Al-Furat Al-Awsat Technical University

Optical modulators are fundamental components in modern optical communication systems. They enable the manipulation of light intensity, a crucial function for data transmission and signal processing. Graphene, a single atomic layer of carbon atoms arranged in a honeycomb lattice, has emerged as a promising material for high-performance electro-optical modulators due to its unique electrical and optical properties. This paper uses graphene to control the light passing through a silicon-on-insulator optical waveguide to emulate an optical modulator. The electrical properties of graphene are exploited by varying the applied voltage on a graphene layer coating the Silicon-on-Insulator waveguide so the concentration of carriers is changed and, therefore, the light interaction with graphene is modified. An effective intensity modulation of light through the Silicon-on-Insulator waveguide is achieved by controlling the carrier’s concentration within the graphene layer through the applied voltage. Numerical calculations based on three-dimensional electromagnetic simulation software are performed on a small size (40 μm long, 0.6 μm width and 0.25 μm height) Silicon-on-Insulator waveguide to design and test the optical response of the proposed modulator. Silicon-on-Insulator waveguide coated with a layer of graphene and Aluminium oxide material performs well as an optical modulator for optical communication systems at 1.55 μm wavelength.

Klebsiella pneumoniae, a gram-negative bacterium from the Enterobacteriaceae family, is a normal part of the human microbiota. It is present in about one-third of healthy individuals but can become an opportunistic pathogen, responsible for serious hospital-acquired infections like pneumonia, septicemia, urinary tract infections, and intra-abdominal infections. Rapid and accurate detection of K. pneumoniae is critical for controlling its spread. In this study, silver nanoparticles synthesized using the Tollens’ method were deposited on filter paper substrates to create a Surface-Enhanced Raman Scattering (SERS) biosensor. This SERS sensor detected K. pneumoniae at very low concentrations (as low as 10¹ CFU) by enhancing molecular vibrations due to electromagnetic effects of the nanoparticles. The sensor showed a detection enhancement factor of 7.942 × 10⁶, with a relative standard deviation (RSD) of 5.80% for six repeated measurements. The plasmonic SERS substrates demonstrated excellent recyclability, reproducibility, and stability, making them a promising tool for detecting K. pneumoniae in low numbers. Graphical Abstract

Globally, adult males are frequently affected with chronic bacterial prostatitis (CBP), a prevalent urological illness. It is believed that the inflammatory reaction linked to CBP causes oxidative stress, which may change the impacted people's levels of enzyme antioxidants the Effect of Chronic Bacterial Prostatitis on Levels Enzymatic Antioxidant. From October - 2022 to August - 2023, examined 270 clinical samples from subjects males (adults). Two groups were divided: the first group consisted of 180 male patients. In contrast, the second group consisted of 90 healthy males (adults). Blood samples of (3-5) ml were collected from all participants and the levels of zinc, selenium, glutathione peroxidase GPx, superoxide dismutase SOD, and catalase CAT were measured. The results showed that the levels of zinc, selenium, GPx, SOD, and CAT in men with chronic bacterial prostatitis were significantly lower than those in the control group of patients who were visited after ethical treatment at AL-Sader Medical City in Najaf Governorate. Aspects approved by the Medical Ethics Committee of the Iraqi Ministry of Health. The aim of this meta-analysis is to statistically evaluate and in-depth examine the case-control studies conducted so far regarding the effects of CBP on the levels of enzymatic antioxidants in adult men.

This study was designed for infection with T. gondii by using specific IgG and IgM by VIDAS technique to indicate T. gondii. The experiment was distributed into three groups: (A) (N=30) kidney failure patients group, (B) (N=30) patients with kidney failure patients and COV-19, (C) (N=30) patients with covid- 19 + kidney failure and T.gondii infected. The criteria were studied Blood Urea (B.U.) mg\dl, Serum Creatinine (S.Cr.) mg\dl, Albumin serum (ALB) mg\dl, Total serum protein (T.S.P) and phosphate test (PO4) mg\dl. The examination of the parameters mentioned above was performed using a spectrophotometer device by wavelength 490-540 nm. It was seen that the amounts of creatinine, PO4, and blood urea were significantly higher (P < 0.05) in COVID-19-infected patients with renal failure than in the control group of patients with renal failure. However, it was noted that patients with COVID-19 and kidney failure, as well as T. gondii infection, demonstrated a return to normal blood urea levels. Creatinine and phosphorous oxyhydroxide levels were not different between those with COVID-19 + renal failure and those with T.gondii infection (P > 0.05).

