Journal of Kerbala University

Due to their exceptional chemical, physical and biological properties, the synthesis of inorganic nanoparticles has received significant attention, enabling diverse applications across various industries. This review highlights research that has focused on developing efficient methods for the synthesis of inorganic nanoparticles, including various metals, metal oxides and semiconductors. In addition to elucidating different synthesis techniques, such as sol-gel processes, hydrothermal methods, and chemical reduction, the role of synthesis agents in controlling the size, shape and surface properties of nanoparticles is demonstrated. Since nanoparticles have unique electronic, optical and mechanical properties, they are suitable for applications in medicine such as cancer imaging and therapy, agriculture, energy and food industry. Therefore, a discussion of the potential applications of these nanoparticles in areas such as targeted drug delivery, biosensors, antimicrobial packaging and environmental remediation is provided. These findings contribute to the advancement and understanding of the synthesis of inorganic nanoparticles, paving the way for innovations in the field of nanotechnology.

Rabies is a deadly viral disease that affects the nervous system of both humans and animals. The causative agent belongs to the family of Rhabdoviridae and is most commonly transmitted through the saliva of infected animals, such as dogs, cats, foxes, and bats, via bites or scratches. Human-to-human transmission is extremely rare but may occur under specific circumstances. The disease manifests in two clinical forms: the furious form, characterized by aggression, excessive salivation, and hydrophobia, and the dumb [paralytic] form, where paralysis gradually develops. Transmission occurs primarily through bites or open wounds exposed to infected saliva and, less commonly, via contact with mucous membranes during handling of infected animals. Diagnosis in advanced stages relies on clinical signs, but definitive confirmation is achieved through post-mortem examination of brain tissue. Laboratory testing can also be performed on saliva or other tissue samples. Currently, there is no effective treatment once clinical symptoms appear, making prevention through vaccination the most effective strategy. Mass vaccination campaigns for animals are crucial in controlling rabies, particularly in rural areas, alongside public education programs to raise awareness about the risks of contact with potentially infected animals. The purpose of studying rabies in humans and animals is to emphasize the importance of understanding its transmission, pathogenesis, and preventive measures, given that rabies remains a fatal viral disease that can be transmitted from animals to humans

Rabies is a deadly viral disease that affects the nervous system of both humans and animals. The causative agent belongs to the family of Rhabdoviridae and is most commonly transmitted through the saliva of infected animals, such as dogs, cats, foxes, and bats, via bites or scratches. Human-to-human transmission is extremely rare but may occur under specific circumstances. The disease manifests in two clinical forms: the furious form, characterized by aggression, excessive salivation, and hydrophobia, and the dumb [paralytic] form, where paralysis gradually develops. Transmission occurs primarily through bites or open wounds exposed to infected saliva and, less commonly, via contact with mucous membranes during handling of infected animals. Diagnosis in advanced stages relies on clinical signs, but definitive confirmation is achieved through post-mortem examination of brain tissue. Laboratory testing can also be performed on saliva or other tissue samples. Currently, there is no effective treatment once clinical symptoms appear, making prevention through vaccination the most effective strategy. Mass vaccination campaigns for animals are crucial in controlling rabies, particularly in rural areas, alongside public education programs to raise awareness about the risks of contact with potentially infected animals. The purpose of studying rabies in humans and animals is to emphasize the importance of understanding its transmission, pathogenesis, and preventive measures, given that rabies remains a fatal viral disease that can be transmitted from animals to humans

In this study, the levels of selenium and manganese were evaluated in water, sediments, and fish from al-Dalmaj marshal , which is located in central Iraq. It is important for supplying fish to the provinces and is considered the main basin where agricultural runoff, rainwater, and other waters accumulate. These levels were found during measurements using an ICP-AES device (Inductively Coupled Plasma Atomic Emission Spectroscopy).Samples were collected between March and July 2024. Average values were: water — Mn 4,880.48 µg L⁻¹, Se 274.09 µg L⁻¹; fish — Mn 1,408.06 µg L⁻¹, Se 142.12 µg L⁻¹; sediment — Mn 25,587.99 µg L⁻¹, Se 232.42 µg L⁻¹. The measurements exceeded the limit allowed by the World Health Organization (WHO), which indicates environmental risks to human health. Continuous monitoring and taking necessary measures to reduce risks and limit pollution are recommended.

