Reasons behind the Drop out of Street Children from Non-formal Primary Education Program

The aim of this study is to find out the inner reasons of dropping out of street children from non-formal primary education program. With this study also we can have a figure of the lifestyle, needs and social conditions of street children. To have the findings of my study qualitative research method will be conducted. In accordance with the study objectives from the large areas only 1 authority member, 1 teacher, 5 admitted children and 5 dropped out children were selected as sample. I designed open-ended questionnaire and semi-structured interview to collect my data. The thematic analysis method was used for data analysis. I have a variation in findings because of using questionnaire for teacher and authority and interview for children. Moreover in commonly I can say the demographic conditions, lacking’s of parental care and guidance, lacking’s of Interest and awareness, lacking’s of facilities, lacking’s of economic support, effects of misguidances, lacking’s of proper relationship among program stuff with parents and children’s are major problems that disturbed the street children to continue their education. Considering the findings it can be recommended that increasing of education program or giving support with educational equipment is not enough to continue education in this marginal sector of society. It is more important to grow consciousness, make them understood the importance of education, giving them a link of better future through education and make a reliable environment between education program and parents-children. Keywords: Street Children, Non-formal Education Program, Drop ou

Ethnobotanical study of the family Marantaceae R. Br in Bangladesh Agricultural University Botanical Garden

The Marantaceae family is a diverse group of plants that has drawn the interest of scientists and researchers worldwide due to their distinctive morphological characteristics, ecological and economic relevance. The Bangladesh Agricultural University Botanical Garden is home to an abundance of Marantaceae species, making it a useful resource for examining the diversity and significance of this plant family. This present study was designed to survey and document the family Marantaceae with an overview of the family emphasizing its morphological, economic, and ethnobotanical relevance based on a literature review. During the study, we found 25 species (two of which have two varieties each) belonging to 8 genera of which Goeppertia contributed the most species, with 16, followed by Maranta with 4 (including varieties) and Thalia with 2; the remaining 5 genera each contributed one species. Our findings reveal the remarkable diversity and significance of Marantaceae plants in this region, highlighting the necessity for their conservation and protection

Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach

Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications

Pattern of Pharmacotherapy of Patients Having Ischemic Heart Disease at a Specialized Hospital in Dhaka, Bangladesh: A Survey Based Study on Patients Discharged from Hospital

Aims: Study on pharmacotherapeutic pattern on cardiovascular patients is rarely done. Patient’s demography, drug usage and its clinical outcome are the basis for the assessment of cardiac treatment. The aim of this study to analyze the demography of patients of ischemic heart disease along with drug usage and current trends of practice in Bangladesh. Methods: This study was carried out over a period of two months at different units of NICVD, situated at Dhaka, Bangladesh. A structured questionnaire was prepared to collect necessary data from patients. Descriptive statistics was used to represent the data. Results: A total 363 discharged patients were interviewed followed by the verification of their discharge report and other medical documents to obtain necessary information. Out of 363 patients, frequency of male patients were high (74.66%, N= 271) than female patients (25.34%, N=92). There is a trends of ischemic heart disease development after 40 years of age and found significant in this study (P< 0.05). In this study, 83.19% of total patients were above 40 years of age. We found a significant number of patients also had diabetes, asthma and chronic kidney disease. Treatment approach of ischemic heart disease includes pharmacotherapy, revascularization and percutaneous coronary intervention. 48 patients (13.22%) out of 363 went for revascularization and percutaneous coronary intervention was done to 25 patients (6.89%). The goal of Pharmacotherapy is to reduce blood cholesterol level, prevention of further platelet aggregation, reduction of angina and control of heart rate. In our study, we found that, statins, anti platelet and anti angina/anti ischemic drugs are core in the treatment of ischemic heart disease. Beta blocker, ACE inhibitor or ARB, CCB is commonly added to standard therapy to reduce mortality and for better therapeutic outcome. Among statins, the frequency of use of atorvastatin (87.93%), combination of clopidogrel and aspirin (73.90%) among anti platelet agents, combination of trimetazidine and nitroglycerine (61.56%) among anti angina/anti ischemic agents were highest. In our study, we found that bisoprolol was most commonly prescribed by the physicians among other beta blockers. Conclusion: The outcome of this study will be helpful for young professionals, general physicians and other professionals involved in the health care setting for the rational use of drugs and to formulate effective strategy for the management of ischemic heart disease

Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach

Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Brain–computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set

Analysis of Auditory Evoked Potential Signals Using Wavelet Transform and Deep Learning Techniques

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. One of the best ways to solve this problem is early and successful hearing diagnosis using electroencephalogram (EEG). Auditory Evoked Potential (AEP) seems to be a form of EEG signal with an auditory stimulus produced from the cortex of the brain. This study aims to develop an intelligent system of auditory sensation to analyze and evaluate the functional reliability of the hearing to solve these problems based on the AEP response. We create deep learning frameworks to enhance the training process of the deep neural network in order to achieve highly accurate hearing deficit diagnoses. In this study, a publicly available AEP dataset has been used and the responses have been obtained from the five subjects when the subject hears the auditory stimulus in the left or right ear. First, through a wavelet transformation, the raw AEP data is transformed into time-frequency images. Then, to remove lower-level functionality, a pre-trained network is used. Then the labeled images of time-frequency are then used to fine-tune the neural network architecture’s higher levels. On this AEP dataset, we have achieved 92.7% accuracy. The proposed deep CNN architecture provides better outcomes with fewer learnable parameters for hearing loss diagnosis

A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology

The Classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals

Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.Peer reviewe