Determination of physical and mechanical properties of Zucchini (summer squash)

Abstract: Several physical and mechanical properties of Zucchini from Kermanshah province of Iran were determined.  The physical and mechanical properties of the zucchini are necessary for the design of automatic equipment for harvesting, processing, transporting, sorting and separating of samples.  At the average moisture content of 94.65% w.b., average of mass, volume, dimensions (big, medium and small diameters), geometric mean diameter, projected area (big, medium and small area), criteria areas, arithmetic means diameter, sphericity, density and surface area were 80.81 g, 85 cm3, 111.7 mm, 34.58 mm, 33.87 mm, 51.74 mm, 3892.52 mm2, 3792.07 mm2, 1126.44 mm2, 2937.02 mm2, 60.05 mm, 45.49%, 0.96 g/cm3 and 8268.20 mm2, respectively, and ratio of weight of rind per weight of fruit was 0.25.  Mechanical properties that measured including elasticity modulus, maximum force which fruit can be supported, work which performed to this force under compression loading, deformation at maximum force and penetration force, their averages were found 0.73 GPa, 167 N, 762.82 N.mm, 8.81 mm and 1.26 N, respectively. Keywords: Zucchini, physical and mechanical properties, compression loading, penetration force

Characterization based machine learning modeling for the prediction of the rheological properties of water‑based drilling mud

The successful drilling operation depends upon the achievement of target drilling attributes within the environmental and economic constraints but this is not possible only on the basis of laboratory testing due to the limitation of time and resources. The chemistry of the mud decides its rheological potential and selection of the techniques required for recycling operations. Conductivity, pH, and photometer testing were performed for the physio-chemical characterization of the grass to be used as an environmental friendly drilling mud additive. In this study, different particle sizes (75, 150, and 300 µm) of grass powder were mixed in mud density of 8.5, 8.6, and 8.7 ppg in the measurement of gel strength and viscosity of drilling mud. The grass additive was added in different weight conditions considering no additive, 0.25, 0.5, and 1 g to assess the contribution of grass on the gel strength and viscosity of the drilling mud. The machine learning techniques (Multivariate Linear Regression Analysis, Artificial Neural Network, Support Vector Machine Regression, k-Nearest Neighbor, Decision Stump, Random Forest, and Random Tree approaches) were applied to the generated rheological data. The results of the study show that grass can be used for the improvement of the gel strength and viscosity of the drilling mud. The highest improvement of the viscosity was seen when grass powder of 150 µm was added in the 8.7 ppg drilling mud in 0.25, 0.5, and 1 g weights. The gel strength of the drilling mud was improved when the grass additive was added to the drilling mud 8.7 ppg. Random forest and Artificial Neural Network had the same results of 0.72 regression coefficient (R2) for the estimation of viscosity of the drilling mud. The random tree was found as the most effective technique for the modeling of gel strength at 10 min (GS_10min) of the drilling mud. The predictions of Artificial Neural Network had 0.92 R2 against the measured gel strength at 10 s (GS_10sec) of the drilling mud. On average, Artificial Neural Network predicted the rheological properties of the mud with the highest accuracy as compared to other machine learning approaches. The work may serve as a key source to estimate the net effect of grass additives for the improvement of the gel strength and viscosity of the drilling mud without the performance of any large number of laboratory tests.publishedVersio

Comparative analysis of exhaust gases from MF285 and U650 tractors under field conditions

Agricultural machinery is an important source of emission of air pollutant in rural locations.  This work deals with the effects of types of tractors and operation conditions on engine emission.  The values of some exhaust gases (HC, CO, CO2, O2 and NO) from two common tractors (MF285 and U650) at three situations (use of ditcher, plowing and cultivator) were evaluated in the West of IRAN (Kermanshah).  In addition, engine oil temperature at operation conditions was measured.  Also results showed the values of exhaust HC and O2 of MF285 are lower than U650, while the other exhausts gases (CO, CO2, and NO) of MF285 are higher than U650.  Value of NO emission increased as engine oil temperature increased.  All of exhaust gases except CO have a significant relationship with type of tractors, while all of measured gases have a significant relationship with installed instruments at 1%.   Keywords: environmental pollution, exhaust gases, tracto

