Improving energy efficiency considering reduction of CO2 emission of turnip production:A novel data envelopment analysis model with undesirable output approach

Modern Turnip production methods need significant amount of direct and indirect energy. The optimum use of agricultural input resources results in the increase of efficiency and the decrease of the carbon footprint of turnip production. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to evaluate the efficiency for peer units compared with the best practice frontier, widely used by researches to analyze the performance of agricultural sector. In this regard, a new non-radial DEA-based efficiency model is designed to investigate the efficiency of turnip farms. For this purpose, five inputs and two outputs are considered. The outputs consist turnip yield as a desirable output and greenhouse gas emission as an undesirable output. The new model projects each DMU on the strong efficient frontier. Several important properties are stated and proved which show the capabilities of our proposed model. The new models are applied in evaluating 30 turnip farms in Fars, Iran. This case study demonstrates the efficiency of our proposed models. The target inputs and outputs for these farms are also calculated and the benchmark farm for each DMU is determined. Finally, the reduction of CO2 emission for each turnip farm is evaluated. Compared with other factors like human labor, diesel fuel, seed and fertilizers, one of the most important findings is that machinery has the highest contribution to the total target energy saving. Besides, the average target emission of turnip production in the region is 7% less than the current emission

Classification of Pomegranate Fruit using Texture Analysis of MR Images

Images obtained by Magnetic Resonance Imaging (MRI) of Iranian important export cultivar of pomegranate Malase-e-Torsh were analyzed by texture analysis to determine Gray Level Co-occurrence Matrix (GLCM) and Pixel Run-Length Matrix (PRLM) parameters. The T2 slices measured at 1.5 T for 4 quality classes of pomegranate semi-ripe, ripe, over-ripe and internal defects classes were analyzed numerically using the software MaZda. To classify pomegranate into different classes, discriminant analysis was conducted using cross-validation method and texture features. Ten GLCM and 5 PRLM features were used in 2 different classifiers. Mean classification accuracy was 95.75 % and 91.28 % for GLCM and PRLM features respectively. By using GLCM and RPLM features, classification accuracy for semi-ripe, over-ripe and internal defects classes was higher when GLCM features were used. Ripe class had higher classification accuracy while PRLM features were used. To improve classification accuracy, combination of GLCM and PRLM features were used. For achieving best classification accuracy, optimum numbers of features were selected based on their contribution to the model. Combination of 7 GLCM and 4 PRLM features resulted in mean accuracy of 98.33 % and the lowest type I and II errors. Especially, type I error in ripe and over-ripe classes were significantly decreased. The classification accuracies were 100, 98.47, 100 and 95 % for semi-ripe, ripe, over-ripe and internal defects classes

Sensitivity analysis of energy inputs in crop production using artificial neural networks

Sensitivity analysis establishes priorities for research and allows to identify and rank the most important factors which lead to great improvements in output factors. The aim of this study is to examine sensitivity analysis of inputs in grape production. We are proposing to perform sensitivity analysis using partial rank correlation coefficient (PRCC) which is the most reliable and efficient method, and we apply this for the first time in crop production. This research investigates the use of energy in the vineyard of a semi-arid zone of Iran. Energy use efficiency, energy productivity, specific energy and net energy were calculated. Various artificial neural network (ANN) models were developed to predict grape yield with respect to input energies. ANN models consist of a multilayer perceptron (MLP) with seven neurons in the input layer, one and two hidden layer(s) with different number of neurons, and an output layer with one neuron. Input energies were labor, machinery, chemicals, farmyard manure (FYM), diesel, electricity and water for irrigation. Sensitivity analysis was performed on over 100 samples of parameter space generated by Latin hypercube sampling method, which was then fed to the ANN model to predict the yield for each sample. The PRCC between the predicted yield and each parameter value (input) was used to calculate the sensitivity of the model to each input. Results of sensitivity analysis showed that machinery had the greatest impact on grape yield followed by diesel fuel and labor

Image Processing and Biometrical Investigations for Choosing the Most Ranked Rice Cultivars

Rice (Oryza sativa L.) is one of the vital food staples in our daily life, amongst the oldest cultivated crops worldwide, ranks as the most widely grown crop. It serves as a vital nutritional material to more than half of the world increasing crowd. This experiment was conducted for evaluation and selection of the most ranked rice cultivars: Kamfirooz, Yasouj, Dom Siah, Gharib, Dollar, Hassan saraei, 304, Lenjan, and Musa Tarom (MTA) as the well-known and prevalent varieties in the 'Fars' and 'Kogilouyeh-o-BoyreAhmad' provinces, Iran, along with other countries. Rough rice grains of the varieties were randomly selected and their principal dimensions were imaged using camera and a special box for light controlling and including Perimeter, Area, MajorAxis Length, MinorAxis Length, Solidity, Eccentricity, and Equiv Diameter, following with image analyzing by image processing software, in a labratroy experiment. The data were statistically analyzed and graphically plotted using SPSS v.17 and MINITAB v.16 software programs. The results of the image analysis indicated that there were significantly differences between most cultivars in the case of physical traits. The cluster categorization of the cultivars showed also that 9 cultivars clustered in 5 groups, i.e. Yasouj and Lenjan cultivars were located in different groups individually, where cultivars: Kamfirrooz, Dom Siah and Dollar in a unique cluster, but Gharib and 304 in the 4th group and MTA and Hassan Saraei in the 5th group. The results of this experiment showed the separateness of rice most recent cultivars according to agronomic and physical status, and leads to picking the most valuable cultivars for upcoming food nutritional values and also experiment

Identifying and structuring the factors affecting sustainable banking resources with using interpretive structural modeling

