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This research aims to design three educational programmes based on blended learning delivery to students attending the "introduction to educational technology" course. Each programme differs in its blending proportion between traditional and e-learning. The objective is to determine the most suitable blending ratio between the two delivery formats for this course, and compare the effectiveness of the three blending levels to the traditional instruction in terms of developing students' academic achievement and their attitudes towards using blended learning at the College of Education, Sultan Qaboos University. The results show a statistically significant difference at the level of <0.05 between the mean scores of all the experimental groups and the three control groups in the post academic achievement test, in favour of the experimental groups. The research recommends using a blended learning strategy with all blending ratios in teaching and developing different learning variables, such as understanding and thinking.

Background Polytrauma from road accidents is a common cause of hospital admissions and deaths, frequently leading to acute kidney injury (AKI) and impacting patient outcomes. Methods This retrospective, single-center study included polytrauma victims with an Injury Severity Score (ISS) >25 at a tertiary healthcare center in Dubai. Results The incidence of AKI in polytrauma victims is 30.5%, associated with higher Carlson comorbidity index (P=0.021) and ISS (P=0.001). Logistic regression shows a significant relationship between ISS and AKI (odds ratio [OR], 1.191; 95% confidence interval [CI], 1.150–1.233; P

Upper extremity deep venous thrombosis (UEDVT) is uncommon but may complicate intracerebral hemorrhage (ICH). A unique challenge arises when UEDVT is diagnosed and treated in situations of ICH. Case Report: We report a 43-year-old man developed edema in his hemiplegic arm 7 days after a spontaneous ICH. UEDVT was detected immediately despite pharmacotherapy with point-of-care compression ultrasound (POCUS). In addition to controlling thrombogenic factors, the dosage of enoxaparin was increased, yet it was subtherapeutic. Following this, the thrombosed veins recanalized and the hematoma diminished as well. Conclusion: A POCUS scan helps diagnose UEDVT, and the risk and benefits of treating UEDVT should be considered in patients with ICH.

The influence of the Nd:YAG laser's oscillation energy on creating textured surfaces on silicon wafers is investigated in this study. The silicon wafer surfaces were directly ablated by a pulsed Nd:YAG laser beam to create the texturing, which was then examined using UV–Vis spectroscopy, AFM and FESEM to determine its characteristics. The reflectance was reduced up to 15% after laser texturing of multicrystalline silicon wafers. The silicon surface suffers from structural defects and a laser damage layer as a result of the laser texturing process, which in turn has an effect on the lifespan of the photo-generated carriers. To compensate for the laser damage layer, the silicon surface was cleaned with diluted KOH (15%) to remove undesirable particles and the oxide layer. Surface roughness and reflectance in silicon solar cells were changed depending on the laser oscillation energy utilized for texturing. The roughness and reflectance were measured using AFM and UV–Vis, respectively. The laser oscillation energy of 84 J/p had the highest average roughness of 0.2104 m and the lowest reflectance of 5%. As a result, standard silicon solar cell devices revealed photovoltaic conversion efficiencies of roughly 7.5% and 5.0% for laser-textured grid and one-dimensional line patterns, respectively.

The vision transformer (ViT) architecture, with its attention mechanism based on multi-head attention layers, has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information. ViTs are notably recognized for their complex architecture, which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices. This renders them more intricate than convolutional neural networks (CNNs). This difficulty is also challenging in the context of histopathology image analysis, where the images are both limited and complex. In response to these challenges, this study proposes a TokenMixer hybrid-architecture that combines the strengths of CNNs and ViTs. This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training, while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification. The TokenMixer mechanism is inspired by the ConvMixer and TokenLearner models. First, the ConvMixer model dynamically generates spatial attention maps using convolutional layers, enabling the extraction of patches from input images to minimize the number of input patches used in training. Second, the TokenLearner model extracts relevant regions from the selected input patches, tokenizes them to improve feature extraction, and trains all tokenized patches in an encoder transformer network. We evaluated the TokenMixer model on the BreakHis public dataset, comparing it with ViT-based and other state-of-the-art methods. Our approach achieved impressive results for both binary and multi-classification of breast cancer subtypes across various magnification levels (40×, 100×, 200×, 400×). The model demonstrated accuracies of 97.02% for binary classification and 93.29% for multi-classification, with decision times of 391.71 and 1173.56 s, respectively. These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images. The source code is accessible: https://github.com/abimouloud/TokenMixer. [ABSTRACT FROM AUTHOR]

In this study, we report the synthesis of iron oxide nanoparticles (FeONPs) using micro-emulsion-hydrothermal method. By adjusting the synthesis temperature, we successfully produced FeO nanorods and nanospheres. In addition, the 2-octanol, and the surfactant cetyltrimethylammonium bromide served as a solvent in the synthesis process. Using iron nitrate hexahydrate as the salt precursor allowed for the formation of FeONPs with varied sizes, shapes and phases. The synthesized materials were extensively characterized using XRD, SEM, TEM, EDS, FTIR, and XPS techniques. However, the structural analysis revealed rhombohedral (hematite), and (magnetite) crystal structures in the materials synthesized at different temperature and durations, with particles ranged in size from 12 to 97 nm. More importantly, the magnetic characterization, performed with a vibrating sample magnetometer and SQUID magnetometer, indicated that the NPs showed not clear superparamagnetic behavior. In conclusion, this work demonstrates the synthesis of FeONPs with controlled size, shape and phase using microemulsion hydrothermal technique, with detailed characterization offering valuable insights into their magnetic and structural properties. [ABSTRACT FROM AUTHOR]

Water pollution is a major concern due to its detrimental effects on the environment and public health. The particular danger of inorganic pollutants arises from their persistent toxicity and inability to biodegrade. Recently, electrocoagulation (EC) has been demonstrated as an alternative sustainable approach to purifying wastewater due to the increasingly strict pollution prevention rules. In particular, EC has been used to remove inorganic pollutants, such as Cr, Zn, Pb, or As. EC has emerged as a sustainable tool for resource recovery of some inorganic pollutants such as N and P that, when recovered, have value as plant nutrients and are critical in a circular economy. These recovered materials can be obtained from diverse agricultural drainage water and recycled as fertilizers. In this work, a state-of-the-art technique is reviewed describing the advances in contaminant removal and nutrient recovery using EC through an in-depth discussion of the factors influencing the contaminant removal process, including operating pH, time, power, and concentration. Furthermore, limitations of the EC technology are reviewed, including the high-power consumption, fast deterioration of the sacrificial electrodes, and the types of contaminants that could not be efficiently removed. Finally, new emerging constructs in EC process optimization parameters are presented. [ABSTRACT FROM AUTHOR]