://dx.doi.org/10.1016/0022-247x(89)90361-2.

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6

Jiang, Lei, Shou Zhong Hu, and Xiao Xiao Xu. "Input-Output Analysis of Environmental Protection Industry." Advanced Materials Research 1073-1076 (December 2014): 2700–2703. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.2700.

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This paper investigates the run of environmental protection industry input-output model. A new mathematic method is applied to study this kind of singular input-output system. With this new method, we need not convert singular systems into general linear systems. A sufficient stability condition under which an environmental protection industry input-output model is stable is proved. This condition is in the form of linear matrix inequality and can be easily tested by computers.

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LIU, Jing, and Katsuya NAGATA. "SO2 Emission Analysis of China with Input-Output Model." Journal of Environment and Engineering 6, no. 3 (2011): 490–98. http://dx.doi.org/10.1299/jee.6.490.

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8

Ngaruko, Deus D. "Comparative Analysis of Alternative ODL Delivery Modes using Input-Process-Output Model in ODL Institutions: Evidence From The Open University Of Tanzania." Advances in Social Sciences Research Journal 1, no. 5 (2014): 94–106. http://dx.doi.org/10.14738/assrj.15.444.

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9

Jia, Li, Yangyang Li, and Feng Li. "Correlation Analysis Algorithm-Based Multiple-Input Single-Output Wiener Model with Output Noise." Complexity 2019 (November 12, 2019): 1–13. http://dx.doi.org/10.1155/2019/9650254.

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A novel identification algorithm for the correlation analysis-based multiple-input single-output (MISO) neurofuzzy Wiener model with noise is proposed. Firstly, several sets of Gaussian signals are utilized to realize the decoupling between the dynamic linear blocks and the static nonlinear blocks of a MISO Wiener system. Then, the correlation analysis is adopted to identify the parameters of the linear parts, and the problem that the output of static nonlinear block is immeasurable can be solved. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative approach used for the identification of the Wiener model. Next, the least-squares algorithm is employed to estimate the consequent parameters of the nonlinear blocks which can avoid the influence of noise. Finally, examples are used to illustrate the performance of the proposed method.

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10

Ivanter, V. V. "Role of Input-Output Model in Macroeconomic Analysis and Forecasting." Studies on Russian Economic Development 29, no. 6 (2018): 581–83. http://dx.doi.org/10.1134/s1075700718060047.

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11

Schäfer, Dieter, and Carsten Stahmer. "Input–Output Model for the Analysis of Environmental Protection Activities." Economic Systems Research 1, no. 2 (1989): 203–28. http://dx.doi.org/10.1080/09535318900000015.

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12

Santos, Joost R., Mark J. Orsi, and Erik J. Bond. "Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model." Risk Analysis 29, no. 12 (2009): 1743–58. http://dx.doi.org/10.1111/j.1539-6924.2009.01328.x.

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13

Zhou, P., L. W. Fan, and H. W. Tang. "On stability analysis of multiple objective dynamic input–output model." Applied Mathematics and Computation 177, no. 1 (2006): 79–84. http://dx.doi.org/10.1016/j.amc.2005.10.033.

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14

Son, Hyesook, Seokyeon Kim, Hanbyul Yeon, Yejin Kim, Yun Jang, and Seung-Eock Kim. "Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models." Applied Sciences 11, no. 13 (2021): 5853. http://dx.doi.org/10.3390/app11135853.

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The output of a deep-learning model delivers different predictions depending on the input of the deep learning model. In particular, the input characteristics might affect the output of a deep learning model. When predicting data that are measured with sensors in multiple locations, it is necessary to train a deep learning model with spatiotemporal characteristics of the data. Additionally, since not all of the data measured together result in increasing the accuracy of the deep learning model, we need to utilize the correlation characteristics between the data features. However, it is difficult to interpret the deep learning output, depending on the input characteristics. Therefore, it is necessary to analyze how the input characteristics affect prediction results to interpret deep learning models. In this paper, we propose a visualization system to analyze deep learning models with air pollution data. The proposed system visualizes the predictions according to the input characteristics. The input characteristics include space-time and data features, and we apply temporal prediction networks, including gated recurrent units (GRU), long short term memory (LSTM), and spatiotemporal prediction networks (convolutional LSTM) as deep learning models. We interpret the output according to the characteristics of input to show the effectiveness of the system.

