Analysis Of Technical Efficiency Research Articles

Comparative Analysis of technical efficiency of wheat production in row planting and broadcasting methods: Empirical evidence from southern Ethiopia

A Cobb–Douglas stochastic production frontier was used to analyze the technical efficiency of wheat producers under row planting and broadcasting methods in Hadiya zone, southern Ethiopia. The estimated results of the Cobb–Douglas frontier model show that the mean technical efficiency was 83.4% and 57.8% under row planting and broadcasting, respectively. Approximately 646.882 kg/ha under row planting and 1393.038 kg/ha under broadcasting of wheat output were lost due to inefficiency. This reveals that under the existing practices there is a room to increase wheat yield more under broadcasting (42.2%) than row planting (16.6%) following the best-practice farms in the study area. The SPF model indicates that NPS, urea, labour and seed are significant determinants of wheat production level in both methods. The estimated SPF model together with the inefficiency parameters show that education, age, fertility status of the plot, family size, land fragmentation and extension contact were the main factors that had a significant influence on technical inefficiency of wheat farmers. Hence, emphasis should be given to improving the efficiency level of those less efficient farmers by adopting the practices of relatively efficient farmers in the area.

Analysis of Efficiency of Use of Factors Production Rice Farming Polluted and Unpolluted By Slaughterhouses Waste In Penggaron Kidul Semarang

Penggaron Kidul is a village in Pedurungan that owns rice fields located around Slaughterhouse (RPH). It causes the rice fields polluted by waste and makes the rice productivity is not optimal. This research aimed to analyze the production factors that affected the amount of rice production, the use of production factors level, and the economic efficiency differences in the use of production factors in polluted and unpolluted fields. The survey method was employed in this research by interviewing the members of Dharma Tani Farmers using a questionnaire. The sampling method used was proportionate stratified random sampling used two populations with 80 people. The number of samples was 66 respondents categorized into 33 farmers (landowners of polluted fields) and 33 farmers (landowners of unpolluted fields). The data analysis conducted using a linear regression analysis method, analysis of Independent Sample T-test, and analysis of technical and economic efficiency then tested using analysis One Sample T-test. The result showed that the use of the land area, labor, and dummy variable of location significantly affected the amount of rice production while the factors of seed, fertilizer, and pesticides did not significantly affect the amount of rice production. The production factors of fertilizer and labor in polluted land had no different from unpolluted fields while the factors of seed and pesticides in polluted fields were different from unpolluted fields. Meanwhile, the production factors of seeds and pesticides in polluted fields were greater than in unpolluted fields. The use of production factors of land area, seeds, fertilizers, pesticides, and labor was technically and economically inefficient. The efficiency economic of production factors in polluted fields is different from unpolluted fields which unpolluted fields are more efficient than polluted fields.

Analysis of Technical Efficiency of Paddy and Maize Farming

This study aims to determine the level of technical efficiency of paddy and maize farming in Gorontalo Province in 2019. The data used comes from the 2019 Ubinan Survey conducted by BPS Gorontalo Province. The total samples studied were 833 farming units, consisting of 392 paddy farming units and 441 maize farming units. The analysis method used is Data Envelopment Analysis (DEA). The results showed that most of the paddy and maize farming in Gorontalo Province in 2019 had not yet reached the full level of technical efficiency. The average level of technical efficiency in paddy farming was 68.7 percent, while the average level of efficiency in maize farming was only 56.7 percent.

Evaluation of the Technical and Economic Efficiency of Oil-Contaminated Wastes Recycling System Based on DEA-Method

The article presents a method for analysis of technical and economic efficiency of oil-contaminated wastes (OCW) recycling system. The approach is based on a comparative analysis of the values of costs and profits for the processing of waste in a particular storage facility using a specific processing technology for various combinations of storages and technologies. The set of compared combinations was compiled on the basis of data on the actual processing technology applied for the OCW recycling in the particular storage and also for the technology selected using a special evaluation algorithm. The algorithm for optimization of the OCW recycling system includes the determination of the reuse potential assessments for OCW storage facilities, as well as evaluation of environmental safety and energy efficiency assessments for processing technologies. The calculation of efficiency estimates is carried out using the DEA method, which is widely used as a method of multi-factorial assessment of the efficiency of production systems, using specially developed software.

Анализ технико-экономической эффективности организации работы блок-трейнов

Анализ технико-экономической эффективности организации работы блок-трейнов

Novel semi-automated graphene nanosheets based pipette-tip assisted micro-solid phase extraction as eco-friendly technique for the rapid detection of emerging environmental pollutant in waters

In this work, a new semi-automated syringe infusion-pump assisted graphene nanosheets (GNSs) based pipette-tip micro-solid phase extraction (PT-μSPE) as a green sample preparation technique was demonstrated for the sensitive analysis of emerging environmental pollutant in environmental waters using HPLC-UV. Microwave-assisted synthesized GNSs powder was packed into a 100 μL pipette-tip (as PT-μSPE cartridge) connected with a commercial plastic syringe (contains water sample). This setup was attached to a programmable auto-syringe infusion pump for the GNSs-PT-μSPE process. Triclosan (TCS) is an emerging environmental pollutant chosen as a target analyte to examine the extraction capacity and feasibility of GNSs as a sorbent material for PT-μSPE. Parameters affecting the extraction capability were systematically evaluated and thoroughly optimized. At optimized experimental parameters, excellent linearity (r2 = 0.9979) was achieved over the concentration range of 2–250 ng mL−1 for TCS, with a detection limit of 0.5 ng mL−1. Applicability of the presented method was examined with real water samples, and extraction recoveries obtained were ranged between 94.6–102.4% with RSD less than 7.8%. The presented protocol is a simple, semi-automated, eco-friendly, low-cost, and efficient sample pretreatment technique for quick analysis of TCS in environmental wastewaters.

A novel autonomous learning framework to enhance sEMG-based hand gesture recognition using depth information

Hand gesture recognition using surface electromyography (sEMG) has been one of the most efficient motion analysis techniques in human–computer interaction in the last few decades. In particular, multichannel sEMG techniques have achieved stable performance in hand gesture recognition. However, the general solution of collecting and labeling large data manually leads to time-consuming implementation. A novel learning method is therefore needed to facilitate efficient data collection and preprocessing. In this paper, a novel autonomous learning framework is proposed to integrate the benefits of both depth vision and EMG signals, which automatically label the class of collected EMG data using depth information. It then utilizes a multiple layer neural network (MNN) classifier to achieve real-time recognition of the hand gestures using only the sEMG. The overall framework is demonstrated in an augmented reality application by the recognition of 10 hand gestures using the Myo armband and an HTC VIVE PRO. The results show prominent performance by introducing depth information for real-time data labeling.

Technical Efficiency in the European Dairy Industry: Can We Observe Systematic Failures in the Efficiency of Input Use?

The paper provides findings on the technical efficiency of the European dairy processing industry, which is one of the most important subsectors of the food processing industry in the European Union (EU). The ability to efficiently use inputs in the production of outputs is a prerequisite for the sustainability and competitiveness of the agri-food sector as well as for food security. Thus, the aim of this paper is to provide a robust estimate of technical efficiency by employing new advances in productivity and efficiency analysis, and to investigate the efficiency of input use in 10 selected European countries. The analysis is based on two-stage stochastic frontier modelling incorporating country-specific input distance function (IDF) estimates and a meta-frontier input distance function estimate, both in specification of the four-component model, which currently represents the most advanced approach to technical efficiency analysis. To provide a robust estimate of these models, the paper employs methods that control for the potential endogeneity of netputs in the multi-step estimation procedure. The results, based on the Amadeus dataset, reveal that companies manufacturing dairy products greatly exploited their production possibilities in 2006–2018. The dairy processing industry in the analysed countries cannot generally be characterized by a considerable waste of resources. The potential cost reduction is estimated at 4–8%, evaluated on the country samples mean. The overall technical inefficiency (OTE) is mainly a result of short-term shocks and unsystematic failures. However, the meta-frontier estimates also reveal a certain degree of systematic failure, e.g., permanent managerial failures and structural problems in European dairy processing industry.

