Objective: This study aims to examine how implementing Extensible Business Reporting Language (XBRL) enhances the efficiency and quality of environmental audits and sustainability reporting in eco-friendly universities. Aligned with Sustainable Development Goal 12 (Responsible Consumption and Production), the study emphasizes promoting transparency and precision in sustainability reporting to encourage responsible management of resources within academic institutions.   Theoretical Framework: The importance of our study is evident in the importance of accurate and transparent reports in the development of environmental performance with theories of sustainable reporting and environmental auditing. One of the most important digital

This study investigates the influence of silver oxide (Ag2O) concentration on the optical characteristics of phosphate bioactive glasses (PBGs). PBGs have emerged as promising alternatives to conventional silicate glasses in the medical field due to their excellent bioactivity and chemical resistance. Samples with varying Ag2O concentrations (0, 0.25, 0.5, and 0.75g) were sintered at 780°C for 2 hrs in an electric furnace. The samples were subjected to Fourier transfer infrared spectroscopy (FTIR), ultraviolet-visible (UV-Vis) spectroscopy, and photoluminescence (PL) tests to assess their functional groups and optical properties. By analyzing the FTIR spectrum of phosphate bioactive glass containing different amounts of Ag2O, it is

The impact of decorating Fe, Ru, Rh, and Ir metals upon the sensing capability of a gallium nitride nanotube (GaNNT) in detecting chlorine trifluoride (CT) was scrutinized using the density functionals B3LYP and B97D. The interaction of the pristine GaNNT with CT was a physical adsorption with the sensing response (SR) of approximately 6.9. After decorating the above-mentioned metals on the GaNNT, adsorption energy of CT changed from −5.8 to −18.6, −18.9, −19.4, and −20.1 kcal/mol by decorating the Fe, Ru, Rh, and Ir metals into the GaNNT surface, respectively. Also, the corresponding SR dramatically increased to 39.6, 52.3, 63.8, and 106.6. This shows that the sensitivity of the metal-decorated GaNNT (metal@GaNNT) increased by in

In this research was study the effect of increasing the number of layers of the semiconductor films as PbS on the average grain sizes and illustrate the relationship between the increase in the average grain size and thickness of the membrane, and membrane was prepared using the easy and simple and does not need the complexity of which is that the chemical bath , and from an X-ray diffraction found that the material and the installation of a random cubic and when increasing the number of layers deposited note the emergence of a number of vertices of a substance and PbS at different levels but the level is more severe (200) as well as the value is calculated optical energy gap and found to be not affected by increase thickness and from th

A field experiment was conducted in an agricultural field in Al-Hindia district, Karbala governorate in a silty clay soil during the year 2020. The research included a study of two factors, the first is the depth of plowing at two levels, namely 13 and 20 cm, which represented the main blocks. The second is the tire inflation pressure at two levels, namely (70 and 140 kPa), which represented the secondary blocks. Slippage percentage, field efficiency, leaf area, and 300 grain weight were studied. The experiment was carried out using a split-plot system under a Randomized complete block design, at three replications. The tillage depth of 13 cm exceeds/transcend by giving it the least slippage of (11.01%), the highest field efficiency of (50.

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables