A study was conducted at the University of Baghdad-College of Agricultural Engineering Sciences - Department of Agricultural Machinery and Equipment for the agricultural season 2023 with the aim of designing, manufacturing and testing a machine used to planting agricultural nursery tray with different types of vegetable or horticultural seeds or forest seeds of various forms, and using different agricultural media where they are conducted The planting process is by pulling the seeds with a negative pressure vacuum system, and then they are feding to the dishes in their right place to complete the planting process. The study included three factors: The speed of the main belt in three levels: 0.43, 0.66 and 0.90 m.sec−1 and three types of agricultural media: Petmus, mix of Petmos and silt 1:1 and silt, and three types of seeds that were planted. A factorial experiment was carried out using a design (CRD) with three replications using the least significant difference and a probability level (LSD = 0.05) to compare the mean of the coefficients. Some performance indicators of the machine were studied, including: Consumed electrical power, electric power, the productivity of the machine, and the efficiency of the machine in picking seeds. The results indicated The peat moss medium excelled in recording the least consumed electric power, which amounted to 2075.8 watts, and the lowest electric power, which amounted to 0.007751 kWh. And the third speed of the conveyor belt excelled in achieving the highest productivity of dishes per hour, amounting to 105.08 dishes.hour−1, and the first speed achieved the least consumed power, amounting to 2047.3 watts, and the least consumed electrical energy at the third speed, which amounted to 0.007703 kWh.
This study provides valuable information on secondary microbial infections in H1N1 patients compared to Seasonal Influenza in Iraqi Patients. Nasopharynx swabs were collected from (12 ) patients infected with Seasonal influenza (11 from Baghdad and 1 Patient from south of Iraq) ,and ( 22 ) samples from patients with 2009 H1N1 ( 20 from Baghdad and 2 from south of Iraq). The results show that the patients infected with 2009 H1N1 Virus were younger than healthy subjects and those infected with seasonal influenza. And the difference reached to the level of significance (p< 0.01) compared with healthy subjects.Two cases infected with 2009 H1N1 virus (9.1%) were fro
... Show MoreStatistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).
Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica
... Show MoreThe method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par
... Show MoreThis work evaluates the influence of combining twisted fins in a triple-tube heat exchanger utilised for latent heat thermal energy storage (LHTES) in three-dimensional numerical simulation and comparing the outcome with the cases of the straight fins and no fins. The phase change material (PCM) is in the annulus between the inner and the outer tube, these tubes include a cold fluid that flows in the counter current path, to solidify the PCM and release the heat storage energy. The performance of the unit was assessed based on the liquid fraction and temperature profiles as well as solidification and the energy storage rate. This study aims to find suitable and efficient fins number and the optimum values of the Re and the inlet tem
... Show MoreUniversity campuses in Iraq are substantial energy consumers, with consumption increasing significantly during periods of high temperatures, underscoring the necessity to enhance their energy performance. Energy simulation tools offer valuable insights into evaluating and improving the energy efficiency of buildings. This study focuses on simulating passive architectural design for three selected buildings at Al-Khwarizmi College of Engineering (AKCOE) to examine the effectiveness of their cooling systems. DesignBuilder software was employed, and climatic data for a year in Baghdad was collected to assess the influence of passive architectural strategies on the thermal performance of the targeted buildings. The simulations revealed that the
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