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15

Koesdijati, Titiek, and Andarmadi Jati Abdhi Wasesa. "Efficiency Distribution Analysis with Data Envelopment Analysis (DEA) Approach Fertilizer." Tibuana 4, no. 02 (2021): 91–98. http://dx.doi.org/10.36456/tibuana.4.02.4010.91-98.

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Distribution channels have an important meaning for achieving company success in the field of marketing so that company management is required to always be responsive and able to adapt to environmental changes. Input and output data processing is done by giving weights to the input and output using the DEA CCR primal model by maximizing the input-oriented-based objective function. The results of processing the efficiency scale show the relative efficiency level of the scale of each DMU in the company. The efficiency scale is obtained through the formulation of the DEA CCR primal model between each DMU input and output. If the DMU gets an input and output efficiency value of less than 100%, then the DMU is said to be relatively inefficient. Meanwhile, if the efficient value is equal to 100%, then the DMU is said to be relatively efficient. Of the 5 distribution cities, Sidoarjo, PaluKendari, Bandung and Lamongan which were analyzed, there were 2 cities that were inefficient or experiencing waste in their input and output variables, so the company needed to reorganize the level of use of inputs and outputs it achieved and utilize them optimally to get output that is optimal. targeted.

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16

Mansikka, Heikki, Don Harris, and Kai Virtanen. "An Input–Process–Output Model of Pilot Core Competencies." Aviation Psychology and Applied Human Factors 7, no. 2 (2017): 78–85. http://dx.doi.org/10.1027/2192-0923/a000120.

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Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

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17

Arifatul Ulya, Nur, and Syafrul Yunardy. "ANALISIS PERANAN SEKTOR KEHUTANAN DALAM PEREKONOMIAN INDONESIA: SEBUAH PENDEKATAN MODEL INPUT-OUTPUT." Jurnal Penelitian Sosial dan Ekonomi Kehutanan 3, no. 1 (2006): 61–74. http://dx.doi.org/10.20886/jpsek.2006.3.1.61-74.

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18

Barpanda, Saswat, and Neena Sreekumar. "Performance Analysis of Hospitals in Kerala Using Data Envelopment Analysis Model." Journal of Health Management 22, no. 1 (2020): 25–40. http://dx.doi.org/10.1177/0972063420908372.

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Performance analysis in any industry plays a vital role in understanding the current scenario and thereby improving the overall efficiency. Using a sample of 20 hospitals randomly selected in Kerala, performance measures of quality were examined as they related to technical efficiency. Efficiency scores for the study hospitals were computed using data envelopment analysis (DEA). The study found that the technically efficient hospitals were performing well as far as quality measures were concerned. DEA can be used to benchmark both dimensions of hospital performance, that is, technical efficiency and quality. The variables selected for the study were divided under input and output measures. Using the DEA model, the factors considered were weighed and analysis was done. The input variables under study are bed number, trained medical staff and services offered. The output variables considered were outpatient rate, mortality rate and number of surgical operations in a month. Through the study, performance of each hospital is measured, and it aims to find out a relation between the input and output variables.

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19

Jewczak, Maciej, and Jadwiga Suchecka. "Application Of Input-Output Analysis In The Health Care." Comparative Economic Research. Central and Eastern Europe 17, no. 4 (2014): 87–104. http://dx.doi.org/10.2478/cer-2014-0034.

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Usage of the economic analysis in the study of the performance of health care system does not surprise anyone nowadays. Trends that are drawn over the years fluctuate from the technology assessment of health programs – in terms of efficiency, costs or utility for patients, through methods to establishing copayment for health services and the demand for medical services. Much of the interest is devoted to analysis of the shape of the health care system: the amount of contributions to the National Health Fund, the managing the system, both at the micro and macro level, or restructuring. Any method that allows to show dependencies, identify weaknesses/strengths of the health care system is appreciated by health policy makers. The aim of this article is an attempt of the use of models of input-output type in the analysis of the performance of the health care sector in Poland. The construction of input-output model is based on the observed data for the specified, variously defined area – it may concern: country, region, municipality, etc., hence with the appropriate designed database, it may be possible to examine the flow of health benefits – for example, expressed in zlotys. Part of the article is dedicated to theoretical aspects of the input-output models and the problems this usage can cause.

