Qusay Shihab Hamad

This work presents a compact ultra-wideband (UWB) Vivaldi tapered slot antenna for short-range vehicular communication and broadband UWB applications. The proposed design is developed using two simple performance-enhancement and size-reduction techniques: a radial-to-radial microstrip-to-slot transition and an impedance-matching transformer. The antenna employing only the radial-to-radial transition is referred to as VTSAR, while the final design incorporating both the radial-to-radial transition and the matching transformer is referred to as VTSARMT. Unlike many compact UWB antenna designs that rely on complex structural loading, parasitic elements, or additional decoupling components, the proposed approach improves compactness, impedance bandwidth, and gain through a simple feeding-transition modification supported by systematic parametric analysis. The intermediate VTSAR provides a 9.84% footprint-area reduction relative to the reference VTSA, while the final VTSARMT further increases the footprint-area reduction to 16.67%. The fabricated VTSARMT exhibits measured impedance matching below −10.61 dB from 2.58 to 22.34 GHz, corresponding to a 47.67% measured bandwidth improvement relative to the reference VTSA. The maximum measured realized gain increases from 6.51 dBi for the reference VTSA to 7.45 dBi for the VTSARMT, while the measured total radiation efficiency ranges from 47.7% to 94.08%. An approximately stable measured group delay of 1 ns is obtained across most of the operating band. To assess the effects of vehicle integration, the proposed antenna is further evaluated through CST-based stationary rooftop-mounted two-vehicle and three-vehicle configurations. The analysis includes radiation-pattern assessment, transmission coefficients, and CST-based received-power evaluation. These simulations provide a comparative assessment of stationary directional links rather than experimental validation of a dynamic V2V channel. The CST-based vehicle analysis provides a preliminary assessment of rooftop integration and stationary directional V2V links under controlled simulation conditions.

The integration of computational thinking (CT) into secondary STEM education remains challenging because many teachers lack structured pedagogical guidance for embedding CT practices into classroom instruction. Although previous studies have explored CT interventions and programming activities, limited research has focused on developing and validating pedagogical modules that systematically support teachers in integrating CT into STEM teaching. Therefore, this study aimed to develop and validate the Computational Thinking Pedagogical Module (McodE) for secondary STEM teachers. The study adopted the module drafting phase of the Sidek Module Development Model and employed the Fuzzy Delphi Method (FDM) to establish expert consensus on the proposed module components. Fifteen experts with expertise in STEM education, computational thinking, educational technology, curriculum development, pedagogy, and Magnetcode programming participated in the validation process. The proposed module was developed from a comprehensive literature review and comprised six pedagogical constructs: module objectives, instructional content, pedagogical strategies, teacher activities, technology integration through the Magnetcode application, and learning spaces. The expert evaluation was conducted using a seven-point linguistic scale, with responses analyzed through Triangular Fuzzy Numbers, threshold values, expert consensus, and defuzzification. The findings indicated that all six constructs and 46 proposed module items satisfied the FDM acceptance criteria (threshold value ≤ 0.20, expert consensus ≥ 75%, and defuzzification value ≥ 0.50), demonstrating a high level of agreement regarding their relevance and appropriateness. The validated McodE provides a systematic pedagogical framework to support secondary STEM teachers in integrating CT into STEM teaching. The findings contribute to the development of teacher-focused instructional modules and provide a validated foundation for future implementation and empirical evaluation in secondary STEM classrooms.

Wrist fractures are one of the most frequent fractures in children that should be diagnosed properly and immediately to avoid any further complications. Regular radiographic analysis is time-consuming, relies on qualified radiologists, and is subject to human error. There has been a lot of buzz about the potential of deep learning technologies to automate medical image analysis in recent times. The algorithms in the YOLO series are sophisticated and refined, representing some of the best work in the field. The GRAZPEDWRI-DX pediatric wrist dataset is used in this study to address the variation in the YOLOv11 detection model’s inference time, recall, precision, and mean average precision (mAP). A medium-scaled variant of YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10 models were compared with YOLOv11-m. According to the experimental results, YOLOv11-m achieves the highest mAP@50–95 (0.569) for fracture detection, whereas YOLOv11-x attains the highest mAP@50–95 (0.424) across all category classes. An ablation study of YOLOv11 architectural elements was done to determine how the model optimized for medical imaging through the attention mechanism. On the other hand, the performance in fracture detection and inference time for YOLOv11 is superior to that of previous YOLO models, while it has lower computational complexity. Additionally, a performance comparison among recent YOLO-based models and the RT-DETR and Faster R-CNN models reveals variations in detection precision, recall, and accuracy, providing insight into the strengths and trade-offs of each approach. YOLOv11 could therefore make recurrent contributions and be of great value to clinical decision-making. Received: 18 August 2025 | Revised: 2 February 2026 | Accepted: 25 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://doi.org/10.1038/s41597-022-01328-z, reference number [15]. Author Contribution Statement Muhanad Abdul Elah Alkhalisy: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Qusay Shihab Hamad: Validation, Investigation, Writing – review & editing, Visualization, Supervision, Project administration. Ali Retha Hasoon Khayeat: Validation, Investigation, Writing – review & editing. Shahrel Azmin Suandi: Writing – review & editing, Supervision.

Climate change, workforce shortages, and sustainability requirements create serious obstacles for oil palm farms, which contribute $65 billion annually. Automated monitoring solutions are very important because traditional manual tree counting techniques, which are widely used in the sector, have counting error rates of 15-25% and require significant human resources. In this comprehensive review, deep learning applications, specifically, convolutional neural networks (CNNs) for oil palm tree detection and counting are evaluated. Performance, constraints, and realistic deployment pathways are examined. Publications from 2016 to 2025 that focused on oil palm recognition using deep learning with quantitative measurements were found using a literature search across Scopus, Web of Science, IEEE Xplore, and Google Scholar. Architectures, dataset properties, and performance metrics were recorded using data extraction. Malaysia is at the forefront of cooperative networks that span 22 nations, according to an analysis of 47 datasets. With mature trees, modern CNN architectures improved with YOLO frameworks achieve >95% detection accuracy; nevertheless, for young trees, they show notable degradation (87.2% vs. 96.8% mAP). Cross-regional generalization (21.9 percentage point accuracy degradation), processing demands (450-650 ms inference), and financial obstacles are important obstacles. Real-time viability is demonstrated by edge-optimized models, which achieve 98.6\% accuracy with 80 ms inference. Geographic bias (68% Malaysian, 23% Indonesian) and restricted public availability (8%) are revealed by dataset analysis. Deep learning can significantly improve oil palm management by 15-20% compared to conventional techniques. Widespread adoption requires standardized benchmark datasets (10,000+ images), transfer learning techniques (<500 images per region), edge-optimized architectures (<100 ms inference), and phased deployment (10-50 hectare pilots). CNN's convergence with precision agriculture positions the industry for comprehensive digitalization while addressing sustainability and labor challenges.