An Early Disease Prediction and Risk Analysis of Diabetic Mellitus using Electronic Medical Records

In the world today, the fourth leading disease is Diabetes that could lead to other serious complicating diseases. Diabetes is one of the most common chronic disease which can also be the cause of death in many cases. An efficient system for early disease prediction and risk analysis of diabetic mellitus is very much needed as it has the major adverse effects. The large amount of medical data is collected by healthcare industry in the form of Electronic Medical Records. The Electronic Medical Records is communal database for clinical disease and risk prediction that are useful in accurately predicting multiple medical events using machine learning approach. Therefore, this research presents an efficient technique for early prediction and risk analysis of diabetic mellitus disease to improve accuracy and precision using Electronic Medical Records.

Technical Efficiency and Productivity Analysis of Bitter Gourd-Capsicum Multiple Cropping System under Tunnels in Punjab, Pakistan

Technical Efficiency and Productivity Analysis of Bitter Gourd-Capsicum Multiple Cropping System under Tunnels in Punjab, Pakistan

Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset

Electroencephalography is a common clinical procedure to record brain signals generated by human activity. EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications, but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience. Various EEG analysis and classification techniques have been proposed to address this problem however, the conventional classification methods require identification and learning of specific EEG characteristics beforehand. Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification. One of the great implementations of deep learning is Convolutional Neural Network (CNN) which has outperformed traditional neural networks in pattern recognition and image classification. Continuous Wavelet Transform (CWT) is an efficient signal analysis technique that presents the magnitude of EEG signals as time-related Frequency components. Existing deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency domain. To improve classification accuracy, we propose an integrated CWT and CNN technique which classifies five types of EEG signals using. We compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g., GoogleNet, VGG16, AlexNet. Furthermore, the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross validation. The average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are, 76.12% and 56.02% respectively. This model produces results on a publicly available dataset: Epilepsy dataset by UCI (Machine Learning Repository).

Improved Feature Selection Method for the Identification of Soil Images Using Oscillating Spider Monkey Optimization

Precision agriculture is the process that uses information and communication technology for farming and cultivation to improve overall productivity, efficient utilization of resources. Soil prediction is one of the primary phases in precision agriculture, resulting in good quality crops. In general, farmers perform the soil prediction manually. However, the efficiency of soil prediction may be enhanced by the use of current digital technologies. One effective way to automate soil prediction is image processing techniques in which soil images may be analyzed to determine the soil. This paper presents an efficient image analysis technique to predict the soil. For the same, a robust feature selection technique has been incorporated in the image analysis of soil images. The developed feature selection technique uses a new oscillating spider monkey optimization algorithm (OSMO) for the selection of features that are relevant and non-redundant. The new oscillating spider monkey optimization algorithm uses an oscillating perturbation rate to increase its precision and convergence behavior. A set of standard benchmark functions was deployed to visualize the performance of the new optimization technique (OSMO), and results were compared based on mean and standard deviation. Furthermore, the soil prediction approach is validated on a soil dataset, having seven categories. The proposed feature selection method selects the 41% relevant features, which provide the highest accuracy of 82.25% with 2.85% increase.

Cycle-StarNet: Bridging the Gap between Theory and Data by Leveraging Large Data Sets

Abstract Advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods for analyzing spectra are either (a) data driven, which requires prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. In this study, we present a hybrid generative domain-adaptation method that turns simulated stellar spectra into realistic spectra by applying unsupervised learning to large spectroscopic surveys. We apply our technique to the APOGEE H-band spectra at R = 22,500 and the Kurucz synthetic models. As a proof of concept, two case studies are presented. The first is the calibration of synthetic data to become consistent with observations. To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations. Fitting the observed spectra shows an improved average reduced from 1.97 to 1.22, along with a mean residual reduced from 0.16 to −0.01 in normalized flux. The second case study is the identification of the elemental source of missing spectral lines in the synthetic modeling. A mock data set is used to show that absorption lines can be recovered when they are absent in one of the domains. This method can be applied to other fields that use large data sets and are currently limited by modeling accuracy. The code used in this study is made publicly available on GitHub (https://github.com/teaghan/Cycle_SN).