Early trust frameworks relied on heuristic or graph-theoretic rules (e.g., EigenTrust, TrustRank) but often suffered from brittle propagation semantics and limited adaptability to dynamic behaviors. In recent years, data-driven approaches—particularly graph neural networks (GNNs), attention-based models, and hybrid deep learning frameworks—have catalyzed significant advances in trust evaluation by learning the latent representation of trust features and propagation patterns directly from data. This review presents a comprehensive survey of network trust research, encompassing foundational definition and taxonomies, classical computational methods, machine learning and deep learning innovations (with emphasis on GNNs and attention mechanisms), evaluation protocols and datasets, key applications (social recommender systems, IoT security, blockchain, and fraud detection), as well as ongoing challenges (data sparsity dynamic adaptation, explainability, and robustness). Network trust quantifies the confidence in relationships among entities (e.g., users, devices, organizations) in interconnected systems, ranging from social networks to the Internet of Things (IoT) and peer-to-peer (P2P) environments. We conclude by outlining promising research directions, such as self-supervised trust representation learning, lifelong adaptation, explainable trust frameworks, privacy-preserving trust computation, and multimodal trust fusion, to guide future work toward robust, scalable, and interpretable trust mechanisms. This review synthesizes the current state of the art in network trust, providing researchers and practitioners with a structured roadmap of methodologies, datasets, applications, and future research directions.

Amid the global shift towards green chemistry and the growing demand to reduce the environmental impact of chemical processes, this study investigates the effectiveness of a modified natural zeolite catalyst as a sustainable alternative to conventional, toxic alkylation catalysts such as aluminum chloride. A series of controlled laboratory experiments were conducted to compare the green and traditional catalysts in terms of reaction yield, product purity, ease of catalyst separation, environmental safety, economic feasibility, and catalyst reusability.
The experimental results revealed that the green catalyst achieved a yield of 84% ± 1.2 compared to 88% ± 1.5 for aluminum chloride. The product purity was 91% ± 0.8 with the green catalyst versus 93% ± 0.6 with the conventional catalyst. Additionally, the green catalyst demonstrated excellent ease of recovery and could be reused for up to three consecutive cycles with only a 3–4% decrease in activity. Analytical techniques including Gas Chromatography–Mass Spectrometry (GC-MS), Fourier Transform Infrared Spectroscopy (FTIR), and Proton Nuclear Magnetic Resonance (¹H-NMR) confirmed the identity and purity of the reaction product, ethylbenzene.
This research highlights the practical potential of natural zeolite-based catalysts in promoting greener chemical synthesis. The findings support the adoption of green catalytic systems as viable, eco-friendly alternatives for industrial organic transformations, particularly in alkylation reactions.

This review examines the most important strategies to enhance titanium dioxide (TiO₂) as an ultraviolet (UV) filter by mitigating its photocatalytic activity in terms of suppressing the risk of harmful reactive oxygen species (ROS) (free radicals). As Zn2SnO4 has been reported recently to exhibit promising UV filter properties similar to TiO2 while producing a very low amount of ROS. This review also provides insight into and compares Zn2SnO4 and TiO2 for incorporation into sunscreen. Zn2SnO4 is potentially expected to maintain the high sun protection factor (SPF) of TiO₂, allowing for effective sunscreen formulation. To the best of our knowledge, this review summarizes for the first time the recent advancements in reducing the high photocatalytic activity of TiO2, and explores the suitability of using a composite of TiO₂@Zn2SnO4 as a promising UV filter in sunscreen

