Effects of Nano particles of (Al2O3, Fe2O3) as Reinforcement on ‎Mechanical Properties of Aluminum

  بحجم نانوي (200 نانومتر)  و بوزن 2.5٪   تمت اضافتها  Al2O3 و Fe2O3 الدقائق النانوية لاوكسيد  كل لغرض دراسة تأثيرها على البنية المجهرية والخواص الميكانيكية للألمنيوم النقي والمنتج عن طريق الصب  بعملية السباكة ان  تقنية النانو اصبحت الآن  تتناول طرق  جديدة لدعم ولتحسين  قوة المعادن والبلاستيك. في الآونة الأخيرة قد حظيت اكاسيد  المعادن النانوية باهتمام كبير  وذلك  لخصائصها الممتازة كالخواص  المغناطيسية والكهربائية والبصرية ومن هذه الاكاسيد   مثل  . Al2O3 و Fe2O3. فحوص البنية المجهرية والصلابة والتأكل والشد قد اجريت على العينات.  أظهرت النتائج  ان إضافة الدقائق النانوية كان لها تأثير كبير على الخصائص مقارنة بخصائص الألومنيوم النقي على سبيل المثال ، زادت الصلابة بنسبة (20 ، 53)٪ ، وفقد التآكل الحجمي بنسبة (27.7 ، 69.7)٪. بالإضافة  على ذلك ، زيادة في UTS  إلى (82 ، 254) ٪ وكانت اعلى قيمة عند  (52 ، 107) ٪  وقد اثبتت هذه النتائج عن تحسن كبير في البنية المجهرية  نتيجة لا ضافة الدقائق النانوية .Al2O3 and Fe2O3 has been added as a nanoparticles (200 nm) 2.5 wt.%, to study their effects on microstructure and mechanical properties of pure Al by casting. Nanotechnology is now taking new processes to enhance the strength of metals and plastics. Lately, the remarkable magnetic, electric, optical, and catalytic capabilities of metal oxide nanoparticles like Al2O3 and Fe2O3 have received a lot of attention. Examination of microstructure, hardness, wear, and tensile has been conducted. All the data showed that adding nanoparticles had a significant impact on the properties compared with the properties of pure Al; for example, hardness increased by (20, 53)%, and volume wear loss by (27.7, 69.7)%. Moreover, increasing reach to (82, 254)% in UTS and (52, 107)% in yield point. These outcomes result from significant microstructural improvement (refining of grain size). &nbsp

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging : an experimental study

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pretrained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets. Subjects Bioinformatics, Artificial Intelligence, Computer Vision</p

Robust spectrum sensing detector based on mimo cognitive radios with non-perfect channel gain

The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.</p

Delusional Severity Is Associated with Abnormal Texture in FLAIR MRI

Background: This study examines the relationship between delusional severity in cognitively impaired adults with automatically computed volume and texture biomarkers from the Normal Appearing Brain Matter (NABM) in FLAIR MRI. Methods: Patients with mild cognitive impairment (MCI, n = 24) and Alzheimer&rsquo;s Disease (AD, n = 18) with delusions of varying severities based on Neuropsychiatric Inventory-Questionnaire (NPI-Q) (1&mdash;mild, 2&mdash;moderate, 3&mdash;severe) from the Alzheimer&rsquo;s Disease Neuroimaging Initiative (ADNI) were analyzed for this task. The NABM region, which is gray matter (GM) and white matter (WM) combined, was automatically segmented in FLAIR MRI volumes with intensity standardization and thresholding. Three imaging biomarkers were computed from this region, including NABM volume and two texture markers called &ldquo;Integrity&rdquo; and &ldquo;Damage&rdquo;. Together, these imaging biomarkers quantify structural changes in brain volume, microstructural integrity and tissue damage. Multivariable regression was used to investigate relationships between imaging biomarkers and delusional severities (1, 2 and 3). Sex, age, education, APOE4 and baseline cerebrospinal fluid (CSF) tau were included as co-variates. Results: Biomarkers were extracted from a total of 42 participants with longitudinal time points representing 164 imaging volumes. Significant associations were found for all three NABM biomarkers between delusion level 3 and level 1. Integrity was also sensitive enough to show differences between delusion level 1 and delusion level 2. A significant specified interaction was noted with severe delusions (level 3) and CSF tau for all imaging biomarkers (p &lt; 0.01). APOE4 homozygotes were also significantly related to the biomarkers. Conclusion: Cognitively impaired older adults with more severe delusions have greater global brain disease burden in the WM and GM combined (NABM) as measured using FLAIR MRI. Relative to patients with mild delusions, tissue degeneration in the NABM was more pronounced in subjects with higher delusional symptoms, with a significant association with CSF tau. Future studies are required to establish potential tau-associated mechanisms of increased delusional severity

Delusional Severity Is Associated with Abnormal Texture in FLAIR MRI

Background: This study examines the relationship between delusional severity in cognitively impaired adults with automatically computed volume and texture biomarkers from the Normal Appearing Brain Matter (NABM) in FLAIR MRI. Methods: Patients with mild cognitive impairment (MCI, n = 24) and Alzheimer's Disease (AD, n = 18) with delusions of varying severities based on Neuropsychiatric Inventory-Questionnaire (NPI-Q) (1-mild, 2-moderate, 3-severe) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed for this task. The NABM region, which is gray matter (GM) and white matter (WM) combined, was automatically segmented in FLAIR MRI volumes with intensity standardization and thresholding. Three imaging biomarkers were computed from this region, including NABM volume and two texture markers called "Integrity" and "Damage". Together, these imaging biomarkers quantify structural changes in brain volume, microstructural integrity and tissue damage. Multivariable regression was used to investigate relationships between imaging biomarkers and delusional severities (1, 2 and 3). Sex, age, education, APOE4 and baseline cerebrospinal fluid (CSF) tau were included as co-variates. Results: Biomarkers were extracted from a total of 42 participants with longitudinal time points representing 164 imaging volumes. Significant associations were found for all three NABM biomarkers between delusion level 3 and level 1. Integrity was also sensitive enough to show differences between delusion level 1 and delusion level 2. A significant specified interaction was noted with severe delusions (level 3) and CSF tau for all imaging biomarkers (p < 0.01). APOE4 homozygotes were also significantly related to the biomarkers. Conclusion: Cognitively impaired older adults with more severe delusions have greater global brain disease burden in the WM and GM combined (NABM) as measured using FLAIR MRI. Relative to patients with mild delusions, tissue degeneration in the NABM was more pronounced in subjects with higher delusional symptoms, with a significant association with CSF tau. Future studies are required to establish potential tau-associated mechanisms of increased delusional severity. </p