COVID-19 pandemic disease continues to spread over all countries. There is increasing evidence that the SARA-COV-2 virus can cause damage to both the peripheral and central nervous systems through either direct or indirect mechanisms, potentially leading to long-term neurological effects. Рrolonged COVID can cause clinical symptoms such as anxiety, depression, fatigue, brain fog with cognitive dysfunction and memory problems. Brain fog, a colloquial term for cognitive Impairment (CI) , has emerged as a significant long-term neurological complication following COVID-19 infection. Recently, several studies have indicated that these cognitive symptoms can persist for months to over a year post-infection, affecting the quality of life of survivors. This article reviews the mechanisms of coronavirus invasion of the brain and how brain fog occurs after long-term Covid disease.

Aquatic ecosystems face increasing contamination from plastic pollutants, with Polyvinyl Chloride (PVC) being one of the most prevalent. This study investigates the impact of ingested PVC powder particles on the body weight and length of O. niloticus (Nile tilapia), a commercially important fish species and a common inhabitant of freshwater systems worldwide. Groups of Nile tilapia specimens were subjected to different amounts of PVC powder particles, specifically 500 ppm and 1000 ppm. Specimens and water parameters were meticulously observed for 40 days. The control groups were provided with a diet that did not contain any PVC. After exposure, body weight and length were measured every two weeks and compared between the experimental and control groups. Preliminary results suggest a significant correlation between PVC ingestion and alterations in the growth parameters of Nile tilapia. Fish exposed to higher concentrations of PVC exhibited reduced body weight and length compared to the control groups. These findings indicate a potential negative impact of PVC pollution on the growth and development of aquatic organisms, highlighting the urgent need for effective strategies to mitigate plastic pollution in freshwater ecosystems.

This work compares the data-based using (bidirectional long short-term memory (BiLSTM)) and model-based using (extended Kalman filter (EKF) and least mean squares (LMS)) strategies to track the communication beams for millimeter wave (mm-wave) vehicular communications. This work utilizes the DeepMIMO dataset for adaptive filtering and machine learning(ML) as training data for the proposed system. BiLSTM networks are commonly used in beam tracking due to their effectiveness in processing sequential data and capturing temporal dependencies depending on their features like sequential data handling and their ability to process data in both forward and backward directions; they can handle the vanishing gradient problem that often occurs in traditional RNNs, the ability to enhance feature learning, and it can be more robust to noise. This work evaluates the simulation results using the average error and outage probability concerning signal-to-noise ratio(SNR) and the number of broadcast antennas. The results show that the ML performance is higher than that of the EKF and LMS, with the LMS having the lowest performance. Furthermore, the work illustrates the mean squares error(MSE) of the angle of arrival(AoA) using the time index, which yields better performance results over time for ML than the MSE values of the EKF and LMS, which rise due to more mistakes.

Traffic‐induced ground vibrations cause significant problems for residents and nearby structures. Reducing the effect of these vibrations on the neighboring environment is a key challenge, particularly in urban areas. This study presents both numerical and experimental investigations of the performance of mass scatters for screening ground vibrations. A three‐dimensional numerical model is validated and extended to conduct a comparative study on the efficiency of three geotechnical methods of isolation. These methods include trench barriers, wave‐impeding blocks (WIBs), and mass scatters. The results showed that mass scatters represent an efficient way of scattering ground vibrations, and their efficiency is mainly related to the weights of mass scatters and their natural frequency, which control the dynamic soil response in the frequency domain. Rigid trench barriers are less effective than soft ones, and their efficiency is more pronounced regarding the WIB. Soft barriers with a depth of an order of half of the wavelength can decrease the vibration levels by up to 50%, which is comparable to the performance of enormous mass scatters. The dimensions of WIBs must be chosen according to the wavelength of incident waves and the cutoff frequency of the topsoil layer. Considering the significant wavelength of traffic‐induced vibration, the use of trench barriers or WIBs becomes impractical and expensive; therefore, mass scatters appear to be an efficient and practical solution.

This research investigated the attenuation properties of gamma ray for some materials, lead (Pb) and tin (Sn), in the energy region from 0.1 - 3 MeV using X-COM code. As a result of increasing photon energy, both MAC and LAC of a selected material decreased. Lead (Pb) offers more effective shielding of gamma rays than tin (Sn) since it has the highest μ and μm values coupled with the lowest HVL and TVL.

Air pollution is widespread in the world and is considered one of the most important risk factors in Iraq, especially as a result of the lack of a green belt surrounding cities and the many causes of pollution, including traffic congestion, the spread of gas power plants and other causes of pollution. The most common factors of pollution in the air are the spread of gases that are harmful to human health, including monoxide, carbon dioxide (CO2), ozone and the spread of dust, which directly affects human health. A smart system has been proposed to measure levels of pollutants, of which carbon monoxide (CO), dioxide and dust are at the forefront. Several cities, including Baghdad, Karbala, Najaf and Hilla, were chosen to measure the percentage of disparity between pollutants in these cities, determine the percentage of CO2 on Google maps for these cities and update the data instantly by sending the data via the cloud computing. The implemented system consists of an Arduino Uno, a (MG811) sensor to measure CO2, a (MQ-2) sensor to CO and a (DSM501A PM2.5) sensor to measure air quality and the percentage of dust in the atmosphere. The data was also sent via the (Time4vps) cloud computing so that the data were updated instantly. The results obtained showed a difference in the percentage of pollutants between cities and different periods during one day and in one city. The proposed system is very successful to ministry of health if it is implemented in all cities and all the regions of cities around the country because it gives the alert to make all health organizations ready to receipt the higher number of patients in the emergency cases.

In this paper, a 5-pole quasi-reflectionless (QR) interdigital BPF with a wide range of non-reflecting signals is proposed, offering good impedance matching. Two absorptive T-shaped stubs are placed at the input and output ports of the interdigital BPF. The absorptive stubs are optimised to obtain the reflection as low as possible for a wide range. Additionally, we shift the adjacent resonators of the interdigital BPF up and down to improve overall performance. The design follows a specific procedure in order to investigate factors affecting the performance. Firstly, the convetional interdigtial bandpass filter is designed relying on the coupling matrix method. Next, we study the conversion of the stopband filter into an absorptive stub. The input impedance of the stub plays a vital role in determining the reflection bandwidth, so it is optimised as well. Meanwhile, the interdigital BPF and one abosrptive stub are combined, where the bandwidth of reflectionless becomes wider compared to the conventional interdigital BPF. Another absorptive stub is added into the other port of the BPF to make the proposed filter symmetric. Different orders of the interdigital PBF are analysed, where the fifth order has the best response. The proposed filter is fabricated and tested, and an FR4 substrate with a thickness of 1.5 mm and a dielectric constant of 4.5 is utilized. The simulation and measurement results agree well. The realised S11 is less than −10dB from 0.72 GHz to 4.1 GHz (i.e., −10dB reflectionless bandwidth 3.38 GHz), while the realised S11 is less than −3dB from 0.42 GHz to 6 GHz (i.e., −3dB reflectionless bandwidth 5.58 GHz). The S21 response is good for the transmission band. Finally, some slots are etched out from the gorund to reduce the influence of the second harmonic response.

Employing unmanned aerial vehicles (UAVs) within relay-based free-space optical (FSO) communication systems provides notable benefits in addressing turbulence induced atmospheric scintillation by virtue of their dynamic movement. This research performs an in-depth evaluation of the effectiveness of a UAV-based FSO system that employs a dual-hop decode-and-forward approach with multiple transmission sources. By integrating the Gamma-Gamma (GG) distribution into the analysis of atmospheric turbulence in conjunction with considerations of atmospheric losses, misalignment, and variations in arrival angles, we derive an accurate equation for the probability density function (PDF) of the overall channel gain. In this article, a mathematical formula for the system's average bit error rate (BER) is derived, and its accuracy is confirmed through thorough Monte Carlo simulations. These simulations illustrate the effectiveness of our theoretical framework across different scenarios. The findings emphasize the enhanced efficiency and adaptability of FSO systems utilizing UAVs for multiple data sources, offering valuable knowledge for applications like surveillance across various locations and communication across mobile networks.

Abstract This study explores self-compacting concrete (SCC) enhancement by incorporating silica fume, fy ash, or both across various mix compositions. The research evaluates six predictive models—linear regression, nonlinear regression, pure quadratic, interaction, full quadratic, and artifcial neural network (ANN)—to predict the compressive strength of SCC. A dataset of 330 experimental studies covering a wide range of parameters, such as water/cement ratio, cement content, aggregate content, superplasticizer content, silica fume content, fy ash content, and curing time, is used. The compressive strength of the datasets ranges from 4.9 to 87 MPa, while slump fow diameter ranges from 450 to 790 mm. The models are assessed using objective function, root mean square error (RMSE), scatter index (SI), and mean absolute error (MAE). The ANN is the most accurate among the models, achieving an R2 of 0.94, RMSE of 3.56 MPa, MAE of 2.67 MPa, and SI of 0.09. The models efectively predict compressive strength across various concrete compositions, although they do not predict slump fow diameter, as SCC specifcations require it to be within 550 to 850 mm.

Demand response (DR) programs are potentially powerful tools to support renewable energy integration, ensure power balance and update electricity market mechanism. Based on the existing work, in this paper propose a day-ahead a smart electricity markets for a decarbonized microgrid system with the DR program. The proposed system aims to minimize the operating cost, and carbon emission. An IEEE 33-bus system is used as an illustrative example to validate the application of the proposed smart electricity market model in the real large system. The proposed unit utilizes the African Vultures Optimization Algorithm (AVOA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Artificial Rabbits Optimization Algorithm (AROA), and Grasshopper Optimization Algorithm (GOA). The simulation results reveal that energy costs and PAR can be reduced energy cost, and carbon emission, whereas the Discomfort Index (DI) is maintained at a minimum value.

