Muthanna Journal of Pure Science (MJPS)

Rather than using speech to communicate with one another, the deaf and dumb use a set of signs known as “sign language”. Yet, utilizing signs to interact with this society is too difficult for non-sign language speakers. To facilitate communication for the deaf public, an application that can identify sign language motions must be developed. Regarding its importance, there are approaches with differing degrees of accuracy for recognizing American Sign Language ASL. The study aims to enhance the accuracy of current ASL identification approaches by putting forward a deep-learning model. A CNN was developed and trained to correctly recognize hand gestures that describe the ASL letters (A-Z). The proposed model performs exceptionally well, attaining high accuracy on the dataset, with a test accuracy of 99.97%. The model is a possible tool for practical applications in assistive technology for the hearing impaired since the results show that it can distinguish between distinct ASL hand signs.

This work exhibits simulation and design of a small-scale graphene plasmonic filter based photonic crystal fiber through finite element method investigation. Some cladding holes inside the PCF possess deposited 2-D graphene enabling the light transmission interaction which leads to surface plasmonic resonance (SPR). Improved optical filter performance results from this process because it enables better control and higher sensitivity and reduced signal loss so filters become optimal for use. A finite element method analyzes both polarization properties and spectral loss performance in the proposed structural design. The simulation evaluation was divided into two testing periods while changing the dimensions of PCF geometry. The investigation of first design components incorporated r = 6 and r = 5 while the second design components incorporated r = 6 and r = 7. The introduction of graphene materials into PCF rings comprises the two design models progressively from one to four rings. The design suffered maximum loss when four graphene rings were inserted in both designs. The absorption increases results in the creation of plasmonic polaritons. In the first design, when r = 6 and r = 5, the quasi-TE guided mode exhibits losses of -113000 dB/ mm for wavelengths λ = 1.14 μm. The quasi-TM guided mode exhibits losses of -109100 dB/ mm for wavelengths λ = 1.17 μm . In the second design, r = 6 and r = 7, the losses of the quasi-TE guided mode are -146800 dB/ mm for wavelengths λ = 1.16 μm. The quasi-TM guided mode exhibits losses of -146600 dB/mm at wavelengths λ = 1.2 μm.

This paper introduces new types of generalized weak mappings in intuitionistic topological spaces such as […] specifically focusing on contra-like mappings. The study explores the relationships between these mappings and investigates their connections within intuitionistic topological spaces. By utilizing extended sets and mappings, this work contributes to the ongoing research in intuitionistic topology, filling a gap in the current literature. The results presented here enhance the understanding of the structure of intuitionistic topological spaces, providing a deeper insight into the properties and behaviour of weak mappings in these spaces. This research opens up new avenues for further exploration and application in the field of topological spaces.

Enhancing low-light images is a major challenge in computer vision. It suffersfrom modest contrast, unclear details, and intensive noise. Low-w light image enhancement aimsed to enhance the quality of the image that is captured under imperfect lighting and restore the image with a high-accuracy colour distributioned with standard lighting. This paper discusses the evolution of techniques used to improve image quality, starting from traditional methods such as dehazingg, histogram equalisation (HE), gamma correction (GC), and Retinex theory methods to modern techniques that rely on deep learning networks based on specially trained models for low- light image enhancement.t Dynamic and restoring lost details, for example, CNNs, GANs, and U-Net.

Nuclear medicine plays a vital role in modern healthcare, utilising radioisotopes for various purposes. Approximately 95% of these radioisotopes are used for diagnostic imaging, while the remaining 5% contribute to treatment applications. Cobalt-57 radioisotopes are used in medical practise for diagnostics and radiotherapy. The excitation functionsns to produce 57Co radioisotope for medical applications using natural iron (Fe), nickel (Ni),i) and copper (Cu) for the reactions natFe(a,x)57Co, natFe(d,x)57Co, natFe(p,x)57Co, natNi(n,x)57Co, natNi(d,x)57Co, natNi(p,x)57Co, natCu(d,x)57Co, natCu(g,2n) 57Co are performed theoretically by Monte Carlo calculations with the TALYS 1.6 nuclear reaction simulation code. Theoretical calculated results are compared with available experimental data in the EXFORlibrary.