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20

KANAGAWA, Taku, Tohru FUTAWATARI, and Hdefumi IMURA. "Urban energy and environmental analysis based on an input-output model." ENVIRONMENTAL SYSTEMS RESEARCH 19 (1991): 70–75. http://dx.doi.org/10.2208/proer1988.19.70.

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21

Lijiang, Zhu. "Linear spectral mixture analysis with the Open Leontief Input-Output Model." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012223. http://dx.doi.org/10.1088/1755-1315/17/1/012223.

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22

Wijayanto, Feri. "Clustering Analysis on Indonesian Education Quality Performance Using Input-Output Model." Advanced Science Letters 22, no. 10 (2016): 2799–803. http://dx.doi.org/10.1166/asl.2016.7127.

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23

Percoco, Marco. "On the Local Sensitivity Analysis of the Inoperability Input-Output Model." Risk Analysis 31, no. 7 (2011): 1038–42. http://dx.doi.org/10.1111/j.1539-6924.2010.01574.x.

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24

Hu, Junmei, Gengyuan Liu, Fanxin Meng, Yuanchao Hu, and Marco Casazza. "Subnational carbon flow pattern analysis using multi-scale input-output model." Ecological Modelling 431 (September 2020): 109138. http://dx.doi.org/10.1016/j.ecolmodel.2020.109138.

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25

Takase, Koji, Yasushi Kondo, and Ayu Washizu. "An Analysis of Sustainable Consumption by the Waste Input-Output Model." Journal of Industrial Ecology 9, no. 1-2 (2008): 201–19. http://dx.doi.org/10.1162/1088198054084653.

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26

Boussif, Abderraouf, and Mohamed Ghazel. "Diagnosability Analysis of Input/Output Discrete-Event Systems Using Model-Checking." IFAC-PapersOnLine 48, no. 7 (2015): 71–78. http://dx.doi.org/10.1016/j.ifacol.2015.06.475.

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27

Lu, Changxiang, Shaochuan Fu, Jiaqi Fang, Jikai Huang, and Yong Ye. "Analysis of Factors Affecting Freight Demand Based on Input-Output Model." Mathematical Problems in Engineering 2021 (March 29, 2021): 1–19. http://dx.doi.org/10.1155/2021/5581742.

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Freight demand is a highly variable process over economic and industrial structure, and accurate freight demand forecasting is the basis of transportation planning. In order to clarify the influencing factors of freight volume so as to analyze and predict the change trend of freight volume accurately, this paper analyzes the impact of changes in economic, industrial structure, and complete consumption coefficients on freight demand, through constructing an input-output model for transportation value analysis and forecasting freight volume by fitting data of transportation value and freight traffic. Studies have shown that the growth in economic aggregate is the main reason for the increase in the value of transportation, and the change in the complete consumption coefficient is the main reason for the increase in freight traffic.

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28

Bendat, Julius S. "Spectral Techniques for Nonlinear System Analysis and Identification." Shock and Vibration 1, no. 1 (1993): 21–31. http://dx.doi.org/10.1155/1993/438416.

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This article reviews some recent and current research work with emphasis on new recommended spectral techniques that can analyze and identify the optimum linear and nonlinear system properties in a large class of single-input/single-output nonlinear models by using experimentally measured input/output random data. This is done by showing how to replace these nonlinear models with equivalent multiple-input/single-output linear models that are solvable by well-established practical procedures. The input random data can have probability density functions that are Gaussian or non-Gaussian with arbitrary spectral properties. Results in this article prove that serious errors can occur when conventional linear model analysis procedures are used to determine the physical properties of nonlinear systems.

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29

Toroptsev, E. L., A. S. Marakhovskii, and R. R. Duszynski. "The problem of digitalization of the dynamic input-output model." Economic Analysis: Theory and Practice 16, no. 5 (2020): 946–72. http://dx.doi.org/10.24891/ea.19.5.946.

