Solar electric ambulance van to assist rural emergencies of Bangladesh- a complete off-grid solution

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 58-59).Rickshaws are an essential method of transportation in Bangladesh. When it was altered to electrically assisted rickshaw, it attained popularity but was soon discontinued due to over consumption of power. Therefore, Control and Applications Research Centre (CARC), BRAC University has proposed the concept to give a complete off-grid arrangement by utilizing the PV array, torque sensor pedal, and solar battery charging station and implementing it in a solar electric ambulance van as a significant part of individuals who live in the provincial zones, where the likelihood of reaching the hospital on time is very low due to lack of mode of transportation. The torque sensor pedal lessens the over-utilization of battery-bank. The intelligent control framework reduces the human force and diminishes the over-utilization of engine. PV panel is introduced on top of the van to share a part of the power and a solar battery charging station is installed to make the entire framework totally autonomous of national grid. This paper consists of the design and implementation of the idea proposed by CARC.Rahmeen TarekAfra AnjumMd. Abrar-Ul-HoqueForkan Abdur RahimB. Electrical and Electronic Engineerin

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

Re-evaluation on Causes of Circular Knitting Machine Production Efficiency and their Impact on Fabric Quality

The productivity of knitting machine depends on yarn type and its quality, knitting parameters, operating conditions, workplace safety measures, and motivational factors of workforce. For several reasons, knitting machine can stop working and the production process is interrupted. In this paper, it was found that the main reason for yarn breakage is the condition of the working environment in which it operates. Yarn breakage is not only responsible for production losses, but it also creates quality problems on fabric texture. Quality is money as it is interlinked with reputation. In order to survive in a competitive market, it is important to analyse and resolve the root causes which can hamper production efficiency. Environmental factors are involved with knitted fabric production efficiency. Less production efficiency enhances more power consumption and other utilities. As a consequence, the cost of pollution on our environment is rising. This paper shows that causes of production loss and efficiency interruptions is due to different conditions of working environment and other facilities which can motivate employees to work efficiently in their workstations

ClaimDistiller: Scientific Claim Extraction with Supervised Contrastive Learning

The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting the F1 score of the base models by 3–4%, and achieved an F1=87.45%, improving the state-of-the-art by more than 7% on the same benchmark data previously used for this task. The same phenomenon is observed on another benchmark dataset, and ClaimDistiller consistently has the best performance. Qualitative assessment on a small sample of out-of-domain data indicates that the model generalizes well. Our source codes and datasets can be found here: https://github.com/lamps-lab/sci-claim-distiller

CONSCIOUS USE OF CODE-SWITCHING TO IMPROVE FLUENCY IN SPOKEN ENGLISH OF BANGLADESHI STUDENTS

Purpose: Present-day English language teaching in Bangladesh, despite adopting Communicative Language Teaching (CLT), focuses more on accuracy (i.e. grammar) than fluency which is proved as a weak strategy. Fluency acquisition in speaking includes pronunciation, but focusing on pronunciation first, slows down the process of becoming a fluent speaker. Methodology: As code-switching exists at the tertiary level in Bangladesh and because of the tremendous fascination of Bangladeshi students towards Bengali, code-switching can be utilized as a tool to improve fluency in spoken English. Thus, this research proposes an alternative to existing approaches. Result: Once desired fluency is achieved; grammar and pronunciation will be emphasized respectively to attain proper speaking skills. The success of this process can be studied in three phases. In the first phase, it examines whether allowing code-switching while speaking English helps Bengali students achieve fluency or not. Applications: This research can be used for universities, teachers, and students. Novelty/Originality: In this research, the model of the Conscious Use of Code-Switching to Improve Fluency in Spoken English of Bangladeshi Students is presented in a comprehensive and complete manner

Estimation of genetic variability, correlation and path coefficient analysis in local landraces of rice (Oryza sativa L.) for the improvement of salinity tolerance

