Input Output Variables Research Articles

Revisiting the Efficiency of Indian Banking Sector: An Analysis of Comparative Models Through Data Envelopment Analysis

This study examines the efficiency of the overall Indian banking industry using Data Envelopment Analysis (DEA) and to perform a comparative efficiency analysis of public, private, and foreign banks using six varied forms. Also, providing ranks to the banks based on their efficiency. The study incorporates BCC output-oriented DEA model using a sample of 50 Indian banks (public banks = 17, private banks = 18, foreign banks = 15) for a period ranging from 2009-10 to 2018-19, hence incorporating the after-effects of the financial crisis and demonetization, this study uses panel data from 2009-10 to 2018-19. The results showed that most of the Indian banks fall on the efficient side or are near to full efficiency. However, public banks outperform private and foreign banks in terms of their average efficiency. Results also specify that the performance of banks is sensitive to input-output variables, units under evaluation, and choice of the model. The current study has just focused on the internal factors for analyzing the efficiency of Indian banks; however, certain external factors might also impact the banks’ efficiency.

The Impacts of Water Pollution Emissions on Public Health in 30 Provinces of China.

China’s economy in recent decades has developed at a very rapid speed, as evidenced by its GDP jumping to second place in the world. Although utilization of domestic water resources has helped spur economic development, sewage discharge as an undesirable output has unfortunately caused many negative effects on human health, causing concern from all walks of life. Therefore, governments in China at all levels are committed to urban sewage treatment policies in order to reduce the negative impact of water pollution on society. While most existing studies have targeted the macro-level modes of economic development and environmental pollution, their selection of research objects is too narrow by failing to adequately consider China’s water pollution and the consequential national health crisis. This study takes cities in 30 provinces of China as the research objects and applies various influencing factors of urban wastewater treatment and health (as two stages) to the modified two-stage dynamic Slacks-Based Measures (SBM) Data Envelopment Analysis (DEA) model. The results reveal that the overall efficiency of each province is increasing and that the efficiency of the wastewater treatment stage is greater, thus contributing to overall efficiency. Conversely, the health stage’s efficiency is far lower than the wastewater treatment stage’s efficiency, which has a notably adverse effect on overall efficiency. In addition, most input-output variables need much improvement. Based on the findings herein, we offer specific suggestions to each province for improving sewage treatment capacity, the level of medical care, and the quality of national health.

Artificial Intelligence-Based Emission Reduction Strategy for Limestone Forced Oxidation Flue Gas Desulfurization System

Abstract The emissions from coal power plants have serious implication on the environment protection, and there is an increasing effort around the globe to control these emissions by the flue gas cleaning technologies. This research was carried out on the limestone forced oxidation (LSFO) flue gas desulfurization (FGD) system installed at the 2*660 MW supercritical coal-fired power plant. Nine input variables of the FGD system: pH, inlet sulfur dioxide (SO2), inlet temperature, inlet nitrogen oxide (NOx), inlet O2, oxidation air, absorber slurry density, inlet humidity, and inlet dust were used for the development of effective neural network process models for a comprehensive emission analysis constituting outlet SO2, outlet Hg, outlet NOx, and outlet dust emissions from the LSFO FGD system. Monte Carlo experiments were conducted on the artificial neural network process models to investigate the relationships between the input control variables and output variables. Accordingly, optimum operating ranges of all input control variables were recommended. Operating the LSFO FGD system under optimum conditions, nearly 35% and 24% reduction in SO2 emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. Similarly, nearly 42% and 28% reduction in Hg emissions are possible at inlet SO2 values of 1500 mg/m3 and 1800 mg/m3, respectively, as compared to general operating conditions. The findings are useful for minimizing the emissions from coal power plants and the development of optimum operating strategies for the LSFO FGD system.

Analysis of the Efficiency Level of Sharia Rural Financing Bank (SRFB ) Using Data Envelopment Analysis (DEA)

This research aims to analyze the efficiency level of Sharia Rural Financing Bank (SRFB) in Surakarta. This study used data from four Rural Sharia Banks taken from www.ojk.co.id, which published its annual report. This study conducted input-output variables with the Data Envelopment Analysis (DEA) method as a tool of analysis. The results of the research showed that the SRFB Dana Amanah is indicated as efficient during the 2017 period. The other three are indicated as inefficient in the last quarter. The inefficiency source came from its operational cost and its incapability to get higher output.

