Design, Fabrication and Characterization of a Unipolar Charge Sensing Amorphous Selenium X-ray Detector

Amorphous Selenium is a direct conversion photoconductor that has been widely used in X-ray imaging applications. Due to its high spatial resolution A-Se plays an important role in breast cancer screening and diagnostics, allowing for the detection of small and subtle lesions. However, a-Se has poor collection efficiency due to low carrier mobility and charge trapping resulting from its amorphous structure. The trapped charges can cause memory artifacts, including photocurrent lag, which can persist for several seconds after the X-ray pulse has ended. As a result, a-Se is a challenging material for dynamic imaging applications that require high spatial resolution. The research discussed in this thesis aims to investigate and address the temporal behavior of a-Se photoconductors, specifically the issue of lag, which can lead to image artifacts and degradation of image quality in dynamic imaging applications. The research involves the design of unipolar charge sensing detectors with pixel sizes of 20, 40, 80 and 150 microns to improve energy resolution and the temporal response compared to conventional a-Se detectors. Theoretical analysis and simulations are presented for the unipolar charge sensing detector including weighting potential, charge collection efficiency, pulse height spectroscopy and energy resolution which range from 5% to 2% . The work further discusses the fabrication process of the designed detector in the G2N lab at the university of Waterloo. It discusses the experimental results obtained and the challenges that were faced while fabricating the detector and how they can be overcome in the futur

Effect of Natural and Artificial Diets on the Life History Parameters of Melon Fruit Fly Bactrocera Cucurbitae (Coquillett)

Fruit flies are the noxious pests of fruits and vegetables throughout out the tropical and subtropical regions of the world. The melon fruit fly Bactrocera cucurbitae is a polyphagous pest of vegetables and fruits. We evaluated the effect of natural bottle gourd, (Lagenaria siceraria) and artificial diets on the life history parameters of B. cucurbitae under lab conditions. Results revealed that shortest incubation period (3.0 ± 0.54) was observed on natural diet whereas; lowest hatching % (12.4 ± 2.11) was observed on blotting papers. However, reduced larval duration (5.6 ± 0.24) was observed when maggots were provided with bottle gourd as compared with artificial diet (6.6 ± 0.24). Furthermore, significantly (p < 0.05) higher pupal recovery, pupal duration and adult emergence were recorded on natural diet. In contrast, statistically higher pupal weight (p < 0.05) was observed on artificial diet. In addition, number of deformed adults was higher in natural diet as compared to artificial diet. These findings could be helpful in defining more optimum conditions for the mass rearing of B.cucurbitae for use in Sterile Insect Technique (SIT), programmes for various orchards. Key words: Natural diet, Artificial diet, Incubation period of B. cucurbitae.

Policy advisory systems and public policy making: Bibliometric analysis, knowledge mapping, operationalization, and future research agenda

This study offers a comprehensive overview of the literature on policy advisory systems (PAS), motivated by its significance, growing interest in the field among policy scholars, and the substantial body of recent research published recently. The study conducted a quantitative bibliometric analysis of 62 articles on PAS published between 1993 and 2022, using data extracted from the Web of Science and Scopus databases. The analysis included performance analysis, collaboration analysis, and science mapping (co-citation and co-word analysis). The dataset comprises articles published in 29 different journals, with 17.2% of these journals publishing three or more articles on PAS. Of the 80 authors in the dataset, 78.8% had only one publication. Over the past 5 years, there has been a 69% increase in the production of research on PAS, with the majority of output coming from Canada, Australia, Germany, and the Netherlands. The intellectual structure of the field was examined through co-citation analysis, revealing two main clusters named dynamics of PAS and Policy Advice & Policy Analysts' Role. The former focuses on the variations and dynamics of PAS, while the latter is more concerned with the nature of policy advice and the role of policy analysts. The co-word analysis identified the most relevant issues linked with PAS, including policy advice, externalization, politicization, and policy capacity. The study has also provided operationalization of the concept with the support of empirical evidence and categorized it into internal and external PAS. Externalization is the dominant trend in Anglophone countries, while public service in Europe and Canada has been the focus of internal PAS research. The study identifies gaps in the literature and calls for further research on the application of PAS in the Global South, the demand for policy advice, and comparative analysis of PAS across different contexts and dimensions