Renewable Energy Supported Bi-directional Electric-Vehicle Charging Station Allocation in Distribution Network using INBPSO Technique

With the sustained growth in the electric-vehicle (EV) industry, the installation of a large number of electric-vehicle charging stations (EVCSs) is the utmost important in recent energy policies of all countries. On the other side, due to the augmented awareness on renewable energy especially the photovoltaic (PV) and wind energies, the installation of renewable energy based EVCSs and their integration into the radial distribution networks have become a promising choice with the feasible bi-directional power flow facility catered by the EVCSs. Bi-directional EVCSs can enable power flow from renewable source/EV to grid and grid to EV to balance power demand for EV as well as distribution network. The optimum allotment of renewable energy powered EVCSs in existing power distribution system is a matter of great significance to maintain the power balance as well as the voltage profile and minimize the total active power loss of the network. This paper presents a strategy for optimum allocation of renewable energy powered EVCSs in a large distribution network with the focus on power balance, voltage profile improvement and active power loss minimization. An improved new binary particle swarm optimization (INBPSO) technique is applied in collaboration with a fast and efficient power flow analysis technique for performing dynamic power flow computation. Validation is conducted on IEEE 15-bus system considering different cases of power flow directions and power ratings. Additionally, voltage as well as loss sensitivity analysis are performed to ascertain the acceptability of the optimum locations obtained by the computation approach. https://dorl.net/dor/20.1001.1.13090127.2021.11.2.24.3

Analysis of technical efficiency of rice production in Anambra State, Nigeria

Technical efficiency of rice farmers in Anambra State, Nigeria using stochastic frontier production function was studied. Multistage random sampling techniques were used to select 120 rice farmers. Structured questionnaire complimented with oral interview were used to collect data from the respondents for the study. The result showed that the estimated level of technical efficiency was ranged between 24 % - 94 %. Also, the determinants factors to technical efficiency of the rice farmers were age of the farmers, level of education and extension services. The major constraints to rice production in the study area were pest and disease (92 %), poor access to credit (83 %), high cost of labour (67 %). Policy options aimed at enhancing farmers’ access to improved rice varieties, paddy, pesticides, and credit facilities and educational programs were proffered.

Public and Private Management of Sub-Saharan African Railways Companies: A Comparative Analysis of Technical Efficiency

Public and Private Management of Sub-Saharan African Railways Companies: A Comparative Analysis of Technical Efficiency

Technical Efficiency Analysis of Pineapple Production at Madhupur Upazila of Tangail District, Bangladesh

The main objective of this study is to analyse the technical efficiency and its determinants of pineapple production at Madhupur upazila of Tangail district in Bangladesh. Cross-section data from a sample of 100 pineapple producers during the 2016-17 cropping season were collected by applying multistage random sampling technique. Farm specific technical efficiency scores were estimated using the Cobb-Douglas stochastic frontier production function approach. Empirical findings show that the estimated technical efficiencies of the sampled farmers’ range from 61.61% to 99.95% with the mean technical efficiency of 91.14%. The result suggests that, on an average, farmers in the study area can potentially increase their productivity by 8.86% through more efficient use of inputs. The estimated stochastic production frontier model indicates that input variables such as area, tillage cost, seedling cost and human labour cost were statistically significant variables to increase the quantity of pineapple production. Technical inefficiency effect model identifies that age of farmers had significant positive, but years of schooling and extension contact had significant negative effect on farmers’ inefficiency. The study therefore recommends that government should focus on the ways of attracting and encouraging the youths as they are more efficient and are likely to be able to properly allocate resources and are willing to adopt new technical innovations. Besides, policies and strategies should be directed towards increasing farmers’ formal as well as informal education through the implementation of effective training programmes by the well-trained extension workers.

