Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownersh

A Tonido cloud server provides a private cloud storage solution and synchronizes customers and employees with the required cloud services over the enterprise. Generally, access to any cloud services by users is via the Internet connection, which can face some problems, and then users may encounter in accessing these services due to a weak Internet connection or heavy load sometimes especially with live video streaming applications overcloud. In this work, flexible and inexpensive proposed accessing methods are submitted and implemented concerning real-time applications that enable users to access cloud services locally and regionally. Practically, to simulate our network connection, we proposed to use the Raspberry-pi3 m

This study is conducted to determine the effect of pathogenicity of the fungus Lecanicillium lecanii in some aspects of life of the insect saw toothed beetle Oryzaephilus surinamensis L. (Coleoptera: Silvanidae) under laboratory conditions with three concentrations of spores and mildew commentator (1 × 103, 1 × 105, 1 × 107) spore / ml , on eggs and larvae second phase of the insect .The study also includs the effect of the fungus concentrations of germination on rice (jasmine) by using direct spray treatment. The results show great fungus efficiency in the control of some aspects of life of the insect, where varied efficiency depends on the concentration of spores, The highest percentage loss of eggs is 63.33% at a concentration

The catalytic cracking of three feeds of extract lubricating oil, that produced as a by-product from the process of furfural extraction of lubricating oil base stock in AL-Dura refinery at different operating condition, were carried out at a fixed bed laboratory reactor. The initial boiling point for these feeds was 140 ºC for sample (1), 86 ºC for sample (2) and 80 ºC for sample (3). The catalytic cracking processes were carried out at temperature range 325-400 ºC and initially at atmospheric pressure after 30 minutes over 9.88 % HY-zeolite catalyst load. The comparison between the conversion at different operating conditions of catalytic cracking processes indicates that a high yield was obtained at 375°C, according to gasoline pr

Early detection of eye diseases can forestall visual deficiency and vision loss. There are several types of human eye diseases, for example, diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. Diabetic retinopathy (DR) which is brought about by diabetes causes the retinal vessels harmed and blood leakage in the retina. Retinal blood vessels have a huge job in the detection and treatment of different retinal diseases. Thus, retinal vasculature extraction is significant to help experts for the finding and treatment of systematic diseases. Accordingly, early detection and consequent treatment are fundamental for influenced patients to protect their vision. The aim of this paper is to detect blood vessels from

This study aims to remove Cd(II) ions from simulated wastewater by using Chlorophyceae algae (CA). Different parameters were studied to show their effects on the biosorption efficiency of CA. These parameters are: the effect of pH 3-7, initial metal ion concentration 20-200 mg/L, sorbent dos-age 0.05-2 g/L, contact time 5-180 min, and agitation speed 100-300 rpm. We found that both the Langmuir and Freundlich models appropriate for characterizing the metal removal process. The biosorption data fit best with the results of the pseudo-second-order kinetic model, demonstrating that the chemisorption process is the dominant mechanism controlling the removal. CA was char-acterized using the scanning electron microscopy test, prior to and post bi

Manganese sulfate and Punica granatum plant extract were used to create MnO₂ nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO₂ NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencie

Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi