Concerns about the environment, the cost of energy, and safety mean that low-energy cold-mix asphalt materials are very interesting as a potential replacement for present-day hot mix asphalt. The main disadvantage of cold bituminous emulsion mixtures is their poor early life strength, meaning they require a long time to achieve mature strength. This research work aims to study the protentional utilization of waste and by-product materials as a filler in cold emulsion mixtures with mechanical properties comparable to those of traditional hot mix asphalt. Accordingly, cold mix asphalt was prepared to utilize paper sludge ash (PSA) and cement kiln dust (CKD) as a substitution for conventional mineral filler with percentages ranging fro

Preserving the Past and Building the Future: A Sustainable Urban Plan for Mosul, Iraq

The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production

This study is achieved in the local area in Eridu oil field, where the Mishrif Formation is considered the main productive reservoir. The Mishrif Formation was deposited during the Cretaceous period in the secondary sedimentary cycle (Cenomanian-Early Turonian as a part of the Wasia Group a carbonate succession and widespread throughout the Arabian Plate. There are four association facies are identified in Mishrif Formation according the microfacies analysis: FA1-Deep shelf facies association (Outer Ramp); FA2-Slope (Middle Ramp); FA3-Reef facies (Shoal) association (Inner ramp); FA4-Back Reef facies association. Sequence stratigraphic analysis show there are three stratigraphic surfaces based on the abrupt changing in depositional

This study was conducted to explore the effects of using ionized water on the productive and physiological performance of Japanese quails (Coturnix japonica). Our study was conducted at a private farm from 20th April, 2016 to 13th July, 2016 (84 d). One hundred 42-day-old Japanese quail chicks were used, divided randomly into 5 groups with 4 replicates. Treatments consisted in a control group (T1 - normal water:), alkaline (T2 - pH 8 and T3 - pH 9), and acidic water (T4 - pH 6 and T5 - pH 5). All birds were fed a balanced diet of energy and protein. The egg production ratio, egg weight, cumulative number of eggs, egg mass, feed conversion ratio, productivity per hen per week, and effects on plasma lipids, uric acid, glucose, calcium, and ph

To evaluate the effectiveness of different microwave irradiation exposure times on the disinfection of dental stone samples immersed in different solutions, and its affect on the dimensional accuracy and surface porosity. Dental stone casts were inoculated with an isolate of Bacillus subtilis to examine the efficiency of microwave irradiation as a disinfection method while immersed in different solutions; water, 40% sodium chloride, or without immersion for different durations. Dimensional accuracy and surface porosity were also evaluated. Significant reduction in colony counts of Bacillus subtilis were observed after 5 minutes of microwave irradiation of immersed dental casts in water and NaCl solution. No evidence of growth was observed a

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