Purpose This scoping review aims to deepen the understanding of end-of-life anticancer drug use in lung cancer patients, a disease marked by high mortality and symptom burden. Insight into unique end-of-life treatment patterns is crucial for improving the appropriateness of cancer care for these patients. Methods Comprehensive searches were carried out in Medline and Embase to find articles on the utilization of anticancer drugs in the end of life of lung cancer patients. Results We identified 68 publications, highlighting the methodological characteristics of studies including the timing of the research, disease condition, treatment regimen, type of treatment, and features of the treatment. We outlined the frequency of anticancer drug use throughout different end-of-life periods. Conclusion This review provides a comprehensive overview of primary studies exploring end-of-life treatments in lung cancer patients. Methodological inconsistencies pose many challenges, revealing a notable proportion of patients experiencing potential overtreatment, warranting more standardized research methods for robust evaluations.

Climate change and global warming necessitate the shift toward low-emission, carbon-free fuels. Although hydrogen boasts zero carbon content and high performance, its utilization is impeded by the complexities and costs involved in liquefaction, preservation, and transportation. Ammonia has emerged as a viable alternative that offers potential as a renewable energy storage medium and supports the global economy’s decarbonization. With its broader applicability in large power output applications, decentralized energy sources, and industrial locations off the grid, ammonia is increasingly regarded as an essential fuel for the future. Although ammonia provides a sustainable solution for future low-carbon energy fields, its wide-scale adoption is limited by NOX emissions and poor combustion performance under certain conditions. As research on ammonia combustion expands, recent findings reveal factors impacting the chemical reaction pathways of ammonia-based fuels, including the equivalence ratio, fuel mixture, pressure, and temperature. Investigations into ammonia combustion and NOX emissions, at both laboratory and industrial scales, have identified NOX production peaks at equivalence ratios of 0.8–0.9 for ammonia/hydrogen blends. The latest studies about the NOX emissions of the ammonia flame at different conditions and their generating pathways are reviewed in this work. Effective reduction in NO production from ammonia-based flames can be achieved with richer blends, which generate more NHi radicals. Other advanced NOX mitigation techniques such as plasma-assisted combustion have been also explored. Further research is required to address these challenges, reduce emissions, and improve efficiencies of ammonia-based fuel blends. Finally, the extinction limit of ammonia turbulent flame, its influential factors, and different strategies to promote the ammonia flame stability were discussed. The present review contributes to disseminating the latest advancements in the field of ammonia combustion and highlights the importance of refining reaction mechanisms, computational models, and understanding fundamental phenomena for practical implications.

This paper investigates the potential transformation to be ushered in by 6G technology in telecommunications, enabling huge data rates, low latencies, high-reliability connectivity, and broadened connections based on its heterogeneous cellular architecture. It is a complex work dealing with an analysis of the evolution toward 6G while using heterogeneous networks (HetNets) that take advantage of various technologies, such as small cells, macro cells, non-terrestrial networks (NTNs), and ultra-dense networks (UDNs). It discusses the architectural progress, such as spectrum efficiency, advanced beamforming, and application of AI and ML toward network optimization for 6G HetNets. The deployment challenge for the 6G HetNets: spectrum allocation, energy efficiency, interoperability, security, with solutions such as cognitive radio networks, energy harvesting, and blockchain for improved security. It also delves into the far-reaching impacts of 6G HetNets in sectors like smart cities, autonomous driving, IoT, telemedicine, and AR/VR experiences. The importance of 6G is further expounded in achieving seamless connectivity, superior mobile broadband, and supporting massive machine-type communications, therefore reshaping our digital interactions. The paper concludes on the significance of collaborative research, policy development, and standardization efforts in 6G deployment complexity management and exploitation of the full potential of 6G toward technological and societal development. It presents a holistic view of 6G’s heterogeneous cellular architecture, emphasizing its architectural innovations, challenges, and the promising future it portends for wireless communication systems.

This study aims to enhance object detection systems by comparing pre-trained classification models with custom-trained ones, focusing on task-based deep learning for image recognition. The problem addressed is the challenge of accurately detecting and classifying objects in complex environments where traditional recognition systems may fall short. The proposed solution leverages transfer learning utilizing pre-trained models like ResNet or VGGNet as feature extractors. By exploiting the convolutional layers of these models, the system captures common features for specific detection tasks. Experimental analyses on benchmark datasets confirm the efficacy of this approach, demonstrating improved detection accuracy and efficiency in various scenarios. Specifically, FasterRCNN achieves a mean Average Precision (mAP) of 78% on synthetic datasets and 74% on real datasets at an Intersection over Union (IoU) threshold of 0.5. This indicates FasterRCNN's superior performance in terms of accuracy, making it a strong candidate for applications requiring high detection accuracy.