Geopolymer concrete columns subjected to axial load and biaxial bending

This thesis focuses on the behaviour of fly ash based geopolymer concrete columns under axial load and biaxial bending. Tests showed that failure load of columns increased with the increase of concrete compressive strength and longitudinal reinforcement ratio, and decreased with the increase of load eccentricity. Use of the Bresler’s reciprocal load formula with an iterative procedure for slender columns in uniaxial bending conservatively predicted the strength of the test columns

Decomposition of color wavelet with higher order statistical texture and convolutional neural network features set based classification of colorectal polyps from video endoscopy

Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of colorectal polyps can be cogitated. The most used diagnostic modality for colorectal polyps is video endoscopy. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of the polyp and thus improve the accuracy of diagnosis results. The proposed method first detects polyp and non-polyp then illustrates an automatic polyp classification technique from endoscopic video through color wavelet with higher-order statistical texture feature and Convolutional Neural Network (CNN). Gray Level Run Length Matrix (GLRLM) is used for higher-order statistical texture features of different directions (Ɵ = 0o, 45o, 90o, 135o). The features are fed into a linear support vector machine (SVM) to train the classifier. The experimental result demonstrates that the proposed approach is auspicious and operative with residual network architecture, which triumphs the best performance of accuracy, sensitivity, and specificity of 98.83%, 97.87%, and 99.13% respectively for classification of colorectal polyps on standard public endoscopic video databases

Business Cycles and Seasonal Cycles in Bangladesh

The empirical regularities of the Bangladesh business and seasonal cycles are documented in this study. Spectrums, seasonality, volatility, cyclicality, and persistence in the level and variance of macroeconomic variables in Bangladesh are explored using monthly and quarterly macroeconomic series. Most of the features of U.S. and East-Southeast Asian business cycles are common to Bangladeshi business cycles; however, there are some differences. As is seen in the U.S. and European economies, seasonal cycles accentuate the features of business cycles in Bangladesh. To our surprise, the seasonal cycles in Bangladesh embody the features of business cycles in the U.S. and East-Southeast Asian economies more thoroughly than they do the business cycles in Bangladesh

An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Gastrointestinal polyps are considered to be the precursors of cancer development in most of the cases. Therefore, early detection and removal of polyps can reduce the possibility of cancer. Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. But, because it is an operator dependent procedure, several human factors can lead to misdetection of polyps. Computer aided polyp detection can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention to. In this paper, an automatic system has been proposed as a support to gastrointestinal polyp detection. This system captures the video streams from endoscopic video and, in the output, it shows the identified polyps. Color wavelet (CW) features and convolutional neural network (CNN) features of video frames are extracted and combined together which are used to train a linear support vector machine (SVM). Evaluations on standard public databases show that the proposed system outperforms the state-of-the-art methods, gaining accuracy of 98.65%, sensitivity of 98.79%, and specificity of 98.52%

Bone Grafts in Jaw Cysts- Hydroxyapatite & Allogenic Bone A Comparative Study

Background: Auto bone is the gold standard in bone grafting. However, the morbidity and additional surgical time associated with its collection, as well as the limited supply, have stimulated the search for substitutes. Allograft is more limited than autograft because it yields more variable clinical results. Composite synthetic grafts offer an alternative because Hydroxyapatite is chemically identical to the inorganic matrix of living bones and it can be processed synthetically. The intent was to evaluate these two graft materials for clinical use and to provide an insight on the different grafting strategies to enhance bone formation. Objective: To find out the bone healing process and the prognostic value for the patient using hydroxyapatite alloplastic material and allogenic bone graft. Method: Total 28 patients were included in the study after the clinical and radiological evaluation where 14 cases were treated with allogenic-bone graft and rest 14 cases were treated with hydroxyapatite alloplastic material after enucleation of the cystic lesion in random manner. The integration of hydroxyapatite and allogenic bone was assessed with postoperative lesion diameter, trabecular pattern, histopathological and scintigraphic examination of the successful graft cases. Statistical analysis was carried out by ‘unpaired T test' and ‘Chi square' test. Result: The radiological, histopathological and scintigraphical outcome of the patients treated with hydroxyaptite granule bone graft were clinically and statistically superior in comparison with those who were treated with allogenic bone graft. Conclusion: This safe and osteoconductive hydroxyapatite appears suitable for filling bone defects and bone cavities, showing less resorption and a rapid osseous integration. Key words: Hydroxyapatite; allogenic bone; scintigraphy; radiology; histopathology.DOI: 10.3329/bsmmuj.v2i1.3707 BSMMU J 2009; 2(1): 25-3

MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder

One of the common and promising deep learning approaches used for medical image segmentation is transformers, as they can capture long-range dependencies among the pixels by utilizing self-attention. Despite being successful in medical image segmentation, transformers face limitations in capturing local contexts of pixels in multimodal dimensions. We propose a Medical Image Segmentation Transformer (MIST) incorporating a novel Convolutional Attention Mixing (CAM) decoder to address this issue. MIST has two parts: a pre-trained multi-axis vision transformer (MaxViT) is used as an encoder, and the encoded feature representation is passed through the CAM decoder for segmenting the images. In the CAM decoder, an attention-mixer combining multi-head self-attention, spatial attention, and squeeze and excitation attention modules is introduced to capture long-range dependencies in all spatial dimensions. Moreover, to enhance spatial information gain, deep and shallow convolutions are used for feature extraction and receptive field expansion, respectively. The integration of low-level and high-level features from different network stages is enabled by skip connections, allowing MIST to suppress unnecessary information. The experiments show that our MIST transformer with CAM decoder outperforms the state-of-the-art models specifically designed for medical image segmentation on the ACDC and Synapse datasets. Our results also demonstrate that adding the CAM decoder with a hierarchical transformer improves segmentation performance significantly. Our model with data and code is publicly available on GitHub.Comment: 10 pages, 2 figures, 3 tables, accepted for publication in WACV 202

Socioeconomic Status Measurement: An Analysis of Incorporation of Mixed Variables into Principal Component Approach

Socioeconomic status of a household in Bangladesh changes overtime for many reasons. The measurement of this change is a very important tool in many aspects. This paper aims to examine the dynamic nature of wealth status in Bangladesh. In particular, we want to capture the overall wealth transition in rural area of Bangladesh from year 2004 to 2015. To calculate this transition, we construct wealth index for each of the year 2004, 2009, and 2015 using the ‘poverty analysis survey data’. This survey has conducted on the same households in each three years. Nonlinear principal component analysis (PCA) with optimal scaling using gifi method as our PCA tool is used here for wealth index construction. This method is designed to use with a data set that contains both numerical and categorical variables jointly. Then the transition of wealth is calculated using these three-wealth index. Based on the transition result, we classified each of the households into four different social groups such as non-poor, ascending poor, descending non-poor, and chronically poor