Application of Yager’s Fuzzy Logic in Sociological Research: An Instance of Potential Payoff

This article exemplifies Yager’s theory of fuzzy logic for interpersonal communication to the area of social research. Taking the dilemma between qualitative and quantitative approaches into the account, there is an anticipation to make a merge between these two. There is an enormous prospect to turn up scientists’ philanthropic innovations if they could use fuzzy logic in social science researches! However, by using fuzzy logic in sociological research there is a great deal of opportunity to study the social facts related to poverty, consumption, employment, intersubjectivity, social capital, environment, gender etc. How can we use Yager’s theory of Fuzzy Logic to analyze the relationship between social capital and labor market partcicpation? From the experiential connection in Bangladesh society, I try to seek this answer using a hypothetical quantification of attributes

Poverty Profiles of Sylhet City Corporation: An MPI Approach

Does income measure of poverty explain it meticulously? To seek this answer we claim poverty is not a unidimensional phenomenon rather it adheres multidimensionality. Sen (2000) views poverty as the deprivation of certain basic capabilities, which varies from elementary physical nourishment to the community life. However, targeting slum dwellers, this article sought to advance multidimensional poverty measures in SCC (Sylhet City Corporation). The study adopts a mixed method approach to examine so. Finding shows that, there are some variations in the percentage of poor households. In terms of income and expenditure 60% households are identified as poor but in MPI number increases to 75%. Data from in-depth interview exhibits that respondents feel themselves as income poor. Some of them consider deprivation of education is the consequence of that income poverty. In addition, few respondents dimple that health problems and physical disabilities mingle their poverty experiences

Diffusion Weighted Imaging and Grading of Brain Tumours

Background: Most prevalent primary cerebral tumours are meningiomas. The other frequent intracranial tumours are pituitary adenomas, which are benign, and gliomas, which are intra-axial brain tumours. The objective of this study is to understand the importance of DW MRI imaging with standard b-value in differentiating presurgical grading of the brain tumour.Design: A total of 24 DWI patients, including 12 meningiomas, 8 gliomas, and 4 pituitary adenomas, were included in this retrospective analysis.Method: The Stejskal-Tanner equation is used to analyse the ADCmean, ADCmin, and ADCmax values from the healthy and tumour core that are obtained out from area of interest (ROI).Result: The ADCmean value of Gliomas ranges from 0.09 x 10-3 mm2s-1 to 0.99 x 10-3 mm2s-1 with a median value of 0.25 x 10-3 mm2s-1. ADCmean value 1.82 x 10-3 mm2/s (sensitivity: 67%. Specificity: 81.8%) and 0.94 x 10-3 mm2/s (sensitivity: 75%. specificity: 81.3%) can discriminate grade II –IV meningioma from grade II-IV glioma.Conclusion: The ADC and its threshold levels offer crucial details on the grades, consistency, and characterization of tumour, aiding accurate diagnosis and therapy

Role of presynaptic glutamate receptors in Modulation of long term synaptic plasticity (LTP) in inhibitory synapes of visual pyramidal neuron after epilepsy: a whole cell patch clamp recording

Purpose: Results from animal models has pointed out that presynaptic NMDA (pre-NMDA) receptor is present in visual cortex and pre-NMDA has significant role in epilepsy. Therefore we have tested the role of pre-NMDA receptor for GABA-ergic transmission in the visual cortex in acute condition of epilepsy. Methods: Using the pilocarpine mouse model, visual cortical slices were prepared immediately after having seizure (Pilo.group), and spontaneous miniature IPSCs (miPSCs) were recorded from pyramidal neurons of layer II/III in visual cortex. Amplitudes and frequencies of miPSC were analyzed and compared with those in age-matched saline-injected controls. Results: Frequency of miniature IPSCs (miPSCs) were significantly increased in saline controls compared to Pilo. Amplitude has no significant difference among groups using NBQX only or NBQX and MK801 combined use. Conclusion: In acute condition of epilepsy, there is no significant role of presynaptic NMDA receptor for GABA-ergic neurotransmitter release which is a indicator of long term potentiation (L TP) of inhibitory synapses

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.MethodsEEG single trials are decomposed with discrete wavelet transform (DWT) up to the level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.ResultsThe proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60, sensitivities 93.55, specificities 94.85, precisions 92.50, and area under the curve (AUC) 0.93 using SVM and k-NN machine learning classifiers.ConclusionThe proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds

Assessing neuroplasticity using magnetoencephalography (MEG) in patient with left-temporo-parietal pilocytic astrocytomas treated with endoscopic surgery

Neuroplasticity has been subjected to a great deal of research in the last century. Recently, significant emphasis has been placed on the global effect of localized plastic changes throughout the central nervous system, and on how these changes integrate in a pathological context. The present study aimed to demonstrate the functional cortical reorganization before and after surgery using magnetoencephalography (MEG) in a participant with brain tumor. Results of Visual Evoked Magnetic Field (VEF) based on functional MEG study revealed significantly different of MEG N100 waveforms before and after surgery. Larger and additional new locations for visual activation areas after the surgery were found suggesting neuroplasticity. The present study highlight a physiological plasticity in a teenage brain and the alterations regarding neural plasticity and network remodeling described in pathological contexts in higher-order visual association areas

Coordination and Three-Stage Supply Chain Optimization of Agricultural Products in Bangladesh Under Uncertainties

Abstract- In this study presents three stage supply chain network (SCN) coordination and profit optimization of agricultural products considering several uncertainties. Most of the agricultural products are in general cost expensive with high risk in probability due to its fluctuating prices. To developed a Mixed Integer Linear Programming (MILP) model and analyze the situation of insufficient production capacity for the producer as the reason for shortages. In this study to investigated supply chain network (SCN) are two distinct freelance supply organizations. SCN management has the difficulties for the disconnected and freelance economic people. Further, fast technological changes and high fight build SCN a lot of complicated. The problem of locating distribution centers (DCs) is one among the foremost necessary problems in design of SCN. The models are applied to a real case of optimization the profit before and after coordination and also to analyze the sensitivity under demand and cost uncertainty. The MILP models consider the facilities are coordinated by mutually sharing information with each other among producer, retailer and distributor. The formulated MILP model is solved by using a mathematical programming language (AMPL) and results obtained by appropriate solver MINOS. Numerical example with the sensitivity of various parameters has been deployed to validate the models. Results show that after coordination, the individual profits could be increased without any extra investment

Observation of tumour-induced reorganization in structural and functional architecture of the brain in three pre-surgical patients with left frontal-temporal brain tumour: a combination of MEG, DTI and neuropsychological assessment

Visual function is mainly located within the bilateral hemisphere of the occipital lobes of the brain. However, our functional magnetoencephalography (MEG) result has demonstrated the reorganization of brain activity in the occipital area in patients with left-sided brain tumour. The results showed that brain laterality changes from bilateral to unilateral activation of the occipital area. Right occipital area (contralateral areas to the tumour), shows increase intensity of activation. Diffusion tensor imaging (DTI) with fibre tracking was performed to further investigate this brain laterality modification and the findings confirmed there is an alteration in the left hemisphere fibre optic tracts. This functional modification and changes of the brain laterality and optic tracts in the brain is suspected to be the result of tumour growth induced changes. The present observation will be discussed in term of the mechanism of tumour induced reorganization and changes with the corroborating evidence from MEG, DTI and neuropsychological assessment

Estimation of the SARS-CoV-2 specific reproduction number in SAARC countries: A 60-days Data-driven analysis

Novel coronavirus disease-2019 (COVID-19) was acknowledged as a global pandemic by WHO, which was first observed at the end of December 2019 in Wuhan city, China, caused by extreme acute respiratory syndrome coronavirus2 (SARS-CoV-2). According to the Weekly operation Update on COVID-19 (November 13, 2020) of the World Health Organization, more than 53 million confirmed cases are reported, including 1.3 million deaths. Various precautionary measures have been taken worldwide to reduce its transmission, and extensive researches are going on. The purpose of this analysis was to determine the initial number of reproductions (Ro) of the coronavirus of SAARC countries named Afghanistan, Bangladesh, India, Pakistan, Bhutan, Nepal, the Maldives, and Sri-Lanka for the first 60 days as the growth is exponential in the early 60 days. The reproduction numbers of coronavirus for Afghanistan, Bangladesh, India, Pakistan, Bhutan, the Maldives, Nepal, and Sri Lanka are 1.47, 3.86, 2.07, 1.43, 1.31, 3.22, 1.75, and 2.39 respectively. The basic reproduction number (R0) 3.86 for Bangladesh and 1.31 for Bhutan indicated that up to 60-days of the outbreak COVID-19, the epidemic was more severe in Bangladesh and less severe in Bhutan among all the SAARC countries. Our predictions can be helpful in planning alertness and taking the appropriate measures to monitor it

Modeling on population growth and its adaptation: A comparative analysis between Bangladesh and India

The biggest challenge in the world is population growth and determining how society and the state adapt to it as it directly affects the fundamental human rights such as food, clothing, housing, education, medical care, etc. The population estimates of any country play an important role in making the right decision about socio-economic and population development projects. Unpredictable population growth can be a curse. The purpose of this research article is to compare the accuracy process and proximity of three mathematical model such as Malthusian or exponential growth model, Logistic growth model and Least Square model to make predictions about the population growth of Bangladesh and India at the end of 21st century. Based on the results, it has been observed that the population is expected to be 429.32(in million) in Bangladesh and 3768.53 (in million) in India by exponential model, 211.70(in million) in Bangladesh and 1712.94(in million) in India by logistic model and 309.28 (in million) in Bangladesh and 2686.30 (in million) in India by least square method at the end of 2100. It was found that the projection data from 2000 to 2020 using the Logistic Growth Model was very close to the actual data. From that point of view, it can be predicted that the population will be 212 million in Bangladesh and 1713 million in India at the end of the 21st century. Although transgender people are recognized as the third sex but their accurate statistics data is not available. The work also provides a comparative scenario of how the state has adapted to the growing population in the past and how they will adapt in the future