Twenty eight local rice landraces were assessed for eleven morphological traits for the improvement of salt tolerance ability of rice genotypes. Genotypic variance (σ2g), phenotypic variance (σ2p), phenotypic co-variance (PCV), genotypic co-variance (GCV), heritability, genetic advance, genetic advance as a percentage of mean, correlation coefficient and path coefficient were estimated. For all the traits, PCV was higher than the GCV indicating that they were controlled by non-additive gene action and selection for the improvement of these traits would be promising. Among the traits, survival rate of plant exhibited high estimates of PCV (460.72) and GCV (324.73) indicated wide range of variability for these traits where lowest phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) where low PCV and GCV were observed for root dry weight (0.17 & 0.12) followed by shoot dry weight (0.31 & 0.16) and total number of roots (0.61 & 0.34) indicating lack of inherent variability and limited scope for improvement through selection for these traits among the genotypes. Highest heritability had observed in all traits except chlorophyll content and root length and maximum value of heritability was noticed for shoot length (75.96%). High heritability along with high genetic advance was noticed for survival rate of plant (31.14%) followed by live leaves percentage (11.98) and the lowest genetic advance was found in root dry weight (0.58) and shoot dry weight (0.58). Correlation study revealed that significant negative correlations at both phenotypic and genotypic level exist between standard evaluation score (SES) and survival rate (%), root length, shoot length, root fresh weight, root dry weight and shoot dry weight at the seedling stage further confirmed the importance of these parameters as useful selection criteria for screening for salt tolerance rice genotypes. Path analysis revealed that the root length (0.487), root dry weight (0.394) and shoot dry weight (0.047) had direct positive effect on standard evaluation score at genotypic level where live leaves (%) per plant (0.168), total number of roots (0.006), chlorophyll content (0.243) and shoot fresh weight (0.102) had direct positive effect on standard evaluation score at phenotypic level. From the correlation and path analysis it can be concluded that root length, root dry weight and shoot dry weight would be more promising for the improvement of salt tolerance in rice genotype

Phenolics and carotenoids contents and radical scavenging capacity of some selected solanaceous medicinal plants

Plants being an important source of medicine play significant role in human health. The aim of the present study was to evaluate thetotal phenolics and carotenoids contents, and free radical scavenging capacity of leaves and fruits of selected five solanaceous medicinal plants, namely Solanum melongena (brinjal), Solanum torvum (tit begun), Solanum virginianum (kantikari), Solanum sisymbrifolium (sada kantikari) and Solanum nigrum (futi begun). Carotenoids content in the leaves and fruits of solanaceous plants varied significantly among the species. Leaf phenolics content ranged between 147.40 (S. melongena) and 585.15 (S. virginianum) mg gallic acid equivalent (GAE)/100 g fresh weight, while fruit phenolics content varied from 50.52 (S. nigrum) to 105.02 (S. virginianum) mg GAE/100 g fresh weight. IC50 values for scavenging 2, 2-diphenyl-1-picrylhydrazyl radical (DPPH) radical ranged between 31.52 (S. nigrum) and 33.55 (S. melongena) mg mL−1 in leaf, while in fruit it ranged between 27.90 (S. virginianum) and 33.11 (S. melongena) mg mL−1. The highest carotenoids content (0.370 mg g−1 fresh weight) was measured from Solanum nigrum leaf. S. virginianum leaf contained about 4−fold high phenolics content than that in S. melongena. S. nigrum leaf had about 15−fold high carotenoids content (0.370 mg g−1 fresh weight). compared to S. torvum and S. virginianum fruits (0.024 mg g−1FW in each). Because of the highest fruit phenolics and carotenoids content along with the lowest IC50 values for scavenging DPPH, S. virginianum fruit can be considered as superior for its health beneficial biochemical constituent

Phenolics and carotenoids contents and radical scavenging capacity of some selected solanaceous medicinal plants

Plants being an important source of medicine play significant role in human health. The aim of the present study was to evaluate thetotal phenolics and carotenoids contents, and free radical scavenging capacity of leaves and fruits of selected five solanaceous medicinal plants, namely Solanum melongena (brinjal), Solanum torvum (tit begun), Solanum virginianum (kantikari), Solanum sisymbrifolium (sada kantikari) and Solanum nigrum (futi begun). Carotenoids content in the leaves and fruits of solanaceous plants varied significantly among the species. Leaf phenolics content ranged between 147.40 (S. melongena) and 585.15 (S. virginianum) mg gallic acid equivalent (GAE)/100 g fresh weight, while fruit phenolics content varied from 50.52 (S. nigrum) to 105.02 (S. virginianum) mg GAE/100 g fresh weight. IC50 values for scavenging 2, 2-diphenyl-1-picrylhydrazyl radical (DPPH) radical ranged between 31.52 (S. nigrum) and 33.55 (S. melongena) mg mL−1 in leaf, while in fruit it ranged between 27.90 (S. virginianum) and 33.11 (S. melongena) mg mL−1. The highest carotenoids content (0.370 mg g−1 fresh weight) was measured from Solanum nigrum leaf. S. virginianum leaf contained about 4−fold high phenolics content than that in S. melongena. S. nigrum leaf had about 15−fold high carotenoids content (0.370 mg g−1 fresh weight). compared to S. torvum and S. virginianum fruits (0.024 mg g−1FW in each). Because of the highest fruit phenolics and carotenoids content along with the lowest IC50 values for scavenging DPPH, S. virginianum fruit can be considered as superior for its health beneficial biochemical constituent