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Subject. The article addresses the digitalization of the dynamic model of inter industry balance. Objectives. We focus on developing our own statistical research base for input-output analysis, structural forecasting, sustainability, economic dynamics and economic growth. Mathematically, the study is formalized by the Cauchy problem for ordinary differential equations. Methods. The methodology components include theoretical and practical bases of the systems, statistical, input-output, and structural dynamic analysis. Results. Based on official statistics, we solved the problem of digitalization of the dynamic model of input-output balance, written in the form of a system of differential equations. For the first time, this model was transferred from a set of purely theoretical structures to a class of computable models. We developed a sequence of coordinated actions and calculations, which serve as a methodology for the said transfer. We also devised and presented the elements of our own statistical research base. Conclusions. The quantitative measurement of the dynamic inter-industry model in the form of a system of differential equations opens up broad perspectives on the sustainability of macroeconomics, its structural readiness for expanded reproduction, i.e. economic growth. The model can be used both independently and in combination with equilibrium and other agent-oriented models.

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Li, Wei, Fuzhong Wu, Huixin Jin, and Shuie Li. "Analysis of Fe and Mn impurities in Chinese sponge titanium enterprise using an input-output model." Metallurgical Research & Technology 117, no. 4 (2020): 401. http://dx.doi.org/10.1051/metal/2020031.

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The impurity content is an important index reflecting the quality of sponge titanium, especially Fe and Mn impurities. However, at present, only qualitative research has been conducted on the input of impurities, and no quantitative research has been conducted on the input and output of impurities. Therefore, based on the input-output model, this study analyzes the input and output of typical impurities in the magnesiothermic reduction-vacuum distillation process, product sorting process, and magnesium processing process, respectively. Quantitative characterization of the input and output of impurities in sponge titanium production. The results show that the Fe impurity is mainly input by the equipment, and the Mn impurity is mainly input from the raw materials. Determined the chemical reactions of impurities Fe and Mn during reduction, impurities of Fe and Mn exist in the product as the form of simple substance after the reaction.

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31

Vittal, B., Raju Nellutla, and M. Krishna Reddy. "Selection and Analysis of Input-Output Variables using Data Envelopment Analysis of Decision Making Units - Indian Private Sector Banks." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 119–27. http://dx.doi.org/10.35940/ijeat.e2674.0610521.

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In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate inputoutput variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

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32

Li Dewhurst, J. H. "Intensive Income in Demo-Economic input-output Models." Environment and Planning A: Economy and Space 22, no. 1 (1990): 119–28. http://dx.doi.org/10.1068/a220119.

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In this paper, the treatment of intensive income in extended input-output models of a ‘demo-economic’ nature is discussed. In the first section of the paper a model is presented that allows for intensive, extensive, redistributive, and exogenous income changes in response to an increase in exogenous final demand for the output of an economy. It is shown that use of Blackwell's method of estimating intensive and extensive income changes in such a model implies a particular growth path for average earnings. In the second section the possibility of intensive income changes arising from increased utilisation of labour is examined. The existence of underutilisation of labour is shown to have a marked effect on output and employment multipliers. Some comments are made on the practical implications of the analysis, illustrated by a simple model of the Queensland economy.

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Artukoglu, M. M., A. Olgun, and H. Adanacioglu. "The efficiency analysis of organic and conventional olive farms: Case of Turkey." Agricultural Economics (Zemědělská ekonomika) 56, No. 2 (2010): 89–96. http://dx.doi.org/10.17221/620-agricecon.

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This paper investigates technical and economically efficiency of 62 organic and 62 conventional olive producing farms in Turkey. According to the study results; by using the CRS model which is input and output-oriented, the average technical efficiency of organic olive farms is 67.68%, the average technical efficiency of conventional olive farms is 47.93%. The technical efficiency of the output-oriented VRS model is 74.78%, and the technical efficiency of the input-oriented VRS model is 93.46%. Also, considering the same model, the average efficiency of the conventional olive farms in the input and output are 59.58% and 94.97%, respectively. Therefore, according to the Data Envelopment Analysis, the technical efficiency in conventional olive farms is less than in the organic ones. When the farms have been evaluated one by one in the light of the total potential improvement values, inputs and outputs, improvement is needed in all values.

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Shiraz, Rashed Khanjani, Madjid Tavana, and Debora Di Caprio. "Chance-constrained data envelopment analysis modeling with random-rough data." RAIRO - Operations Research 52, no. 1 (2018): 259–84. http://dx.doi.org/10.1051/ro/2016076.