A Technical Efficiency Analysis of Financial Subsidies Allocated by the Cap in Romanian Farms Using Stochastic Frontier Analysis

Abstract The Common Agricultural Policy (CAP) has undergone radical changes as a consequence of international agreements. Through a parametric approach based on Stochastic Frontier Analysis, it has been possible to estimate the impact of financial subsidies allocated under the Common Agricultural Policy for the period from 2007 to 2017 in the framework of the first and second pillars to Romanian farms that are part of the FADN dataset. The findings have revealed the positive effect of financial subsidies allocated to disadvantaged rural areas in increasing technical efficiency, and a modest impact of decoupled payments disbursed under the first pillar of the CAP on the Romanian farms investigated.

Determinants of Persistent and Transient Technical Efficiency of Milk Production in EU

This paper deals with the estimation of technical efficiency of milk production in the EU, its decomposition and the analysis of determinants of transient and persistent efficiency. Attention was focused on specialized milk production using FADN data in the period from 2004 to 2017. The analysis is based on the four-component model that represents the most advanced approach to technical efficiency analysis at present and the multi-step estimation procedure extended by technical efficiency determinants. To the best of our knowledge, this is the first application of this model based on the multi-step estimation with the inclusion of technical efficiency determinants on this type of specialization in the EU. The results show that the overall technical efficiency achieves the mean value of 68% and is relatively dense around the mean. The persistent inefficiency poses a greater problem for dairy production and varies considerably across European regions compared to the transient part. Based on the assessment of the development of transient efficiency, it is evident that it is influenced by the situation on the dairy market, in particular by the milk crises. The most effective conversion of inputs to outputs is achieved in the Netherlands, the United Kingdom, and Belgium and is least burdened with institutional and structural rigidities. The results show that transient efficiency is positively influenced by paid labour share, rented land share, level of modernization and level of off-farm activities and negatively by the level of subsidies on livestock. The positive effect of the economies of size on persistent efficiency is not proved. However, specialization, despite the higher vulnerability of specialized farms to price shocks, affects persistent efficiency positively. Localization of farms in LFAs, as expected, has a negative impact on persistent technical efficiency.

TECHNOLOGY ADOPTION AND TECHNICAL EFFICIENCY OF OIL PALM SMALLHOLDER PLANTATION IN RIAU AND WEST KALIMANTAN

This study was conducted to assess the role of adoption of oil palm cultivation technology that has been recommended by the government (RI Regulation of the Minister of Agriculture No 131 / Permentan / OT.140 / 12/2013 concerning Guidelines for Good Oil Palm Cultivation) on production efficiency from smallholder plantations on the dependent smallholder and independent smallholder in Riau and West Kalimantan. Specifically, this study is: (1) to analyze the factors that influence the level of production and analyze the technical efficiency of smallholder oil palm in Riau and West Kalimantan, (2) to analyze the effect of intensity of technology adoption on productivity and efficiency of smallholder oil palm production in Riau and West Kalimantan, (3) To identify the factors affecting technical inefficiency in smallholder plantations in Riau and West Kalimantan. The research objectives were analyzed by using a stochastic frontier model to estimate productivity, the level of technical efficiency, and producer inefficiency simultaneously with allocative efficiency and economic efficiency. The results showed that smallholder oil palm farmers who adopt all recommended technologies compared to farmers who adopt not all recommended technologies had higher production efficiency. Analysis of technical efficiency showed that farmers in the dependent smallholder were more technically efficient than independent smallholders. Farmers who adopt more technology and had more experience, had higher efficiency than others. Farmers who were often associated with extension workers and more experience had higher efficiency than others. Keywords: oil palm smallholder, technology adoption, technical efficiency, Riau, West Kalimantan