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The Ni1−x CuxFe2O4 (where x = 0, 0.05, 0.10, 0.15) nano ferrite powder was synthesized through chemical co-precipitation method, NaOH and acid oleic as raw materials. The XRD patterns confirmed the spinal structure phase purity of materials. XRD results showed that lattice parameter decreases with the increase of copper concentration by increasing copper concentration in the parent material. Scanning electron microscopy (SEM) was used to determine the morphology and particle size. SEM analysis indicated that all the samples are in nano size and homogeneous. AC electrical properties of nanoparticles were investigated by employing impedance spectroscopy. The real and the imaginary parts of impedance, permittivity, modulus along with the real part of ac conductivity, and tan delta were measured and analyzed for all synthesized samples in 1 Hz to 7 MHz for different voltages at 300 K.

Systemic sclerosis (SSc) is a heterogeneous disease with a propensity to involve multiple organ systems. There is a significant proportion of these patients with interstitial lung disease (ILD) who are at risk of mortality and morbidity. There are limited available tools to assess the severity of parenchymal lung involvement and are subject to confounding factors, including the presence of pulmonary hypertension and concomitant smoking history. The diagnostic tools include careful clinical history, examination, thoracic imaging, and pulmonary function tests. One of the limitations of assessing disease severity in SSc-ILD is the lack of standardized definitions for disease activity and serum biomarkers to predict future progression. Although there has been significant progress in managing SSc-related ILD over the last couple of decades with a few randomized double-blind clinical trials assessing the role of immunosuppression (mainly Cyclophosphamide and Mycophenolate Mofetil), the efficacy of these therapies is at best modest and is associated with significant toxicities. Furthermore, Nintedanib has shown promise in reducing forced vital capacity decline in SSc-ILD and in progressive fibrotic-ILD of a range of etiologies. Data are emerging for therapies like Rituximab and Tocilizumab, and we are likely to see further evidence of similar drugs being efficacious in this disease cohort. A relatively simplified algorithm is proposed in this review to guide clinicians dealing with ILD and SSc. It is imperative that clinicians take a multi-disciplinary approach to managing this complex disease in a changing therapeutic landscape.

As technology and many users develop, communication between institutions and society needs to be humane and communicate directly with the public. One of the most prominent modern technologies for communication in advertising design and digital marketing is the visual elements embodied in emojis (emojis), memes (memes) and asylum for influencers, the visual elements have a powerful influence by connecting society on different platforms, It reflects real feelings without direct contact status, expressing people's condition and helping to build virtual communication status between others and becoming a reflection of the trending state of any event. Its use in marketing has become an expressive force of the product and brand in the absence of direct communication and its spread has helped the incursion of social platforms and the use of many applications. The research aims to study the visual elements in the communication process as a means of expressing the trend and its influence in marketing for digital advertising and to provide some graphic and appropriate design solutions for digital design with the power of visual elements. The problem of research is to study the spread of visual elements and how to employ visual elements as an expression of tendency in digital advertising design, Research limits on digital advertising in the period 2018- 2023. The research follows the descriptive curriculum followed by analytical study and then the designs of the researcher's work.

This paper introduces OVANet, a new deep learning framework for the diagnosis and classification of ovarian cancer subtypes from histopathological images. OVANet has been designed by integrating modified VGG19 and InceptionV3 blocks, incorporating dual attention mechanism (Squeeze-and-Excitation and custom spatial attention) to capture rich, multi-scale, and context-aware feature representations. This unique architecture combines intermediate layers from both base models, along with additional convolutional and max-pooling layers, dual attention mechanisms and global average pooling to extract diverse image features crucial for accurate subtype differentiation. OVANet is trained and evaluated on an original dataset of 508 histopathological images, augmented to approximately 13,024 images through preprocessing and augmentation techniques like rotations, shifts, zooms, and flips. This approach significantly enhances the generalization capabilities of the model, enabling it to achieve superior performance compared to well-known pretrained models and existing research outputs. The propose model shows accuracy, precision and AUC of 99.01% along with a recall of 99.87%. Using feature map visualization of all layers of the model and a model explainibility technique GRADCam, visual representation of classification have been shown which will help to establish transparency in clinical aspect. This architecture can be considered as a significant advancement in medical imaging, especially for the diagnosis and treatment planning of ovarian cancer. To reduce the death rate of cancer, proper detection is very essential and this model exhibits so promising results that can easily implemented clinically.

Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. For this work, a dataset of 6982 MRI images from the IEEE Data Port was considered, in which a total of 5712 images of four classes (1321 glioma, 1339 meningioma, 1595 no tumor, and 1457 pituitary) were used in the training set and a total of 1270 images of the same four classes were used in the testing set. A Wasserstein Generative Adversarial Network was implemented to generate synthetic images to address class imbalance, resulting in a balanced and consistent dataset. A comparison was conducted between various data augmentation metholodogies demonstrating that Wasserstein Generative Adversarial Network-augmented results perform excellently over traditional augmentation (such as rotation, shift, zoom, etc.) and no augmentation. Additionally, a Gaussian filter and normalization were applied during preprocessing to reduce noise, highlighting its superior accuracy and edge preservation by comparing its performance to Median and Bilateral filters. The classifier model combines parallel feature extraction from modified InceptionV3 and VGG19 followed by custom attention mechanisms for effectively capturing the characteristics of each tumor type. The model was trained for 64 epochs using model checkpoints to save the best-performing model based on validation accuracy and learning rate adjustments. The model achieved a 99.61% accuracy rate on the testing set, with precision, recall, AUC, and loss of 0.9960, 0.9960, 0.0153, and 0.9999, respectively. The proposed architecture’s explainability has been enhanced by t-SNE plots, which show unique tumor clusters, and Grad-CAM representations, which highlight crucial areas in MRI scans. This research showcases an explainable and robust approach for correctly classifying four brain tumor types, combining WGAN-augmented data with advanced deep learning models in feature extraction. The framework effectively manages class imbalance and integrates a custom attention mechanism, outperforming other models, thereby improving diagnostic accuracy and reliability in clinical settings.

Rationale: Nonspecific interstitial pneumonia (NSIP) is a subtype of interstitial lung disease which can either be idiopathic or secondary to other conditions. Idiopathic NSIP is a relatively rare entity and diagnosis should be considered carefully as it is mainly a diagnosis of exclusion. The aim of this retrospective study was to evaluate a cohort of NSIP patients with a view to identifying any clinical and mortality differences between idiopathic and secondary varieties. Methods: We screened 700 patients from our interstitial lung disease database and identified 44 cases of NSIP retrospectively. Statistical analysis was conducted to evaluate if there was a difference in demographics such as gender and ethnicity, physiological parameters including forced vital capacity, diffusing capacity, average oxygen saturations, and immunology profile between two groups. Furthermore, a difference in mortality was evaluated between idiopathic and secondary NSIP. Results: The data analysis showed that 63.6% (28 of 44) of patients had idiopathic NSIP versus 36.4% (16 of 44) of patients had secondary NSIP. Majority of the secondary NSIP patients had an underlying connective tissue disease. In the idiopathic variety, there was a male preponderance (64.2%, P = .02) which was statistically different compared to relatively equal gender divide in secondary NSIP which was statistically insignificant (male vs. female: 43.8% vs. 56.3%, respectively, P = .42). The mean age of the idiopathic group was 74 years compared to 64 years in the secondary group which was statistically different (P = .01). In both groups (idiopathic and secondary NSIP), more than two-thirds (68%) were of White British ethnicity. Immunology profile was similar across both groups with no statistical difference in IgM, IgG, or IgA levels. At the time of analysis, there were 17.9% deaths (5 of 28) in the idiopathic NSIP group versus 6.3% (1 of 16) in the secondary NSIP group but this was not statistically significant (P = .14). Similarly, there was no statistically significant difference in the forced vital capacity (P = .59), diffusing capacity (P = .88), and resting oxygen saturations (P = .28) between idiopathic and secondary NSIP varieties. Conclusion: Our analysis showed that there was a statistically significant difference in gender (male preponderance in idiopathic NSIP only) and mean age difference among both varieties. There were no statistically significant differences in the clinical features and outcomes including mortality, physiological, and immunological parameters between idiopathic and secondary NSIP. Idiopathic NSIP was more common than secondary NSIP and secondary NSIP is mostly due to underlying connective tissue disease.