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Data envelopment analysis (DEA) is a useful management tool for measuring the relative efficiency of decision making units (DMUs) which consumes multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally indispensable in classical DEA models, real-world problems often involve random and/or rough input and output data. We present a chance-constrained DEA model with random and rough (random-rough) input and output data and propose a deterministic equivalent model with quadratic constraints to solve the model. The main contributions of this paper are fourfold: (3.1) we propose a DEA model for problems characterized by random-rough variables; (3.2) we transform the proposed chance-constrained model with random-rough variables into a deterministic equivalent non-linear form that could be simplified as a deterministic model with quadratic constraints; (3.3) we perform sensitivity analysis to investigate the stability and robustness of the proposed model; and (3.4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.

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35

Kang, Yan-Fang, Lu-Xian Fang, Yue-Hui Zhao, and Shun-Yan Ren. "Passivity Analysis of Complex Delayed Dynamical Networks with Output Coupling." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/792069.

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A new complex dynamical network model with output coupling is proposed. This paper is concerned with input passivity and output passivity of the proposed network model. By constructing new Lyapunov functionals, some sufficient conditions ensuring the input passivity and output passivity are obtained. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed results.

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Imai, Jun-ichi, Hiroyuki Shioya, and Masahito Kurihara. "Modeling and Analysis of Genetic Algorithms Based on the Viewpoint of Mixture Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 3 (2003): 268–75. http://dx.doi.org/10.20965/jaciii.2003.p0268.

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Some mathematical models have been proposed for theoretical analyses of genetic algorithms (GAs). However, these works have limited their objects to a few kinds of GAs in order to formulate them accurately. In this paper, we regard a GA as an information source that generates input-output data. That is, we regard a population and its next population generated by the GA as input and output respectively. Then we model the GA by learning from these data. Since this method uses only the input-output relations of data and ignores interior structures, we can describe a variety of GAs in a common form, and analyze them from a new point of view. We use some mixture models for a representation of these input-output relations in this paper. By using a mixture model for modeling a GA, we can represent the GA system as a combination of some partial systems. In this paper, we treat two types of mixture models, and investigate how these models are effective for analyzing GAs through some numerical experiments.

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Wang, Guofeng, Jiancheng Chen, and Xiangzheng Deng. "Modelling Analysis of Forestry Input-Output Elasticity in China." International Journal of Forestry Research 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/4970801.

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Based on an extended economic model and space econometrics, this essay analyzed the spatial distributions and interdependent relationships of the production of forestry in China; also the input-output elasticity of forestry production were calculated. Results figure out there exists significant spatial correlation in forestry production in China. Spatial distribution is mainly manifested as spatial agglomeration. The output elasticity of labor force is equal to 0.6649, and that of capital is equal to 0.8412. The contribution of land is significantly negative. Labor and capital are the main determinants for the province-level forestry production in China. Thus, research on the province-level forestry production should not ignore the spatial effect. The policy-making process should take into consideration the effects between provinces on the production of forestry. This study provides some scientific technical support for forestry production.

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38

Chuenchum, Pavisorn, Nattapong Pattanapong, Pongsak Suttinon, and Piyatida Ruangrassamee. "Cross-Sectoral Analysis of Water Usage in Thailand Using Input–Output Model." Engineering Journal 22, no. 6 (2018): 93–115. http://dx.doi.org/10.4186/ej.2018.22.6.93.

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Wang, Zhe, Yu Li, Ze Hong Li, Liang Yuan, and Ji Zheng. "An Analysis of Carbon Footprint of Beijing Based on Input-Output Model." Advanced Materials Research 807-809 (September 2013): 1052–58. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.1052.

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Climate change caused by increasing carbon emission is harmful to global environment and human society. Developing low-carbon economy through reasonable industries planning and effective utilization of resources is a significant path to achieve the aim of energy saving and carbon emission reduction. The word carbon footprint means carbon emission caused by a certain industry, activity, product or individual, and the issue of carbon emission should be linked with economic activity to analyze, while input-output model is a reliable method to contact two factors. Based on input-output model, this paper calculated direct or indirect carbon emission which is demanded for the products of final consumption in Beijing, and analyzed carbon footprint of each industrial sector in 2005, 2007 and 2010 by operating Leontief matrix. The result demonstrates annual carbon emission of Beijing increased from 10482.68×104ton to 17407.28×104ton during 2000-2011. Manufacturing industry, excavating industry, transportation and postal industry exert supreme impact on carbon emission in Beijing. Carbon footprint of transportation and postal industry and other tertiary industries such as information, business, service, education, science researching industries in 2010 had an obvious rise compare with the data of 2005 and 2007.

