Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference ve

Nd:YAG laser pulses of 9 nanosecond pulse duration and operating wavelength at 1.06 μm, were utilized to drill high thermal conductivity and high reflectivity aluminum and copper foils. The results showed a dependence of drilled holes characteristics on laser power density and the number of laser pulses used. Drilled depth of 74 ϻm was obtained in aluminum at 11.036×108 W/cm2 of laser power density. Due to its higher melting point, copper required higher laser power density and/or larger number of laser pulses to melt, and a maximum depth of 25 μm was reached at 13.46×108 W/cm2 using single laser pulse.

Phosphorus and dye (direct black) removal for small – scale wastewater applications were investigated using oven dried alum sludge (ODS).The use of alum sludge not only provides a low cost technique but also reduces the hazard and the cost related to the disposal of large amount of alum sludge. Phosphorus and dye removal exceeds 90% for all operating conditions applied in the research.
The residuals generated during the treatment of wastewater were further tested to study the possibility of aluminum leaching from oven dried alum sludge during the adsorption of phosphorus and dye. These tests observed a reduction in aluminum leaching indicating a lower risk imposed on land and surface water based on disposal options rather than on al

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2, 0, 0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlat

Single-photon detection concept is the most crucial factor that determines the performance of quantum key distribution (QKD) systems. In this paper, a simulator with time domain visualizers and configurable parameters using continuous time simulation approach is presented for modeling and investigating the performance of single-photon detectors operating in Gieger mode at the wavelength of 830 nm. The widely used C30921S silicon avalanche photodiode was modeled in terms of avalanche pulse, the effect of experiment conditions such as excess voltage, temperature and average photon number on the photon detection efficiency, dark count rate and afterpulse probability. This work shows a general repeatable modeling process for significant perform

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

In this research and by using the concept of   , a new set of near  set which is nano-Ἷ-semi-g-closed set was defined. Some properties and examples with illustrative table and an applied example were presented.

The research demonstrates new species of the games by applying separation axioms via  sets, where the relationships between the various species that were specified and the strategy of winning and losing to any one of the players, and their relationship with the concepts of separation axioms via  sets have been studied.