Recently, serious and enormous security challenges have led to the development of new and secure methods for data transmission, reducing the chances of hackers using their methods to analyze confidential information between the two parties. Therefore, data security and protection are among the most widely used techniques in the field of computer science, given their great importance in all fields, whether political, economic, or military. The primary objective of this research is to provide a hybrid security method for transmitting confidential text using encryption and steganography together, combining both algorithms to achieve high levels of data security. Furthermore, data compression is an integral part of information technology in our lives, as compression algorithms have made multimedia (text, images, audio files, and video) available for publishing on websites. We proposed first compressing the text data to reduce size and cost, then using a random key generator to encrypt the data and hide it in a colored image cover. The method was tested using texts of different lengths and also applied to different colored cover images, and the work was evaluated using PSNR. The time required for encryption and decryption was also evaluated to measure the effectiveness of the recommended methodology.

M proteins perform a significant role in Streptococcus pyogenes pathogenicity, which is considered one of the causative agents of numerous human diseases. This protein contributes to enhancing the ability to cause infections through multiple mechanisms, including adhesion to host cells and evasion of the immune system. This research highlights the role of M protein as a potential therapeutic target by exploring its associated biological mechanisms. The findings suggest that targeting this protein may contribute to the development of new vaccines and treatments against infections.

The mixed symmetry states property (MSS) for even-even 138-144 and 148 Gd isotopes was studied using IBM-2. This model discovered and emphasised various empirically questionable states and provided good agreement for the excited energy levels for the energy bands ground, g-band and acceptable for the excitations – β and γ bands. The effect of the Majorana parameter was studied as a function of changing the mixing symmetry of the levels using three different values (0.005, 0.02, and 0.04), and the value of the Majorana parameter was almost constant. By increasing the Majorana parameter to the values (0.02 and 0.04), the mixing levels began to appear clearly, especially the levels. Energy levels and energy ratios showed that 138Gd and 140Gd are between rotational SU(3) and gamma-unstable O(6) symmetries, and 142Gd, 144Gd, and 148Gd are transitional nuclei among gamma-unstable O(6) to vibrational SU(5) symmetries. The results of theoretical and practical energy levels were in good agreement.

In this study, a series of azo derivatives incorporating a 1,2,3- triazole moiety were synthesized and evaluated for biological activity. The synthetic pathway began with N,N-diaminodiphenylmethane, which was diazotized using sodium nitrite (NaNO₂) and hydrochloric acid, by coupling with 1-methyl-1H-1,2,3-triazole-4-carbaldehyde in the presence of sodium hydroxide (NaOH) to yield the azo compound. This azo compound was then reacted with p-aminoacetophenone in ethanol, using NaOH as a catalyst, to form a chalcone derivative. Subsequent reactions were carried out under reflux conditions with urea (in basic medium), phenylhydrazine (using a little of glacial acetic acid as catalyst), and hydrazinium hydroxide to obtain Oxazine and pyrazole derivatives, respectively. Structural confirmation of the prepared compounds was thorough using FT-IR, ¹³C-NMR, and ¹H-NMR spectroscopy. Biological screening revealed that all synthesized compounds exhibited promising antimicrobial activity.

The risk of Distributed Denial-of-Service (DDoS) attacks on smart home systems is increasing due to the advanced nature of network complexity and the rise in the application of encrypted information. To improve DDoS detection, this research suggests using a new framework that combines an Enhanced Particle Swarm Optimization (EPSO) with a Recurrent Transformer Network (RTN). The collection and examination of network data is done at the packet level as well as the flow level. Through analyzing this information, the EPSO algorithm can detect important patterns even when they are in low volume, while the RTN can track temporal patterns even in encrypted communication. Experimental evaluations demonstrate the framework’s superiority over traditional methods, achieving higher accuracy, reduced false alarms, and improved smart home security against DDoS threats. Where we achieved an accuracy of 98%, recall of 99%, precision of 96%, F1-score of 97%, and an AUC of 99%.

Forecasting outcomes of any system is essential for a better understanding and optimal management of the fluxes occurring in system operations. Machine Learning (ML) approaches can solve complex relationships among collected data that are hard to describe using forecasting models. This paper aims to give an overview of many described prediction methodologies that use Artificial Neural Networks (ANN) and Support Vector Regression (SVR) under the diversity of the dataset and understand the performance of each method. To improve the forecasting performance, the author proposed depending on some performance indicators, such as The Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination R2. Concludes that SVR generally outperforms ANN in forecasting of groundwater quality, drought indices, oil production, and illuminance prediction. The ANN shows better performance in certain scenarios, such as predicting wheat moisture content, solar energy, and monthly streamflow.