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40

Deng, Wei. "The Analysis of Energy and Environmental Efficiency Based on Input-Output Model." Applied Mechanics and Materials 34-35 (October 2010): 1253–57. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1253.

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Input-output method can conveniently analyze energy consumption and environmental load for iron and steel enterprises. It can visually express production manufacturing statement and the consumption of energy and non-energy. Mathematical models are helpful for quantitative analysis and data handling. In addition, it’s useful to analyze main affected factors. Unit process energy consumption and steel ratio directly affect energy efficiency, which can be analyzed by e-p method.

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41

Liu, Xinjian. "A New Model for AS-AD Analysis Based on Input-Output Frame." Modern Economy 02, no. 03 (2011): 203–12. http://dx.doi.org/10.4236/me.2011.23026.

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Lee, Hyunae, Chulmo Koo, and Namho Chung. "The economic impacts of smart tourism: analysis using an input-output model." Journal of Hospitality and Tourism Studies 22, no. 2 (2020): 1–12. http://dx.doi.org/10.31667/jhts.2020.6.83.1.

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Kuznetsova, Elizaveta, Rim Louhichi, Enrico Zio, and Romain Farel. "Input-output Inoperability Model for the risk analysis of eco-industrial parks." Journal of Cleaner Production 164 (October 2017): 779–92. http://dx.doi.org/10.1016/j.jclepro.2017.06.250.

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Wu, X. J., J. Sun, J. Liu, Y. K. Ding, G. H. Huang, and Y. P. Li. "Ecological Network-based Input-output Model for Virtual Water Analysis in China." IOP Conference Series: Earth and Environmental Science 435 (February 7, 2020): 012010. http://dx.doi.org/10.1088/1755-1315/435/1/012010.

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Jahanshahloo, G. R., F. Hosseinzadeh Lotfi, M. Rostamy-Malkhalifeh, and S. Ghobadi. "Using Enhanced Russell Model to Solve Inverse Data Envelopment Analysis Problems." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/571896.

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This paper studies the inverse data envelopment analysis using the nonradial enhanced Russell model. Necessary and sufficient conditions for inputs/outputs determination are introduced based on Pareto solutions of multiple-objective linear programming. In addition, an approach is investigated to identify extra input/lack output in each of input/output components (maximum/minimum reduction/increase amounts in each a of input/output components). In addition, the following question is addressed: if among a group of DMUs, it is required to increase inputs and outputs to a particular unit and assume that the DMU maintains its current efficiency level with respect to other DMUs, how much should the inputs and outputs of the DMU increase? This question is discussed as inverse data envelopment analysis problems, and a technique is suggested to answer this question. Necessary and sufficient conditions are established by employing Pareto solutions of multiple-objective linear programming as well.

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Rukmana, Asep Nana, and Bambang Siswoyo. "Penerapan Linier Discriminant Analysis pada Klasifiksi Distress Binsin Perbankan." ETHOS (Jurnal Penelitian dan Pengabdian) 7, no. 2 (2019): 224–32. http://dx.doi.org/10.29313/ethos.v7i2.4554.