Background Nintedanib slows progression of lung function decline in patients with progressive fibrosing (PF) interstitial lung disease (ILD) and was recommended for this indication within the United Kingdom (UK) National Health Service in Scotland in June 2021 and in England, Wales and Northern Ireland in November 2021. To date, there has been no national evaluation of the use of nintedanib for PF-ILD in a real-world setting. Methods 26 UK centres were invited to take part in a national service evaluation between 17 November 2021 and 30 September 2022. Summary data regarding underlying diagnosis, pulmonary function tests, diagnostic criteria, radiological appearance, concurrent immunosuppressive therapy and drug tolerability were collected via electronic survey. Results 24 UK prescribing centres responded to the service evaluation invitation. Between 17 November 2021 and 30 September 2022, 1120 patients received a multidisciplinary team recommendation to commence nintedanib for PF-ILD. The most common underlying diagnoses were hypersensitivity pneumonitis (298 out of 1120, 26.6%), connective tissue disease associated ILD (197 out of 1120, 17.6%), rheumatoid arthritis associated ILD (180 out of 1120, 16.0%), idiopathic nonspecific interstitial pneumonia (125 out of 1120, 11.1%) and unclassifiable ILD (100 out of 1120, 8.9%). Of these, 54.4% (609 out of 1120) were receiving concomitant corticosteroids, 355 (31.7%) out of 1120 were receiving concomitant mycophenolate mofetil and 340 (30.3%) out of 1120 were receiving another immunosuppressive/modulatory therapy. Radiological progression of ILD combined with worsening respiratory symptoms was the most common reason for the diagnosis of PF-ILD. Conclusion We have demonstrated the use of nintedanib for the treatment of PF-ILD across a broad range of underlying conditions. Nintedanib is frequently co-prescribed alongside immunosuppressive and immunomodulatory therapy. The use of nintedanib for the treatment of PF-ILD has demonstrated acceptable tolerability in a real-world setting.

يتناول البحث مدخل الحرم الجامعي الذكي "Smart Campus" كأحد التوجهات الحديثة في مجال التخطيط والتصميم العمراني الذي يعتمد على فكرة التصميم المتكامل ذات الأبعاد المتنوعة لفراغات الحرم الجامعي الذكي. يهدف هذا المدخل الى زيادة كفاءة وجودة العملية التعليمية من خلال تحسين الفراغات الجامعية وتحويلها من فراغات انتقالية الى فراغات ذكية تفاعلية تعمل كامتداد خارجي للمباني بما تحتويه من أنشطة علمية، وبحثية، واجتماعية، وترفيهية. ومن ثم يهدف البحث الى اقتراح إطار لأسس تصميم فراغات الحرم الجامعي الذكي، كأداة يمكن من خلالها تحويل الجامعات التقليدية الى جامعات ذكية؛ من أجل حل المشاكل التي يعاني منها الحرم الجامعي التقليدي التي تؤثر بالسلب على جودة العملية التعليمية على مستوى العالم وبالأخص الحالة المصرية. وبالتالي مواكبة التغيرات التكنولوجية السريعة التي تحدث في العالم ودخول الجامعات المصرية عصر الجامعات الذكية. اعتمد البحث على المنهج النظري التحليلي من خلال عمل تحليل مقارن بين الأبعاد والأسس التصميمية للحرم الجامعي التقليدي والحرم الجامعي الذكي، من أجل التوصل إلى الإطار المبدئي المقترح لأسس تصميم فراغات الحرم الجامعي الذكي، ثم تم تدقيق واختبار صلاحية تطبيق الإطار المقترح على ثلاث تجارب عالمية. بالإضافة إلى تطبيقه على حالتين دراسيتين من الجامعات المصرية; جامعة القاهرة، الجامعة الأمريكية بالقاهرة، وتم عمل استبيان مع مجموعة من الخبراء العمرانيين المتخصصين في مجال التصميم العمراني. ومن ثم التوصل إلى الإطار المقترح لأسس تصميم فراغات الحرم الجامعي المصري الذكي. This paper tackles “Smart campus” concept as a new approach for urban design and urban planning. This concept combines multiple interrelated aspects. These aspects have an immense reflection on the efficiency of smart campus open spaces. Adding to that, it also eases the transition from traditional transition open spaces to smart interactive outdoor spaces. This allows it to perform as an extension of indoor educational spaces. Such as scientific activities, research activities, and leisure activities. This paper aims to design a framework for smart campus spaces guidelines, as a tool for transforming traditional campuses into smart campuses. This is performed to solve the problems facing the traditional campus worldwide and in Egypt particularly. Implementing framework affects the efficiency and quality of the educational process significantly. Analytical theoretical approach is adopted in this paper to perform a comparative analysis between the dimensions and design guidelines of the traditional and smart campus. 1- Reach a proposed initial framework for smart campus spaces guidelines. 2- Applying the initial Framework to (3) a global case study to Test the validity of applying it. 3- Applying Framework to (2) national case studies from Egyptian universities, Cairo University (CU), and American University in Cairo (AUC). 4- Conduct a survey to be presented to a group of experts in the field of urban design to check the final framework and the results of the research. Finally, reach a proposed framework for smart campus spaces guidelines.