81 research outputs found

    Estimation of growth and financial analysis through the application of Ipil ipil (Leucaena leucocephala) leaf meal as supplements to soybean and fish meal in the diet of juvenile monosex tilapia (Oreochromis niloticus)

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    Among plant protein ingredients,ipil ipil (Leucaena leucocephala) leafmeal (ILLM) is considered the most nutritive plant protein source after soybean meal in aquatic feeds. That was proven in a 21-day experiment conducted to assess the response of juvenile Monosex Nile tilapia Oreochromis niloticus with four iso-nitrogenous formulated diets: One control diet was formulated based on fishmeal, one on soybean meal and one on rice bran, ipil ipil leafmeal was also included in experimental diets

    Climate change and its risk reduction by mangrove ecosystem of Bangladesh

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    Climate change is amongst the most dreaded problems of the new millennium. Bangladesh is a coastal country bounded by Bay of Bengal on its southern part and here natural disasters are an ongoing part of human life. This paper discusses about the possible impact of climate change through tropical cyclones, storm surges, coastal erosion and sea level rise in the coastal community of Bangladesh and how they cope with these extreme events by the help of mangrove ecosystem. Both qualitative and quantitative discussions are made by collected data from different research work those are conducted in Bangladesh. Mangrove ecosystem provides both goods and services for coastal community, helps to improve livelihood options and protect them from natural disaster by providing variety of environmental suppor

    Automated detection of pain levels using deep feature extraction from shutter blinds‑based dynamic‑sized horizontal patches with facial images

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    Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or “shutter blinds”. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases—University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database—which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain

    Determination of factors influencing student engagement using a learning management system in a tertiary setting

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    Determining the key factors that affect student engagement will assist academics in improving students’ motivation. The Quality Indicators for Learning and Teaching (QILT) reports have shown low engagement levels in higher education student cohorts (QILT 2016, 2017). While factors such as online education, lack of attendance, and poor course content design have been attributed to this cause, it is still not clear as to the determination of those factors influencing student engagement in a higher education setting. It is widely accepted that the selection of appropriate learning resources is an essential phase in the education process. In contrast, an incompatible range of course materials can demotivate a student from engaging in the course (Quaye & Harper 2014). In the modern tertiary setting, Information and Communication Technology (ICT) plays an essential role in disseminating information with a Learning Management System (LMS) as the platform to communicate crucial course-related information. Academics can develop course materials on these LMSs to engage students beyond the classrooms, and students need to interact through the same platform to comprehend the transmitted knowledge. Since LMSs are operated on a computer platform, academics and students require strong ICT skills which are further utilised in the preparation of course materials. The knowledge required is dependent on the relevance and appropriateness of materials, the way various tasks are prepared, how communication is facilitated, the role and utilisation of discussion forums and other available social media structures, and the way in which assessments are conducted. This cumulatively leads to the development of a Just in Time (JIT) type of knowledge, which can be challenging to measure. The investigation into these major factors forms the basis of this study. Thus, understanding how various factors influence student engagement through the use of LMS platforms in a tertiary setting is the focus of this study. This study used a hybrid method involving a qualitative component to understand the factors that influence the student engagement in an LMS driven learning setting and a quantitative component for confirmation of various factors identified through the literature review. The study developed five specific hypotheses for testing, and the following table shows the outcomes of hypotheses testing: H1: Students are influenced by teaching resources in order to realise engagement in classroom activities - ACCEPTED H2: Academics influence engagement in classroom activities through their involvement in various teaching and management aspects - REJECTED H3: An academic’s activities influence the management of teaching activities, resulting in improved engagement by students in the class - ACCEPTED H4: Learning Management Systems (LMS) are a key part in improving students’ engagement - REJECTED H5: Management of various study-related activities to reach focus in the study will positively influence students’ engagement - ACCEPTED The outcomes of the study indicate that students and associated classroom activities, teaching resources, management of teaching, the way LMSs are established, and students’ requirements and needs play a key role in assuring engagement. This study also found that an academic’s activities play a less significant role in fostering engagement as there appears to be a shift from teaching to teaching management, as evidenced in the qualitative discussion. Further, the participants expected academics to have superior technology communication skills as this is essential in an LMS driven setting. Interestingly, this study correlated with a number of standards dictated by the Tertiary Education Quality Standards of Australia (TEQSA), a regulatory body that enforces standards in Australian tertiary education. This correlation was observed despite the fact that students that participated in this study had limited awareness of these TEQSA standards. The main contribution of this study is in highlighting the fact that academics and other support services in tertiary settings should focus on how the LMS is presented as participants expressed that clear navigation of the system is essential for engagement. This has profound implications in the way the recruitment of academics is conducted. In terms of practice, TEQSA standards are key in assuring quality in tertiary settings, and this study has provided strong evidence as to the needs for support systems, the way learning objectives are mapped to deliver learning outcomes, appropriateness of the content, time imposition on students in managing their study-related activities, and integration of technology. These are now a standard part of the TEQSA assessment. The study can be further improved in the future by collecting data from various cohorts: for example, fulltime vs part-time, domestic vs overseas, and mature vs school leavers, to better assess their views in terms of engagement as these cohorts come with varying needs. These can then be encapsulated in the learning materials and systems development. This would then lead to a better alignment of learning management and engagement to realise better outcomes