Estimation of genetic variability, correlation and path coefficient analysis in local landraces of rice (Oryza sativa L.) for the improvement of salinity tolerance

Twenty eight local rice landraces were assessed for eleven morphological traits for the improvement of salt tolerance ability of rice genotypes. Genotypic variance (σ2g), phenotypic variance (σ2p), phenotypic co-variance (PCV), genotypic co-variance (GCV), heritability, genetic advance, genetic advance as a percentage of mean, correlation coefficient and path coefficient were estimated. For all the traits, PCV was higher than the GCV indicating that they were controlled by non-additive gene action and selection for the improvement of these traits would be promising. Among the traits, survival rate of plant exhibited high estimates of PCV (460.72) and GCV (324.73) indicated wide range of variability for these traits where lowest phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) where low PCV and GCV were observed for root dry weight (0.17 & 0.12) followed by shoot dry weight (0.31 & 0.16) and total number of roots (0.61 & 0.34) indicating lack of inherent variability and limited scope for improvement through selection for these traits among the genotypes. Highest heritability had observed in all traits except chlorophyll content and root length and maximum value of heritability was noticed for shoot length (75.96%). High heritability along with high genetic advance was noticed for survival rate of plant (31.14%) followed by live leaves percentage (11.98) and the lowest genetic advance was found in root dry weight (0.58) and shoot dry weight (0.58). Correlation study revealed that significant negative correlations at both phenotypic and genotypic level exist between standard evaluation score (SES) and survival rate (%), root length, shoot length, root fresh weight, root dry weight and shoot dry weight at the seedling stage further confirmed the importance of these parameters as useful selection criteria for screening for salt tolerance rice genotypes. Path analysis revealed that the root length (0.487), root dry weight (0.394) and shoot dry weight (0.047) had direct positive effect on standard evaluation score at genotypic level where live leaves (%) per plant (0.168), total number of roots (0.006), chlorophyll content (0.243) and shoot fresh weight (0.102) had direct positive effect on standard evaluation score at phenotypic level. From the correlation and path analysis it can be concluded that root length, root dry weight and shoot dry weight would be more promising for the improvement of salt tolerance in rice genotype

PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition

Parkinson's disease (PD) is a neuro-degenerative disorder that affects movement, speech, and coordination. Timely diagnosis and treatment can improve the quality of life for PD patients. However, access to clinical diagnosis is limited in low and middle income countries (LMICs). Therefore, development of automated screening tools for PD can have a huge social impact, particularly in the public health sector. In this paper, we present PULSAR, a novel method to screen for PD from webcam-recorded videos of the finger-tapping task from the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS). PULSAR is trained and evaluated on data collected from 382 participants (183 self-reported as PD patients). We used an adaptive graph convolutional neural network to dynamically learn the spatio temporal graph edges specific to the finger-tapping task. We enhanced this idea with a multi stream adaptive convolution model to learn features from different modalities of data critical to detect PD, such as relative location of the finger joints, velocity and acceleration of tapping. As the labels of the videos are self-reported, there could be cases of undiagnosed PD in the non-PD labeled samples. We leveraged the idea of Positive Unlabeled (PU) Learning that does not need labeled negative data. Our experiments show clear benefit of modeling the problem in this way. PULSAR achieved 80.95% accuracy in validation set and a mean accuracy of 71.29% (2.49% standard deviation) in independent test, despite being trained with limited amount of data. This is specially promising as labeled data is scarce in health care sector. We hope PULSAR will make PD screening more accessible to everyone. The proposed techniques could be extended for assessment of other movement disorders, such as ataxia, and Huntington's disease