A qualitative framing analysis of how firearm manufacturers and related bodies communicate to the public on gun-related harms and solutions

There is a growing understanding that the producers and sellers of harmful products directly and indirectly affect population health and policy, including through seeking to influence public understanding about the nature of harms and their solutions. However, the firearm industry and related organisations have not to date been the subject of this type of enquiry. This study sought to address this evidential gap through examining the ways in which the firearm industry and industry-associated organisations frame firearms, firearm-related harms and possible solutions to gun violence. This was a thematic qualitative documentary analysis of materials from 7 of the largest firearm manufacturers and associated organisations. Two authors independently extracted textual material from web articles, press releases, annual reports and shareholder communications between 1st April 2019 to 1st April 2020 (302 documents). A hybrid approach combining both deductive and inductive coding was adopted, guided by the literature on the commercial determinants of health and using NVivo version 12. The firearm industry and firearm industry-funded organisations use framings about the safety and role of guns, evidence on associated harms and solutions that align with the industry's business interests, consistent with evidence on other harmful product manufacturers. This study identified framing strategies employed by the firearm industry and related organisations. These included attempts to undermine evidence, linking regulation to a dystopian future, minimising some of the most common harms, placing the responsibility for harms on individuals, and attempting to foster a heightened sense of risk to personal safety

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background: Skin diseases are reported to contribute 1.79\% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Objective: Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. Methods: We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. Results: The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. Conclusions: To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

ULTRASONOGRAPHIC EVALUATION OF LOWER UTERINE SEGMENT THICKNESS IN PREGNANT WOMEN WITH PREVIOUS CESAREAN SECTION: A SYSTEMATIC REVIEW

Background: Nowadays it’s a big problem of lower uterine thickness in pregnant women that causes a lot of problems. Lower uterine segment thickness is a strong predictor for uterine scar defect in women with prior caesarian section. Lower segment cesarean section (LSCS) rates are raising throughout the world. Women with previous one cesarean can undergo either the trial of vaginal birth or elective repeat cesarean section in their next pregnancy. The study aims to assess the diagnostic accuracy of sonographic measurement of lower uterine segment thickness in pregnant women So there we are going to evaluate lower uterine segment thickness in pregnant women sonographically by using transabdominal and transvaginal approaches as well. Objective: To evaluate lower uterine segment thickness in pregnant women with previous caesarian section by sonography. Materials and Methods: An electronic data base search was performed through the searches using PubMed, Google Scholar, international Journal of Gynecology & obstetrics and some other online journals and medical websites with the range from 2000-2021. All studies included in the research was in English language. Articles which had descriptive studies related to sonographic features of lower uterine segment thickness of pregnant women with C-section. Results: Research data of 200 patients in all studies showed that Transvaginal ultrasound provided greater reliability in LUS measurements than did transabdominal ultrasound. The use of three-dimensional ultrasound improved significantly the reliability of the LUS muscular thickness measurement obtained transvaginal Conclusion: We identified certain sonographic patterns that can accurately shows the lower uterine segment thickness in pregnant women with previous caesarian section. Keywords: LUS lower uterine segment thickness, Transabdominal and Transvaginal ultrasonography, Pregnancy, Caesarian Section DOI: 10.7176/JHMN/93-07 Publication date:September 30th 202

Soft Tissue Tumours with Epithelioid Morphoogy

 To study the morphologicalfindings of soft tissue sarcomas with epithelioidmorphology and their distribution with respect tothe age, gender and location

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background:Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency.Objective:Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions.Methods:We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance.Results:The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet.Conclusions:To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time