Considering the recent developments in banking system, banks are able to increase their power of partnership in different areas of manufacturing, industrial, agricultural and else by attracting more resource approach, and creating competitive advantage, which result in expansion and increase of earnings or benefits of the bank as well as creating competitive advantage. Considering the limitation on resources and time, the ability to jointly invest in all of the influential factors on creating sustainable banking resources cannot be achieved. The main aim of the present research is to provide a model of the influential factors on creating sustainable banking resources so with usage of which, managers can identify the factors that have the highest influence on the others and plan on the them. For this purpose, 10 influencing factors on creating sustainable banking resources such as financial risks, amount and quality of equity, amount of assets, etc. by taking into account the relevant studies as well as benefiting from specialist's opinions are chosen. Then the influential structure of these factors with usage of interpretive structural modeling method is drawn. The obtained results show that management of equities and human resources, are the most influential factors on providing sustainable banking resources

Oxidized multi walled carbon nanotubes for improving the electrocatalytic activity of a benzofuran derivative modified electrode

In the present paper, the use of a novel carbon paste electrode modified by 7,8-dihydroxy-3,3,6-trimethyl-3,4-dihydrodibenzo[b,d]furan-1(2H)-one (DTD) and oxidized multi-walled carbon nanotubes (OCNTs) is described for determination of levodopa (LD), acetaminophen (AC) and tryptophan (Trp) by a simple and rapid method. At first, the electrochemical behavior of DTD is studied, then, the mediated oxidation of LD at the modified electrode is investigated. At the optimum pH of 7.4, the oxidation of LD occurs at a potential about 330 mV less positive than that of an unmodified carbon paste electrode. Based on differential pulse voltammetry (DPV), the oxidation current of LD exhibits a linear range between 1.0 and 2000.0 μM of LD with a detection limit (3σ) of 0.36 μM. DPV was also used for simultaneous determination of LD, AC and Trp at the modified electrode. Finally, the proposed electrochemical sensor was used for determinations of these substances in human serum sample

Efficacy of the Biomaterials 3 wt%-nanostrontium-hydroxyapatite-enhanced Calcium Phosphate Cement (nanoSr-CPC) and nanoSr-CPC-incorporated Simvastatin-loaded Poly(lactic-co-glycolic-acid) Microspheres in Osteogenesis Improvement

Aims The purpose of this multi-phase explorative in vivo animal/surgical and in vitro multi-test experimental study was to (1) create a 3 wt%-nanostrontium hydroxyapatite-enhanced calcium phosphate cement (Sr-HA/CPC) for increasing bone formation and (2) creating a simvastatin-loaded poly(lactic-co-glycolic acid) (SIM-loaded PLGA) microspheres plus CPC composite (SIM-loaded PLGA + nanostrontium-CPC). The third goal was the extensive assessment of multiple in vitro and in vivo characteristics of the above experimental explorative products in vitro and in vivo (animal and surgical studies). Methods and results pertaining to Sr-HA/CPC Physical and chemical properties of the prepared Sr-HA/CPC were evaluated. MTT assay and alkaline phosphatase activities, and radiological and histological examinations of Sr-HA/CPC, CPC and negative control were compared. X-ray diffraction (XRD) indicated that crystallinity of the prepared cement increased by increasing the powder-to-liquid ratio. Incorporation of Sr-HA into CPC increased MTT assay (biocompatibility) and ALP activity (P \u3c 0.05). Histomorphometry showed greater bone formation after 4 weeks, after implantation of Sr-HA/CPC in 10 rats compared to implantations of CPC or empty defects in the same rats (n = 30, ANOVA P \u3c 0.05). Methods and results pertaining to SIM-loaded PLGA microspheres + nanostrontium-CPC composite After SEM assessment, the produced composite of microspheres and enhanced CPC were implanted for 8 weeks in 10 rabbits, along with positive and negative controls, enhanced CPC, and enhanced CPC plus SIM (n = 50). In the control group, only a small amount of bone had been regenerated (localized at the boundary of the defect); whereas, other groups showed new bone formation within and around the materials. A significant difference was found in the osteogenesis induced by the groups sham control (16.96 ± 1.01), bone materials (32.28 ± 4.03), nanostrontium-CPC (24.84 ± 2.6), nanostrontium-CPC-simvastatin (40.12 ± 3.29), and SIM-loaded PLGA + nanostrontium-CPC (44.8 ± 6.45) (ANOVA P \u3c 0.001). All the pairwise comparisons were significant (Tukey P \u3c 0.01), except that of nanostrontium-CPC-simvastatin and SIM-loaded PLGA + nanostrontium-CPC. This confirmed the efficacy of the SIM-loaded PLGA + nanostrontium-CPC composite, and its superiority over all materials except SIM-containing nanostrontium-CPC

Eco-efficiency measurement and material balance principle:an application in power plants Malmquist Luenberger Index

Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide ‘real’ measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper

Energy management in crop production using a novel fuzzy data envelopment analysis model

Data envelopment analysis is a relatively “data oriented” approach to measure the efficiency of a set of decision making units which transform multiple inputs into multiple outputs. However, some production processes may generate undesirable outputs like smoke pollution or waste. On the other hand, in many situations, such as a manufacturing system, a production process or a service system, inputs and outputs can be considered as a fuzzy variable. Thus, this paper has presented a new non-radial DEA model based on a modification of Enhanced Russell Model (ERM model) in the presence of an undesirable output in a fuzzy environment. Hereafter, a method for solving the proposed fuzzy DEA model based on the concept of alpha cut and possibility approach is presented. A useful stochastic closeness coefficient is also proposed to present a complete ranking. The proposed methodology is applied to evaluate the efficiencies of barley production farms in 22 provinces in Iran