Noise evaluation of MF285 tractor while pulling a trailer in an asphalt road

Tractors have been used for transportation on roads by many farmers in addition to use in the field operations. MF285 tractor is the popular kind of tractor in Iran (about 30% of all tractors) and almost this tractor has been used without cabin.  Despite the problems caused by noise from the tractors and all its adverse effects on users and observers, no comprehensive research has been done on them.  The result of this research indicate that the noise level of MF285 tractor, in 2250 r/min engine speed, will be 90 dB(A) which in comparison with the standard value, 85 dB(A), is dangerous for operator’s ears.  The test site was prepared according to the international standards.  The noise emitted by tractor in three gears (2, 3 and 4) and three speeds (1,500, 1,950 and 2,250 r/min) were measured and then analyzed statistically.  Analysis of variance and Duncan’s mean comparison test showed that the Sound Pressure Level (SPL) at the position of the driver in comparison to the observer position was statistically significant (

Mass modeling of caper (Capparis spinosa) with some engineering properties

Nomenclature M = fruit mass, g; V = fruit Volume, cm 3 ; Dg = geometric mean diameter, mm; S = surface area, mm 2 ; L = length of fruits, mm; W = width of fruit, mm; T = thickness of fruit, mm; PA1 = first projected area which perpendicular to L direction, mm 2 ; PA2 = second projected area which perpendicular to W direction, mm 2 ; PA3 = third projected area which perpendicular to T direction, mm 2 ; CPA = criteria projected area, mm 2 ; SD = standard deviation; b0, b1, b2 = curve fitting parameters; X = independent parameter. Abstract Introduction Horticultural crops used as food with a similar weight and uniform shape are in high demand in terms of marketing value. Objectives Therefore, an awareness of methods for grading fruits and vegetables based on weight is crucial. A part of this research was aimed at presenting some physical properties of caper. Methods In addition, in this study, the mass of caper was predicted using different physical characteristics in four models that include linear, quadratic, S-curve and power. Results According to the results, all properties considered in the current study were found to be statistically significant at the 1% probability level for the best and the worst models for prediction; the mass of caper was based on volume and second projected area of the caper with determination coefficients of 0.984 and 0.323, respectively. Conclusion Mass model based on first projected area from an economical standpoint is recommended. Lorestani AN, Jaliliantabar F, Gholami R (2012) Mass modeling of caper (Capparis spinosa) with some engineering properties. Quality Assurance and Safety of Crops & Foods, 4

Noise evaluation of MF285 and U650 tractors by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) method

In this research ANFIS method has been used to predict sound pressure levels of MF285 and U650 tractors for following machines: moldboard plow, chisel plow, cultivator, rotary tiller, boom-type sprayer, disk harrow and ditcher. Combination of fuzzy logic with architectural design of neural network leads to creation of neuro-fuzzy systems, which benefit from feed forward calculation of output and back-propagation learning capability of neural networks, while keeping interpret-ability of a fuzzy system. An adaptive neuro-fuzzy inference system architecture based on the Takagi-Sugeno model created to modeling of sound pressure level of MF285 and U650 tractors during agricultural operations. The testing performance of the proposed ANFIS model revealed a good predictive capacity to yield acceptable error measures with, R2= 0.917 and also RMSE= 1.06, SSE= 76.11 and MAE= 0.7495. The study recommends that the ANFIS technique can be successfully used in estimation of sound pressure level of MF285 and U650 tractors

A modified empirical criterion for strength of transversely anisotropic rocks with metamorphic origin

A modified empirical criterion is proposed to determine the strength of transversely anisotropic rocks. In this regard, mechanical properties of intact anisotropic slate obtained from three different districts of Iran were taken into consideration. Afterward, triaxial rock strength criterion introduced by Rafiai was modified for transversely anisotropic rocks. The criterion was modified by adding a new parameter α for taking the influence of strength anisotropy into consideration. The results obtained have shown that the parameter α can be considered as the strength reduction parameter due to rock anisotropy. The modified criterion was compared to the modified Hoek–Brown (Saroglou and Tsiambaos) and Ramamurthy criteria for different anisotropic rocks. It was concluded that the criterion proposed in this paper is a more accurate and precise criterion in predicting the strength of anisotropic rocks

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

14 p.Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Publisher Copyright: © 2021, The Author(s).Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.Peer reviewe

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change