Research on three-stage amplified compliant mechanism-based piezo-driven microgripper

The microgripper based on the principle of lever amplification is easy to realize; however, the theoretical amplification factor is limited by the space size and the structure is not compact enough. The microgripper based on the triangular amplification principle has a compact structure and high amplification factor, but it is not conducive to miniaturization design. Considering compactness, parallel clamping, high magnification, and miniaturization design, a three-stage amplifier consisting of a semi-rhombic amplifier and lever amplifiers is designed. To begin with, the theoretical amplification ratio and the relationship between input variables and output variables are calculated by energy method. Furthermore, the finite element analysis software is used to optimize the structural parameters and analyze the performance of the model. Lastly, the experimental verification is carried out. At 150 V of driving voltage, the maximum output displacement was 530mm, and the actual magnification was 24 times. Microparts can be gripped in parallel and stably, which confirms the validity of the design.

Model-Based Computational Shipyard Dynamics and Its Applications

A ship production system can be represented by various production factors and production management indicators. To manage such a system efficiently, a theory is required to analyze and predict its behavior. The theory should be able to fully express the relationships between production factors and production management indicators. For this purpose, computational shipyard dynamics was proposed by Kim et al. in the Journal of Ship Production and Design (2018). This methodology includes input variables, output variables, and key performance indicator (KPI) functionals, implemented as a simulation model. In this study, a rigorous approach to realize a model of a complex ship production system is presented by embodying previous research. A model-based ship production system theory is defined, which consists of production factors, production management indicators, and the relationships between them, called the simulation-based KPI functionals. The production factors are defined using a six-factor model, and the production management indicators are defined using KPIs that are already used in shipyards so that they are quantitatively measurable. To verify the proposed theory, it was applied to fabrication shop, panel block assembly line, and ship block logistics simulation cases, which are examples that are similar to the job shop, flow shop, and project shop cases, respectively. Introduction 1.1. Background Approximately 40% of shipyards worldwide have been closed over the last decade. The reasons for this are the economic recession and intense competition. Inefficiency in the production system is not a big problem in the marine shipping industry and offshore-plant-related industries when these industries are booming with high demands and sufficient funding. However, when crises approach, unrevealed problems emerge, and low productivity leads to catastrophes at shipyards.

Prediction of Rainfall Characteristics Based on Fuzzy Expert System

Rainfall prediction is one of the most extremely important and trickiest job in the modern world because, Rain is the lifeblood of human survival, of life on our planet. Gorgeous beauty of fuzzy logic was described by various author for prediction in various field. In this work, fuzzy logic is applied in proposing a model to predict rainfall percentage with parameters. Fuzzy Expert System inputs(parameter) include the temperature, humidity, wind speed , Dew point etc., with Output as rainfall Percentage .Here we develop a new model as RPFES Model to predict rainfall percentage for particular geographical location of Tamil Nadu. There are four steps for developing RPFES Model : The first step is Fuzzification process by triangular membership function for representing the input variables and output variables. The next step is Fuzzy Inference with Fuzzy Rules the method applied in this research work are Root Sum Square (RSS). The Root Sum Square of drawing inference was employed to infer the data from the fuzzy rules developed and finally we move to Defuzzification process for getting Rainfall percentage by individually. From RPFES Model, Individual Person get the Rainfall Prediction as Percentage with purely by Fuzzy Logic and not by Metrological Center. Weather Prediction is the major essential and challenging operational responsibilities which are carried out by meteorological services all over the world.

Decomposition of fuzzy exponential mathematical quantitative process in industrial manufacturing design

Small and medium-sized manufacturing enterprises have the characteristics of large numbers and small scales. Problems such as backward manufacturing technology, lack of talents, small amount of information resources, and insufficient product research and development capabilities have severely restricted the development of enterprises. The backward manufacturing design model cannot adapt to the development trend of modern manufacturing informatization. This paper proposes and designs a fuzzy inference model and fuzzy inference engine algorithm with threshold. In order to describe the numerical multiple input and multiple output variables in the industrial manufacturing design industry, the relevant experience is used to make numerical reasoning decisions. Applying fuzzy sets and fuzzy theory to the expert system, a fuzzy rule model containing the membership function information and thresholds of the corresponding fuzzy sets is proposed and established, and a fuzzy reasoning system suitable for numerical and uncertain reasoning decisions is constructed. The improved grey relational analysis method is used to decompose and evaluate the exponential mathematical quantitative process of manufacturing enterprises. Based on the fuzzy Decision Analytic Network Process (DANP) method to calculate the relative weight of the influencing factors in the evaluation system, the evaluation index of the enterprise is obtained. Starting from the industrial manufacturing design process, this article constructs a relatively comprehensive and reasonable enterprise exponential mathematical quantitative process decomposition evaluation system. Considering that there are complex interactions between the various influencing factors in the system, the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is selected to process the direct impact matrix of the evaluation system, and the causal relationship between the indicators is obtained. The fuzzy exponential gray correlation method is used to evaluate the quantitative process of industrial manufacturing design, avoiding the shortcomings of traditional methods that only consider ideal values.

