Introduction and research problem Teamwork is essential in healthcare settings especially among nurses. Although, it’s not that easy and perhaps a challenging task to achieve effective teamwork. Effective teamwork among nurses is crucial for improving patient outcomes and enhancing the quality of care in healthcare settings. In addition, it has positive impacts on staff satisfaction and retention rates. This project aims to implement and evaluate the effectiveness of the Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) program in improving teamwork among nurses in the female medical ward at East Jeddah Hospital in Jeddah, Saudi Arabia. Material and Method TeamSTEPPS is a training program designed to enhance teamwork and collaboration in healthcare, focusing on five key skills: team structure, leadership, situation monitoring, mutual support, and communication. This study used a quasi-experimental design to implement TeamSTEPPS-based team-building activities with 38 nurses and nursing assistants in a female medical ward, aiming to evaluate their impact on teamwork and collaboration. Summary of Results The main result shows that about three quarter of subjects’ perception improved from pre to post team structure, about (60%) of subjects perception improved from pre to post leadership, about (70%) of their perception improved from pre to post situation monitoring, more than (65%) of subjects perception improved from pre to post mutual support, more than (70%) of their perception improved from pre to post communication. Conclusion and Recommendations The study shows that a structured teamwork enhancement program improves nursing teams’ efficiency, communication, safety culture, and staff well-being. By promoting shared leadership and mutual support, healthcare organizations can reduce errors, empower nurses, and enhance patient safety, aligning with Saudi Vision 2030. Sustained leadership support, continuous learning, and ongoing evaluation are essential to maintaining and expanding these improvements.
Silkworm diseases significantly impact sericulture productivity, leading to substantial economic losses worldwide. Early and accurate disease detection is therefore essential for effective disease management. This study provides a comprehensive review of research published between 2021 and 2025 on the application of machine learning (ML) and deep learning (DL) techniques for silkworm disease classification. It explores a variety of computational approaches, including conventional ML models such as Support Vector Machines (SVM) and Random Forests (RF), alongside advanced deep learning architectures, particularly Convolutional Neural Networks (CNNs). The findings indicate that while conventional ML methods achieve moderate classification accuracy (typically between 85% and 95%) using handcrafted features, DL-based models significantly outperform them, often achieving accuracy levels above 97%. Recent advancements emphasize the use of transfer learning, lightweight architectures, and real-time deployment systems to support field-level applications. Despite these improvements, several challenges remain, including limited dataset availability, lack of standardized benchmarks, and insufficient real-world validation. This study highlights the need for large-scale datasets, robust model evaluation, and integration with edge and IoT-based systems for practical implementation. Overall, the review demonstrates that deep learning-based approaches offer a promising direction for developing efficient, scalable, and intelligent diagnostic systems for silkworm disease classification.
Topological indices are numerical invariants associated with graphs that provide useful information about their structural properties. They have significant applications in chemical graph theory, network analysis, and mathematical modeling, particularly in the study of molecular structures and the prediction of physicochemical properties. In this paper, we present the fundamental concepts of the Harary, Schultz, and Gutman indices and investigate generalized sum, product, and difference-based eccentricity–Harary indices. These indices are applied to the molecular graphs of octane isomers. The basic concepts and structural characteristics of octane isomers are discussed, and the exact values of the generalized sum, product, and difference-based eccentricity–Harary indices are determined for the octane isomers. Furthermore, statistical analysis is performed using SPSS software to examine the predictive ability of the proposed topological indices with respect to selected physicochemical properties of octane isomers. Regression models are developed to identify the most effective topological indices as predictors of the considered physicochemical properties. The results highlight the applicability of the proposed generalized eccentricity–Harary indices in QSPR analysis of octane isomers.
Objective: To develop and optimize effervescent gastroretentive floating matrix tablets (EGFMT) of repaglinide using grafted gum (GG-N) of two viscosity grades (1% and 2% solutions) as the matrix-forming polymer and sodium bicarbonate as the gas-generating agent, in order to prolong gastric residence and sustain drug release. Methods: Drug identity and drug–excipient compatibility were assessed by melting point, solubility, UV spectroscopy (λmax 245 nm) and FTIR studies. Eighteen formulations (nine for each viscosity grade) were prepared by direct compression according to a 3² full factorial design, with polymer amount (X1) and sodium bicarbonate level (X2) as independent variables and floating lag time (Y1), swelling index at 1 h (Y2) and time for 100% drug release (T100, Y3) as responses. The tablets were evaluated for in vitro buoyancy, swelling, weight and content uniformity, hardness, friability and in vitro drug release in 0.1 N HCl. Release data were fitted to zero-order, first-order, Higuchi, Hixson–Crowell and Korsmeyer–Peppas models. ANOVA, response surface analysis and desirability-based optimization were performed using Design-Expert software. Results: All formulations floated within 1–18 s and remained buoyant for 4–19 h. Hardness (4–5 kg/cm²), friability (<1%), weight uniformity and drug content complied with pharmacopoeial limits. FTIR spectra showed no chemical incompatibility between repaglinide and the excipients. T100 ranged from 10 to 23 h; it increased with polymer amount and viscosity grade and decreased with increasing sodium bicarbonate. Drug release followed zero-order kinetics (r = 0.96–0.99) with a diffusion-dominated mechanism (Higuchi model), and Korsmeyer–Peppas exponents (n = 0.59–0.81) indicated anomalous (non-Fickian) transport. Both polymer amount and sodium bicarbonate significantly influenced the responses (p < 0.05). The optimized formulations (GG 2%: 15.57 mg polymer and 11.78% sodium bicarbonate; GG 1%: 10.03 mg polymer and 19.03% sodium bicarbonate) showed floating lag times of 11.68 s and 10.9 s, T100 of 17.03 h and 15.3 h, and total floating times of 12 h and 13 h, respectively. Conclusion: Grafted gum, in combination with sodium bicarbonate, is a suitable matrix system for gastroretentive floating tablets of repaglinide, and the factorial design with response surface methodology enabled an optimized formulation with rapid buoyancy and sustained release.