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Abstract. Linear discrimant machine learning analysis is part of artificial intelligence that can learn from past data, recognize patterns to get optimal solutions. The prediction of the bankruptcy of a Sharia public bank company in Indonesia is very important. Modeling Machine Learning with five input-output models can be implemented between financial ratio variables against bankruptcy. Overall, a linear discrimant analysis algorithm is able to train data to build patterns of input-output relationships and modeling behavior well. Every company certainly wants an appropriate and efficient decision making. Linear discrimant analysis builds predictive models using financial ratio variables as predictors. In this study the model can recognize well the pattern of financial ratios with the results of model validation in the form of means square error 8% and coefficient terminated 98%.Abstrak. Linier discrimant analisys machine learning merupakan bagian dari kecerdasan buatan yang dapat belajar dari data masa lalu, mengenali pola untuk mendapatkan solusi yang optimal. Prediksi kebangkrutan suatu perusahaan bank umum Syariah di Indonesia sangat penting. Modeling Machine Learning dengan lima model input-output dapat diimplementasikan antara variabel rasio keuangan terhadap kebangkrutan. Secara keseluruhan, algoritma linier discrimant analysis mampu melatih data untuk membangun pengenalan pola hubungan input-output dan perilaku pemodelan dengan baik. Setiap perusahaan tentu menginginkan sebuah pengambilan keputusan yang tepat dan efisien. Linier discrimant analisis membangun model prediksi menggunakan variabel rasio keuangan sebagai prediktor. Pada penelitian ini model dapat mengenal dengan baik pengenalan pola rasio keuangan dengan hasil validasi model berupa means square error 8% dan koefesien diterminasi 98%.

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Li, Hong Bo, and Ke Bin Zhang. "The Input-Output Analysis of Construction Industry in West China." Applied Mechanics and Materials 584-586 (July 2014): 2353–58. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.2353.

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In order to objectively evaluate the input-output efficiency of the construction industry in the western region, this passage analyzes the input and output status of each construction areas in the west in 2012 by establishing DEA model , and in compare with the national average. Results showed that: in 2012, the input-output efficiency of construction, the highest values in the western region is Guangxi Province, the lowest in Tibet, and the overall efficiency of the majority of the western region higher than the national average, Finally, the passage pointed out the insufficiencies about input-output of construction industry development in some western areas, and propose recommendations for improvement.

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ALEM, Habtamu. "Effects of model specification, short-run, and long-run inefficiency: an empirical analysis of stochastic frontier models." Agricultural Economics (Zemědělská ekonomika) 64, No. 11 (2018): 508–16. http://dx.doi.org/10.17221/341/2017-agricecon.

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This paper examines the recent advances in stochastic frontier (SF) models and its implications for the performance of Norwegian crop-producing farms. In contrast to the previous studies, we used a cost function in multiple input-output frameworks to estimate both long-run (persistent) and short-run (transient) inefficiency. The empirical analysis is based on unbalanced farm-level panel data for 1991–2013 with 3 885 observations from 455 Norwegian farms specialising in crop production. We estimated seven SF panel data models grouped into four categories regarding the assumptions used to the nature of inefficiency. The estimated cost efficiency scores varied from 53–95%, showing that the results are sensitive to how the inefficiency is modeled and interpreted.

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Geng, Kunling, Dae C. Shin, Dong Song, et al. "Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus." Neural Computation 30, no. 1 (2018): 149–83. http://dx.doi.org/10.1162/neco_a_01031.

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This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.

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Koks, Elco E., Lorenzo Carrera, Olaf Jonkeren, et al. "Regional disaster impact analysis: comparing input–output and computable general equilibrium models." Natural Hazards and Earth System Sciences 16, no. 8 (2016): 1911–24. http://dx.doi.org/10.5194/nhess-16-1911-2016.

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Abstract. A variety of models have been applied to assess the economic losses of disasters, of which the most common ones are input–output (IO) and computable general equilibrium (CGE) models. In addition, an increasing number of scholars have developed hybrid approaches: one that combines both or either of them in combination with noneconomic methods. While both IO and CGE models are widely used, they are mainly compared on theoretical grounds. Few studies have compared disaster impacts of different model types in a systematic way and for the same geographical area, using similar input data. Such a comparison is valuable from both a scientific and policy perspective as the magnitude and the spatial distribution of the estimated losses are born likely to vary with the chosen modelling approach (IO, CGE, or hybrid). Hence, regional disaster impact loss estimates resulting from a range of models facilitate better decisions and policy making. Therefore, this study analyses the economic consequences for a specific case study, using three regional disaster impact models: two hybrid IO models and a CGE model. The case study concerns two flood scenarios in the Po River basin in Italy. Modelling results indicate that the difference in estimated total (national) economic losses and the regional distribution of those losses may vary by up to a factor of 7 between the three models, depending on the type of recovery path. Total economic impact, comprising all Italian regions, is negative in all models though.

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