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderly, and then projected onto the latent space by using the nonlinear functional expansion technology. In the latent space, partial least squares method is applied to compute the regression coefficients between the input variables and output variable of each batch. These regression coefficients, called the latent factors, can describe the functional relationships in the unobserved multimode data. Therefore, the latent factors are used for mode clustering so that the process data with similar functional relations can be clustered in one mode together. For each mode, the nonlinear soft sensor is established based on KPLS. To assign the mode of the online query sample, a mode identification strategy based on Bayesian inference is designed for the soft sensor online prediction. Finally, two cases studies are adopted to validate the proposed method.

CSD Based DR Approach for the Solution of Differential Equation of LTI SISO System of Causal Type

Dimensional reduction (DR) approach is one of the way by which the problem of higher dimensional space can reduces to lower one. The main advantage of this DR approach is that the space-time interval (Δs – Δt); where Δs represents the computation space while Δt represents the computation time; requirement for it being relatively short. Within this paper the consecutive single derivative (CSD) based DR approach is consider for the solution of differential equation of linear time invariant (LTI) single input single output (SISO) system of causal type on the matrix ground. Besides this CSD based DR solution, the behavioural operator of the system is also discussed which represents the identity of a given system relating the input-output variables of desired.

Efficiency Assessment of Public Education & Health Sector in Selected Middle-Income Countries with Special Reference to Millennium Development Goals (MDGs)

This study investigates relative efficiency of public education and health sector in selected middle income countries with special reference to Millennium Development Goals (MDGs). The study uses data for two reference years; 2000 (implementation year of MDGs for developing countries) and 2015 (the final year of MDGs). Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) are used to calculate relative efficiency, operating scale of the countries (DMUs) and productivity change in relative efficiency over time respectively. The paper conceptualizes relative efficiency of the countries in discretionary, multicriteria input-output variables context to investigate efficiency differences among the countries and deduce important takeaways. Educational expenditure, teachers at primary level, health expenditure, birth attended by skilled staff are used as input variables while enrollment at primary level, completion of primary level education, infant survival per annum and child survival per annum are used as output variables. The DEA results show that all countries could not operate at efficient level to target MDGs. The level of efficiency was not same under different DEA specifications in both the periods. Some countries were inefficient because of their size; either having too large size or too small size of operation. The sources of change in efficiency over the time were either because of real change in efficiency or change in technology frontier or both. The study identified a set of institutional and individuals factors which contribute to the efficiency and inefficiency of DMUs under investigation.

Efficiency Assessment of Public Education & Health Sector in Selected Middle-Income Countries with Special Reference to Millennium Development Goals (MDGs)

This study investigates relative efficiency of public education and health sector in selected middle income countries with special reference to Millennium Development Goals (MDGs). The study uses data for two reference years; 2000 (implementation year of MDGs for developing countries) and 2015 (the final year of MDGs). Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) are used to calculate relative efficiency, operating scale of the countries (DMUs) and productivity change in relative efficiency over time respectively. The paper conceptualizes relative efficiency of the countries in discretionary, multicriteria input-output variables context to investigate efficiency differences among the countries and deduce important takeaways. Educational expenditure, teachers at primary level, health expenditure, birth attended by skilled staff are used as input variables while enrollment at primary level, completion of primary level education, infant survival per annum and child survival per annum are used as output variables. The DEA results show that all countries could not operate at efficient level to target MDGs. The level of efficiency was not same under different DEA specifications in both the periods. Some countries were inefficient because of their size; either having too large size or too small size of operation. The sources of change in efficiency over the time were either because of real change in efficiency or change in technology frontier or both. The study identified a set of institutional and individuals factors which contribute to the efficiency and inefficiency of DMUs under investigation.

Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model

Landslide displacement time series can directly reflects landslide deformation and stability characteristics. Hence, forecasting of the non-linear and non-stationary displacement time series is necessary and significant for early warning of landslide failure. Traditionally, conventional machine learning methods are adopted as forecasting models, these forecasting models mainly determine the input and output variables experientially and does not address the non-stationary characteristics of displacement time series. However, it is difficult for these conventional machine learning methods to obtain appropriate input-output variables, to determine appropriate model parameters and to acquire satisfied prediction performance. To deal with these drawbacks, this study proposes the wavelet analysis (WA) to decompose the displacement time series into low- and high-frequency components to address the non-stationary characteristics; then proposes thee chaos theory to obtain appropriate input-output variables of forecasting models, and finally proposes Volterra filter model to construct the forecasting model. The GPS monitoring cumulative displacement time series, recorded on the Shuping and Baijiabao landslides, distance measuring equipment monitoring displacements on the Xintan landslide in Three Gorges Reservoir area of China, are used as test data of the proposed chaotic WA-Volterra model. The chaotic WA-support vector machine (SVM) model and single chaotic Volterra model without WA method, are used as comparisons. The results show that there are chaos characteristics in the GPS monitoring displacement time series, the non-stationary characteristics of landslide displacements are captured well by the WA method, and the model input-output variables are selected suitably using chaos theory. Furthermore, the chaotic WA-Volterra model has higher prediction accuracy than the chaotic WA-SVM and single chaotic Volterra models.

Analysis of Dynamic Model of Cigarette Consumption Reduces First Goal of SDGs in Indonesia

This research aims to see the extent to which the real conditions of society can be separated from the trap of poverty due to consuming cigarettes. This research uses secondary data which taken from various sources of publication. The variables used are input variables and output variables. Input variables are in the form of community income. Output variables used are average per capita expenditure. Data analysis using Dynamic System Analysis with software of ITHINK ® Ver. 6.01 from the High Performance System (2000). The result of this research indicate that in the condition of the community consuming 1 cigarette per day shows that groups of community are difficult to escape from the trap of poverty until the end of Medium Term and Long Term Development Plan is poor society, almost poor society and middle to lower society. While middle to middle society, middle to upper society and rich society still have an income balance from the beginning of the research year to the next 20 years. While the result of the scenario of consumption of 2 packs of cigarettes per day show besides poor society, almost poor society and middle to lower society are difficult to escape from the trap of poverty, middle to middle society are in poverty for the next 20 years as well. Therefore the goal of the first SDGs in Indonesia to eliminate poverty is difficult to achieve.

Point and interval forecasting for carbon price based on an improved analysis-forecast system

Effective analysis and forecasting of carbon prices, which is an essential endeavor for the carbon trading market, is still considered a difficult task because of the nonlinearity and nonstationarity inherent in carbon prices. Previous studies have failed at the analysis and interval prediction of carbon prices and are limited to point forecasts. Therefore, an improved carbon price analysis and forecasting system that consists of an analysis module and a forecasting module is established in this study; more importantly, the forecasting module includes point forecasting and interval forecasting as well. Aimed at investigating the characteristics of the carbon price series, a chaotic analysis based on the maximum Lyapunov exponent is performed, the determination of appropriate distribution functions based on our newly proposed hybrid optimization algorithm is conducted, and different distribution functions are effectively designed in the analysis module. Furthermore, in the point forecasting model, the phase space reconstruction technique is applied to reconstruct the sequences decomposed by variational mode decomposition due to the chaotic characteristics of the carbon price series, and the reconstructed sequences are considered as the optimal input–output variables of the forecasting model. Then, an adaptive neuro-fuzzy inference system model is trained by the newly proposed hybrid optimization algorithm, which is developed for the first time in the domain of carbon price point forecasting. Moreover, based on the results of point forecasting and the distribution function of the carbon price series determined by the analysis module, the interval forecasting results can be obtained and implemented to provide more reliable information for decision making. Empirical results based on the carbon price data of the European Union Emissions Trading System and Shenzhen of China demonstrate that the proposed system achieves better results than other benchmark models in point forecasting as well as interval forecasting.

Design of a flexible piezoelectric microgripper based on combined amplification principles

Aiming to address the problem of the low amplification ratio of traditional microgrippers, a two-stage microgripper based on the principle of combined amplification was designed with a high amplification ratio and large displacement, using a simple and compact structure. The relationship between theoretical input variables and output variables were first calculated by a projection theorem. Secondly, the performance of the microgripper was analyzed by finite element analysis (FEA). Finally, the accuracy of the theoretical calculation and FEA was verified experimentally. The results show that the microgripper has high magnification and can be gripped in parallel, with self-adaptability for many irregular shaped micro objects. The actual magnification was 23.2×, which is greater than similar products.

