Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicio

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance

Objectives: This study aims to evaluate the role of social media in promoting awareness of green university initiatives and assess the effectiveness of sustainability reports in engaging students at Baghdad University. In alignment with Sustainable Development Goal 12 (Responsible Consumption and Production),It seeks to provide recommendations for enhancing digital platforms for sustainability communication.   Theoretical Framework: The study is grounded in the Green University Model, Social Media Engagement Theory, and the Sustainability Reporting Framework, which emphasize integrating sustainable practices in education, using digital platforms for community engagement, and leveraging sustainability reports for transparency and

Indoor air pollution is a critical environmental challenge with negative impacts on human health, especially in urban and enclosed settings. This study aimed to evaluate the tolerance of three indoor ornamental plants—Epipremnum aureum, Sansevieria trifasciata, and Syngonium podophyllum Schott—against pollutants generated by cigarette smoke and kerosene combustion. Plants were exposed under controlled indoor conditions, while particulate matter (PM₁. ₀, PM₂. ₅, PM₁₀), total volatile organic compounds (TVOCs), and carbon dioxide (CO₂) were measured before, during, and after exposure. Plant tolerance was assessed using the Air Pollution Tolerance Index (APTI), calculated from total chlorophyll content, leaf extract pH, ascor

A case–control study (80 patients with chronic hepatitis B virus [HBV] infection and 96 controls) was performed to evaluate the association of an IL12A gene variant (rs582537 A/C/G) with HBV infection. Allele G showed a signifcantly lower frequency in patients compared to controls (31.2 vs. 46.9%; probability [p]=0.009; corrected p [pc]=0.027) and was associated with a lower risk of HBV infection (odds ratio [OR]=0.49; 95% confdence interval [CI]=0.29–0.83). A similar lower risk was associated with genotypes CG (17.5 vs. 29.2; OR=0.25; 95% CI=0.08–0.81; p=0.02) and GG (10.0 vs. 16.7; OR=0.25; 95% CI=0.07–0.91; p=0.036), but the pc value was not signifcant (0.12 and 0.126, respec‑ tively). Serum IL35 levels showed signifcant difere

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

The present paper is an experimental study to improve the productivity of the conventional solar still. This done by modifying conventional still in a way that the distilled basin is larger than distillation basin, thus providing an increase in the condensation surface and speeding up the condensation process. Moreover, increase in the dimensions of the distilled base helps coupling reflective panels to the distilled base to reflect incident solar radiation to the distillation basin. For this purpose , two solar stills were made, one conventional designand another made according to the proposed design. The two solar stills were tested during the period from February to July 2009 under varying weather conditions of Basra, Iraq (latitude o