Traction force and power requirement when performing primary tillage occupy the minds of almost farmers, this field research had aim to determine and calculate the pulling force of the most commonly used moldboard and chisel plows, the research conducted in silt clay loam for chisel and moldboard plows as the main factor, two depths of tillage 18 and 25 cm as a second factor and three speeds of tractor 2.55, 4.30 and 6.15 km.h-1 as a third factor. Moldboard plow recorded least traction force 7.550 kN, drawbar power 11.583 hp, power losses due to slippage 1.088 hp, power on the rear axle of the tractor 15.770 hp and brake horse power 17.495 hp. Chisel plow recorded best traction efficiency 76.217 % and total traction efficiency 68.659 %. Dep

Origination for Teaching Physics at Secondary Stage main of the Educational Aims

The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperatu

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time t . The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integrated with the FD method t

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

This paper reports experimental and computational fluid dynamics (CFD) modelling studies to investigate the effect of the swirl intensity on the heat transfer characteristics of conventional and swirl impingement air jets at a constant nozzle-to-plate distance ( L = 2 D). The experiments were performed using classical twisted tape inserts in a nozzle jet with three twist ratios ( y = 2.93, 3.91, and 4.89) and Reynolds numbers that varied from 4000 to 16000. The results indicate that the radial uniformity of Nusselt number (Nu) of swirl impingement air jets (SIJ) depended on the values of the swirl intensity and the air Reynolds number. The results also revealed that the SIJ that was fitted with an insert of y = 4.89, which correspo

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences