Classroom Culture and its Importance in the Post Methods Era for Designing Pedagogy in Bangladesh

Classroom culture is an effective determinant for pedagogy to be effective in a particular context. In order to arrive at an appropriate pedagogy in the post methods era the classroom teachers, researchers and curriculum designers must investigate classroom culture. ELT practitioners of various countries are dissatisfied with the effectiveness of the borrowed pedagogy as the pedagogy has not been designed on the basis of the classroom culture as well as of the wider cultural and contextual realities of a second language learning situation. This dissatisfaction is severe in Bangladesh. So, it has been a must to reconstruct ELT practices. In this regard, some concepts of Post Method Pedagogy can help a lot for formulating a new process of learning English. The present study has been undertaken with a view to offering some new insights in the light of some aspects of Post Methods Pedagogy

Rethinking Probability of Eclectic Approach in the Post-Method Era: A Study for Making English Teaching-Learning Effective in Bangladesh

For effective English teaching and learning in Bangladesh probability of Eclecticism can be rethought To do so the concept of the post-method era is to be implemented Both Eclecticism and post-method pedagogy put much emphasis on the effectiveness of pedagogy For a context like Bangladesh it is very important to formulate culture and context-sensitive need-based pedagogy as there is a lack of effective pedagogy As the earlier pedagogy failed to produce competent English language users in Bangladesh it is time to formulate need-based appropriate pedagogy The present study has been undertaken to consider the existing teaching-learning realities of Bangladesh in the post-method era and suggest effective pedagogy based on the concept of Eclectic Approac

Formulating Oscillator-Inspired Dynamical Systems to Solve Boolean Satisfiability

Dynamical systems can offer a novel non-Boolean approach to computing. Specifically, the natural minimization of energy in the system is a valuable property for minimizing the objective functions of combinatorial optimization problems, many of which are still challenging to solve using conventional digital solvers. In this work, we formulate two oscillator-inspired dynamical systems to solve quintessential computationally intractable problems in Boolean satisfiability (SAT). The system dynamics are engineered such that they facilitate solutions to two different flavors of the SAT problem. We formulate the first dynamical system to compute the solution to the 3-SAT problem, while for the second system, we show that its dynamics map to the solution of the Max-NAE-3-SAT problem. Our work advances understanding of how this physics-inspired approach can be used to address challenging problems in computing

Designing a K-state P-bit Engine

Probabilistic bit (p-bit)-based compute engines utilize the unique capability of a p-bit to probabilistically switch between two states to solve computationally challenging problems. However, when solving problems that require more than two states (e.g., problems such as Max-3-Cut, verifying if a graph is K-partite (K>2) etc.), additional pre-processing steps such as graph reduction are required to make the problem compatible with a two-state p-bit platform. Moreover, this not only increases the problem size by entailing the use of auxiliary variables but can also degrade the solution quality. In this work, we develop a unique framework for implementing a K-state (K>2) p-bit engine. Furthermore, from an implementation standpoint, we show that such a K-state p-bit engine can be implemented using N traditional (2-state) p-bits, and one multi-state p-bit -- a novel concept proposed here. Augmenting traditional p-bit platforms, our approach enables us to solve an archetypal combinatoric problem class requiring multiple states, namely Max-K-Cut (K=3, 4 shown here), without using any additional auxiliary variables. Thus, our work fundamentally advances the functional capability of p-bit engines, enabling them to solve a broader class of computationally challenging problems more efficiently

Agro-Morphological, Physico-Chemical and Molecular Characterization of Rice Germplasm with Similar Names of Bangladesh

AbstractThirty-one duplicate and similar named rice germplasms of Bangladesh were studied to assess the genetic variation for the agro-morphological and physico-chemical traits and simple sequence repeat banding patterns during 2009–2012 at Bangladesh Rice Research Institute. The range of variations within the cultivar groups showed higher degree. The principal component analysis showed that the first five components with vector values > 1 contributed 82.90% of the total variations. The cluster analysis grouped the genotypes into four clusters, where no duplicate germplasm was found. The highest number (11) of genotypes was constellated in cluster I and the lowest (3) in cluster II. The intra- and inter-cluster distances were the maximum in cluster I (0.93) and between clusters I and IV (24.61), respectively, and the minimum in cluster IV (0.62) and between clusters I and III (5.07), respectively. The cluster mean revealed that the crosses between the genotypes of cluster I with those of clusters II and IV would exhibit high heterosis for maximum good characters. A total of 350 alleles varied from 3 (RM277) to 14 (RM21) with an average of 7.8 per locus were detected at 45 microsatellite loci across the 31 rice accessions. The gene diversity ranged from 0.48 to 0.90 with an average of 0.77, and the polymorphism information content values from 0.44 (RM133) to 0.89 (RM206) with an average of 0.74. RM206, RM21, RM55, RM258 and RM433 were considered as the best markers on the basis of their higher polymorphism information content values. The dendrogram from unweighted pair-group method with arithmetic average clustering also classified the genotypes into four groups, where group IV comprised of 20 genotypes and group III of one genotype, but no duplicate was found. Finally, similar and duplicate named rice germplasms need to be conserved in gene bank as are distinct from each other

A robust ECG denoising technique using variable frequency complex demodulation

Background and Objective Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Incidence and characteristics of maternal mortality: a retrospective study in Dhaka medical college

Background: Maternal mortality remains a significant public health challenge globally, particularly in low-resource settings like Bangladesh. This study aims to analyze the incidence and characteristics of maternal mortality at Dhaka Medical College Hospital, a major tertiary care center in Bangladesh. Methods: This retrospective observational study was conducted, reviewing 10,592 birth records from July 2009 to June 2010. The study focused on maternal deaths during this period, identifying 189 cases. Data on age, socioeconomic status, parity, antenatal care practices, and causes of mortality were analyzed Results: The maternal mortality rate was found to be 1.78%. The majority of deaths occurred in younger women, with 25.40% in the 16-20 age group and 36.51% in the 21-25 age group. A significant majority (80.95%) of the deaths occurred among women from low socioeconomic backgrounds. Regarding parity, the highest mortality was observed in women with 1-2 children (39.68%). Antenatal care was notably deficient, with 75.66% of participants not receiving any. The leading causes of maternal mortality were eclampsia (31.75%) and obstetric haemorrhage (30.16%). Conclusions: The study highlights a high incidence of maternal mortality among younger women and those from low socioeconomic backgrounds, with eclampsia and obstetric haemorrhage being the predominant causes. The lack of antenatal care is a critical concern. These findings underscore the need for improved antenatal care services, emergency obstetric care, and targeted interventions to address socioeconomic disparities in maternal health