    Garantizar los derechos de las personas desplazadas por el cambio climático en Bangladesh

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    Cinco áreas críticas requieren medidas urgentes debido a que padecen la amenaza del desplazamiento interno como consecuencia del cambio climático ya grave y creciente en Bangladesh

    Determination of factors influencing student engagement using a learning management system in a tertiary setting

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    Determining the key factors that affect student engagement will assist academics to improve the student motivation. The Quality Indicators for Learning and Teaching (QILT) reports have shown low engagement levels in higher education students [21, 22, 23]. While factors such as online education, lack of attendance and poor design of course content have been attributed to this cause, it is still not clear as to the determination of those factors influencing student engagement in a higher education setting. In the modern tertiary settings, Information and Communication Technology (ICT) plays an essential role in disseminating the course related information with a Learning Management System (LMS) which become the platform to communicate crucial course-related information. Academics can develop course materials on these LMS’ to engage students beyond the classrooms and students need to interact with those LMS’ to get apprehend the transmitted knowledge. Since LMS’ are operated on a computer platform, academics and students require strong ICT skills which are further utilized in preparation of course materials. Their relevance, appropriateness, the way various tasks are prepared, how communication is facilitated, the role and utilization of discussion forums and other social media structures available to students to interact with, and the way in which assessments are conducted, providing a Just in Time (JIT) type of knowledge students require. The investigation into these major factors forms the basis of this study. Thus, understanding how various factors related to LMS’ in a tertiary setting influence student engagement and then determining those factors that contribute to this engagement are the main objective of this study. To pursue the main objective of this study, a hybrid method mainly involving a pseudo meta-analysis to unearth additional evidence required for the study, a comprehensive qualitative component to understand the sector factors and perhaps a small quantitative component to confirm the sector views will be employed

    Determination of optimum stocking density of Macrobrachium rosenbergii larvae using multiple feed in a commercial hatchery at Cox’s Bazar, Bangladesh

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    Experimental stocking density of Macrobrachium rosenbergii in larval rearing was conducted in A.G. Aqua Hatchery, Chakaria, Bangladesh to study the effect of different stocking densities on growth, survival rate and diseases stress under hatchery condition. The research work was conducted using six cemented rectangular tanks having 3m3 capacity (1.5mX2mX1m) each. Stocking density were maintained in three experimental setup as 200, 150 and 100ind/L of the T1, T2 and T3 respectively with one replicate each. The larvae were fed with Artemia nauplii, Custard, Maxima and brine shrimp flakes. Water quality was maintained by exchanging 20-30% (12ppt saline water) daily. During the study period, temperature, pH, DO, salinity, nitrite-nitrogen, ammonia and alkalinity were maintained from 28.5-31.5ÂşC, 7.5-7.8, 5.8-5.9mg/L, 12-13ppt, 0.14-0.2 mg/L, 0.22-0.3mg/L, and 140-160mg/L respectively. The growth rates of larvae at 11th stage were recorded in terms of body length 0.115, 0.136, and 0.169 mm/day whereas body weight were observed 0.000115, 0.000180, and 0.000240g/day. The survival rate of larvae were found 21.8%, 30.4% and 51.3% in treatments T1, T2 and T3 respectively. PL was obtained as 43, 45, and 51PL/L and days required of 41, 38 and 34 days in stocking density of 200, 150, and 100ind/L respectively. It was found that the minimum of 34 days was required to attain the PL (12th stage) using the stocking density of 100 individuals/L. Cannibalism, Zoothamnium, Exuvia Entrapment Disease (EED), and Bacterial Necrosis (BN) were found to be the threat to the commercial hatchery operation that might responsible for potential larval damages which can be reduced by lowering the stocking densities in larval rearing tank that also increased the survival and growth rate

    NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals

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    Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise robust network to improve the CVDs classification performance.For the identification of the optimal transformation method for noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we propose a novel convolutional recurrent neural network (CRNN) architecture called noise robust cardio net (NRC-Net), which is a lightweight model to classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound.The results of this study indicate that,CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16

    Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

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    Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification
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