River-level estimation is a critical task required for understanding flood events, but it is often complicated by the scarcity of available data. Recent studies propose using large networks of river-camera images to estimate river levels. However, this approach currently requires significant manual intervention, including ground topographic surveys and water image annotation. In this research, we present an innovative method to ease river-level estimation from river-camera images using through machine learning algorithms. In this project, the data cleaning process is done to remove any missing or distorted features or other anomalies in the data that need to be dealt with and is considered an initial stage of data processing. Then the stage of classifying the images into 2 categories. Based on the data set of the Kerala River in India, which is a unique set that includes a set of images taken for 2018 for all months. These images were processed and converted into digital data. This dataset contains 118 rows and 16 columns, including two columns named ANNUAL and RAINFALL. The RAINFALL column is the last completed column. Data can be classified as "yes" or "no" to determine whether the images in the dataset have been processed or not. Using K-Nearest Neighbor (KNN) algorithms, Random Forest (RF) and Support vector machine (SVM). Which achieved the highest percentage of 94.7%.

This paper describes a practical speech-driven framework that lets users with motor impairments run Windows operating system commands by speaking them. The system uses the open-source Sphinx-4 engine and Java Speech Grammar Format (JSGF) to match spoken phrases to actions that have already been set up in a command dictionary. The prototype does basic things like opening folders, starting programs, and changing settings. Tests in different levels of background noise showed that the response accuracy was about 90% in quiet settings and 82% in moderate noise. All of the functions that were tested showed that the system could respond to commands in less than one second. These results show that grammar-based speech control is a lightweight and easy-to-use alternative to traditional input devices.

This paper presents the idea of a cascaded boundary mitigation scheme for Pixel Value Differencing (PVD) steganography by jointly using modulus operation and MSB preserving bit-stream adjustment to address the falling-off-boundary issue. While the behavior of the applications of modulus and bits stream adjustment has been studied separately, their interaction and their performance at large scale have never been studied together. The method was implemented in Python and tested on the full dataset of Flickr30K (31,783 real world photographs) and a fixed payload of ~0.95 bits/pixel. Across all the images the framework achieved an average PSNR of 40.2dB (with a range of 34.3-45.1 dB), perfect-block rate (successful embedding without fallback) of 94.1% (with a range of 74%-100%), and extraction accuracy of 98.2% (100% on 28,147 images). The average capacity loss due to the fallback mechanism was 6.4%. Detailed results on five representative images of different texture are provided which confirm the robustness of the method for smooth as well as highly textured content. The approach preserves the linear O(M×N) complexity and provides a reproducible baseline to compare the traditional and the AI enhanced steganography techniques.

Social media platforms like Facebook, YouTube, and Twitter have witnessed remarkable growth, and the type of data and information shared on these sites has evolved dramatically. Because users of all ages can readily access these platforms, this technological advancement has also been essential in encouraging the spread of hate speech and enhancing its impact on society. Researchers have sought to develop a range of strategies and technology models to detect and mitigate this growing threat.
Even though hate speech identification in English-language literature using Natural Language Processing (NLP) approaches has advanced significantly, research on the Arabic language, especially the Iraqi dialect, is still lacking. This research aims to identify hate speech in the Iraqi dialect by creating a database of more than 150,000 comments taken from YouTube videos about Iraqi topics that have sparked public debate. The gathered remarks were prepared and processed in several steps, including human cleaning. The comments were then divided into four major semantic classes: hate speech, abusive, offensive, and normal.
The efficiency of many machine learning models in processing texts written in the Iraqi dialect was evaluated. Graph neural networks (GNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model, Bidirectional Long Short-Term Memory networks (BiLSTM), and the FastText model were among the models. The outcomes showed that these models performed differently when it came to digesting content in the Iraqi dialect. FastText, on the other hand, recorded a performance rate of 96.1% in both the training phase and in predicting previously unseen remarks, achieving the greatest Accuracy, Precision, Recall, and F1-Score. Therefore, despite its simplicity, the FastText model offers a practical solution for classifying hate speech in different Arabic dialects.