Asymptomatic urinary tract infections (UTIs), also referred to as asymptomatic bacteriuria, are defined by the presence of bacteria in the urinary tract in the absence of overt symptoms. This condition, which is particularly common among female college students, can result in severe infections if left untreated and contribute to antimicrobial resistance when mismanaged. The objective of the investigation is to investigate the prevalence, risk factors, and bacterial etiology of asymptomatic UTIs among students at Middle Technical University in Baghdad, Iraq. Methods: A descriptive cross-sectional study was conducted from February 2023 to May 2024, with 100 students, 49, 51 were males and female respectively within the age range of 18 to 24 years. selected through stratified random sampling. Self-administered questionnaires were utilised to collect data regarding personal history, health practices, and dietary patterns. MacConkey, CLED, and blood agar were employed to culture midstream urine samples, and standard biochemical assays were employed to identify microorganisms. In order to identify bacterial antibodies in serum samples, ELISA was implemented. The statistical analysis identified correlations between UTI positivity and demographic, behavioural, and clinical variables. Results: Asymptomatic bacteriuria was prevalent in 15% of the population, with a higher incidence in females (21.6%) than in males (8.2%). Staphylococcus aureus (33.3%) was the second most frequently identified pathogen, following Escherichia coli (46.7%). On-campus housing, inadequate hand hygiene, holding urine, low water intake, and prior UTI history were found to be risk factors substantially associated with UTI positivity (p [] 0.001). The diagnostic accuracy of ELISA and culture was exceptional, with 100% sensitivity and specificity. Conclusion: This study indicates that students at Middle Technical University, particularly females, have a significant prevalence of asymptomatic urinary tract infections (UTIs). The study identifies inadequate hydration, poor hygiene practices, prolonged urine retention, and campus living conditions as specific risk factors that elevate the likelihood of infection. The necessity for targeted preventive strategies is underscored by the recent emergence of Escherichia coli and Staphylococcus aureus as the predominant uropathogens. The study shown that ELISA and urine culture are very accurate diagnostic methods, which is advantageous as asymptomatic UTIs can be challenging to detect. Regular screening of high-risk people is essential, since untreated asymptomatic bacteriuria may result in symptomatic infections and antibiotic resistance. To diminish the incidence of infections, it is essential to promote educational initiatives that underscore the need of appropriate cleanliness, enough hydration, and prudent antibiotic usage. To enhance treatment and preventative strategies, future research should assess health outcomes over extended durations and analyse bigger population samples.

As technology advances, cybercriminals adopt more sophisticated strategies to attack weaknesses in individual computers, organisational networks, and nation-states. Organisations systematically gather substantial quantities of security-relevant data, including log events from individuals, networks, and software applications, for further forensic analysis. Conventional security analysis methods are inadequate for handling huge data volumes and may generate excessive false alarms, particularly when organisations transition to cloud architectures and accumulate more data. Furthermore, the identification of current and more complex assaults, such as persistent and advanced threats (APTs), requires ongoing monitoring and analysis of extensive security-related data, with precision and speed. Big Data analytics is actively used in several domains, including financial transactions, healthcare, and industrial applications, among others. It has recently garnered the interest of the information security community because to its purported capability to correlate security-related data and derive insights effectively at an unprecedented scale. In this study, we examine the limitations of conventional technology/systems and SIEM tools in handling massive amounts of data and complex, advanced threats. We further examine the prerequisites for the effective use of Big Data analytics in the domains of cyber threat intelligence and cybersecurity to address extensive data volumes and complex threats. Ultimately, we emphasise the issues arising from this adoption and provide solutions to address these challenges in future study.

This paper describes the creation of three near-infrared (NIR) acceptors, namely BCDT, BCDT-4F, and BCDT-4Cl, with the A−D−C−D−A (acceptor−donor−core−donor−acceptor)-type conformation. The acceptors use intramolecular noncovalent interactions to ensure the conservation of the molecular shape of the unfused complexes. Quantifications of EHOMO, ELUMO, Eg, λmax, Ex, and ESP have been calculated to illustrate the optoelectronic and structure-related properties. In the gas phase, the non-fullerene acceptor BCDT-4Cl has shown the lowest band gap energy of 1.87 eV and the lowest excitation energy of 1.66 eV. Thus, it is anticipated that BCDT-4Cl will emerge as a very viable candidate for future organic non-fullerene acceptors. We have calculated the dipole moments of the specified acceptors for both the ground state (μg) and the excited state (μe). Notably, BCDT-4Cl had the highest dipole moment values, namely μg = 5.66 and μe = 6.86 Debye. The predicted λmax value of 734 nm obtained by Density Functional Theory (DFT) for the reference molecule is consistent with the actual λmax value of 784 nm. The synthesised compounds have acute sensitivity in the near-infrared region and show a red shift in the absorption spectra, therefore covering a broader spectrum of wavelengths ranging from 730 to 7743 nm.