Hospital Efficiency in Indonesia with Frontier Analysis

Hospitals are health care institutions for people who provide individual health services. Health services in hospitals are provided for inpatients, outpatients, and emergency unit patient. Facilities in the hospital are also provided for the fulfillment of health services for all patients. However, currently, there are still many doctors who are unbalanced in getting patients. For example, in a hospital, there is a doctor who is a favorite of patients, so they have a large queue of patients. However, on the other hand, some doctors are not favorites so that almost no patients come. This makes the hospital inefficient in terms of labor and time. This study discusses the efficiency of existing resources in hospitals which includes input variables (beds, general practitioners, specialist doctors, nurses, pharmacists, medical technicians, health workers, non-health workers, building area) and output variables (inpatient, outpatient, and patient emergency unit). The method used in this study is Stochastic Frontier Analysis using Frontier 4.1 software. The results indicated that more than 50% of the hospitals are below the efficiency average. This happens for the three types of output.

Transmissivity of Measure and Value in Human Systems

If competent human intervention is about functional appreciation of change as a mo-tion of matter, it must begin with measure and quantify. This old challenge, recast into the 21st century social, acquires a novel technical as well as moral methodologi-cal dimension. For today’s life is characterised by many interacting ‘levels of being’, of disparate, contingent complexity, geo-political and communicational, genetic and viroid etc., often operating away from equilibrium, each with its input-output vari-ables and limits of applicability. Its action space is fragmented and manipulated by the victorious neo-liberal division of labour increasingly traumatised by the range of capabilities of the high tech tools vastly exceeding those of the human body and mind. The competence then depends chiefly on the ability to project one such set of param-eters on another without losing the transmissivity of projected content and its limits. It will be argued that this challenge calls for a fresh research and development agenda of mapping such transmissions across the relevant domain boundaries, and for foster-ing a class of facilitators fit and willing to legitimate this process by methodological procedures rising to the requirements of dynamic ontology of quasi-objects underly-ing the functioning of human systems and of human expression and communication at large. What is at stake is a shift in attitude, chiefly about the way of seeing and connecting things in terms of quantitative, accreditable relations between empirical parameters and their limits. Its ultimate aim is to provide generic guidelines for de-veloping competent citizenship in which the ultimate measure of value is personal and social independence for all; only then a credible and sustainable process of reforms can come into being and gradually return emergent knowledge to the service of humanity.

A data envelopment analysis model for evaluating the performance of taxi operators: a case study in Harbin, China

ABSTRACTThis paper measures the performance of taxi operators using data envelopment analysis (DEA) and the improvement of performance based on benchmarks. This includes basic DEA models and a cross efficiency model. In the present work, a sample set of 20 taxi operators are considered, and an evaluation indicator framework is constructed, which mainly involves the identification of decision-making units (DMUs) and the determination of input-output variables. The combined results of multiple DEA models are used to evaluate the performance of taxi operators in a case study of Harbin. The results highlight the significance of the combined application of multiple DEA models, especially a cross-efficiency model that can obtain more objective efficiency values. Finally, the benchmarking method is used to capture the achievements of best-performing operators and provide valuable improvement recommendations for inefficient operators. Conclusions provide a suitable basis for the operation and management of the taxi market.

Understanding national innovation system (NIS) using porter’s diamond model (PDM) of competitiveness in ASEAN-05

PurposeThe purpose this study is to investigate national innovation systems (NIS) using Porter’s Diamond model (PDM) by examining the five founding member nations of the Association of South East Asian Nations (ASEAN) namely Indonesia, Malaysia, Philippines, Singapore and Thailand, for the period 2010-2014 (WCY 2015, WDI 2015).Design/methodology/approachPDM of competitiveness helps us understanding a nation’s competitive position in world trade. In exploring the empirical relationship between NISs and PDM, a non-parametric approach has been applied using the Malmquist Productivity Index (MPI). This study focused on representing the PDM in a simplified manner and endeavored to understand NIS more rigorously through PDM. The study has used several innovation input-output variables to investigate the efficiency and productivity of the countries concerned. The accuracy of the study has been enhanced by the use of MPI.FindingsPDM is found efficacious in the practice and strengthening of NIS in the context of these countries’ competitiveness. This study found unchanged Total Factor Productivity (TFP) for Philippines, Singapore and Thailand; that is technological progress is yet to be obtained. Indonesia lags both in technical and technological progress. On the other hand, Malaysia is found to be over-using the existing input-output factors of NIS.Originality/valueMany authors have attempted to measure competitiveness and NIS separately in the context of ASEAN or other South-East Asian countries. However, as yet, no empirical investigation has been conducted to assess the competitiveness of a nation by applying NIS-based analysis according to PDM.