Hadhramout University

In this study, a novel nanocomposite based on Fe3O4, nickel acetate, and the organic ligand Benzophenone-4,4-dicarboxylic acid (H2bpda) was synthesized and thoroughly characterized. The coexistence of the magnetic phase and the Ni-H2bpda complex resulted in a stable, recoverable material exhibiting high efficiency in removing tetracycline from aqueous solutions. Key operational parameters, including adsorbent dosage, initial pollutant concentration, pH, temperature, and contact time, were systematically investigated to determine the optimal adsorption conditions. Kinetic analysis indicated that the adsorption process followed a pseudo-second-order model, while isotherm studies showed a better fit with the Langmuir model, suggesting uniform adsorption sites on the nanocomposite surface. Beyond its high tetracycline adsorption capacity, the synthesized nano-composite demonstrated significant antibacterial activity against a wide range of Gram-positive and Gramnegative standard strains, wastewater-isolated bacteria, and pathogenic aquatic microorganisms. This behavior highlights the synergistic effect between the inorganic and organic Ni-based components in generating an active surface capable of inhibiting microbial growth. The combination of effective adsorption, reusability, and broad-spectrum antibacterial properties makes the Ni-H2bpda/Fe3O4 nanocomposite a promising candidate for water purification and microbial contamination control.

Pyrene-based chalcones (PyChs) have emerged as promising potential for nonlinear optical (NLO) applications, due to their extended It-conjugation, efficient intramolecular charge transfer (ICT), and tenable electronic properties. These materials exhibit significant third-order NLO responses which making them suitable for optoelectronic applications. The lack of an integrated understanding of how molecular structure and electronic properties affect the NLO response of PyChs limits their rational optimization. Although many compounds show promising third-order NLO properties, the absence of clear structure-property relationships and strong links between experimental and computational results hinders predictive design and practical development. This review aims to critically analyse the synthesis strategies, structural characterization, and NLO properties of PyChs. Particular emphasis is placed on the factors affecting the optical properties of these compounds. Additionally, to correlate molecular structural features with experimentally measured third-order NLO responses and computationally derived electronic parameters to establish clear structure-property relationships and provide guidance for the rational design of high-performance PyCh-based NLO materials. The synthesis of PyChs is discussed with emphasis on the Claisen-Schmidt condensation mechanism followed by structural analysis using single-crystal Xray diffraction (SCXRD) and Fourier-transform infrared spectroscopy (FTIR). Their optical and electronic behaviours are further explored through computational approaches, including frontier molecular orbital (FMO) analysis, natural bond orbital (NBO) theory, excited-state electronic transitions, and the evaluation of polarizability and hyperpolarizability via density functional theory (DFT). Emphasis is placed on structure-property relationships governing NLO activity, particularly the roles of molecular planarity and donor-acceptor interactions. Additionally, third-order NLO responses are analysed using the Z-scan technique and correlated with computational insights. Overall, this review highlights PyChs as promising materials for potential applications in photonic technologies and opens up future research in high-performance NLO applications.

Refactoring is widely used to improve internal software quality; however, its impact on external functionality remains insufficiently explored. This study investigates how different refactoring operations influence software functionality through a controlled experimental analysis. A set of ten commonly used refactoring operations was applied to the jEdit system. Software functionality was quantitatively evaluated using a composite metric derived from cohesion, polymorphism, interface size, design size, and inheritance hierarchy. The selected ten refactoring operations were performed 453 times across ten independent experiments in jEdit. The results reveal that refactoring operations do not have uniform effects on functionality. Operations that enhance encapsulation and modular distribution significantly improve functionality, with Encapsulate Field achieving the highest increase. In contrast, operations that reduce abstraction, such as Inline Method and Inline Class, negatively impact functionality. Additionally, some operations show no measurable effect, indicating limitations in metric sensitivity. These findings demonstrate that refactoring should not be assumed to universally improve software functionality. Instead, its impact depends on the nature and context of the operations being applied. The study provides empirical evidence and practical guidance for selecting refactoring operations that effectively enhance functional quality while avoiding potential degradation.

Brain tumors remain among the most dangerous life-threatening neurological disorders, and their accurate identification, segmentation, and classification are critical for improving treatment outcomes. Traditional cancer detection methods are often labor-intensive and susceptible to human error, whereas an automated solution is proposed to foster early diagnosis and reduce inaccuracies. This study presents FCDS-DeepVision, a lightweight convolutional neural network built from scratch to classify brain disorders using magnetic resonance imaging scans. The preprocessing stage applies a combination of intensity normalization, noise suppression, and contrast enhancement to improve the visibility of diagnostically relevant structures within the magnetic resonance imaging scans. To further reduce background interference, a segmentation step is introduced to localize anatomically meaningful brain regions prior to classification. This localized representation supports a more reliable separation of normal tissue from pathological patterns associated with glioma, meningioma, and pituitary tumors. The proposed framework is evaluated using a publicly available dataset of 7,020 brain magnetic resonance imaging scans collected from Kaggle. Experimental results indicate that the model maintains fast inference while producing stable and reproducible predictions across multiple evaluation metrics, including precision, recall, F1-score, Area Under the Curve, and overall accuracy. Rather than relying on a single performance indicator, the analysis emphasizes consistency across metrics under controlled experimental settings. The results suggest that such an automated pipeline can assist diagnostic workflows by reducing manual effort and limiting variability introduced through subjective interpretation. While the current findings are encouraging, further investigation is required to assess robustness under broader data distributions, larger cohorts, and real clinical acquisition conditions. Extending the dataset and validating the framework in practical diagnostic environments remain important directions for future work.

Alzheimer’s disease (AD) represents an important and growing global health challenge, characterized by progressive neurodegeneration and cognitive decline. Early and accurate diagnosis, especially distinguishing between different stages such as cognitively normal (CN), mild cognitive impairment (MCI), and AD, is crucial for timely intervention, patient management, and the development of effective therapies. However, current diagnostic paradigms, relying on clinical assessments, neuropsychological tests, and conventional neuroimaging analysis, often suffer from limitations including subjectivity, high cost, limited accessibility, and insufficient sensitivity to subtle changes in the early stages or during progression. Furthermore, many existing automated approaches focus on binary classification (AD vs. CN), neglecting the critical intermediate MCI stage and the temporal dynamics of disease progression. This paper introduces a novel, automated, multi-stage AD classification framework leveraging enhanced deep learning techniques applied to structural Magnetic Resonance Imaging (MRI) data. Our integrated approach addresses the shortcomings of previous methods by combining an improved ResNet50 architecture for enhanced feature extraction, a Bidirectional Gated Recurrent Unit (Bi-GRU) to model temporal disease progression from sequential MRI scans, and a faster R-CNN framework with Soft Non-Maximum Suppression (Soft-NMS). This final component provides refined localization of AD-related neuropathological changes by learning to identify these pathological regions implicitly, without requiring any manual annotations, thereby mitigating issues with overlapping region proposals. Comprehensive experimental validation was performed on the widely used Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, using 25,024 structural MRI scans from 6,100 unique participants encompassing subjects with CN, MCI, and AD. The proposed framework demonstrated exceptional performance, achieving state-of-the-art accuracy in distinguishing AD from CN at 98.2