Efficiency of the foreign exchange markets in South Asian Countries

This paper examines the weak form efficiency of the foreign exchange markets in seven SAARC countries using monthly return series for each of these markets over a period of 21 years (1985-2005). We applied a battery of unit root tests and variance ratio tests (individual and multiple) to see whether the return series (and also, the raw data) follow random walk process. Our results suggest that the increments of the return series are not serially correlated. Therefore, we conclude that foreign exchange markets in SAARC countries are weak form efficient.

Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning

Handwriting Recognition has been a field of great interest in the Artificial Intelligence domain. Due to its broad use cases in real life, research has been conducted widely on it. Prominent work has been done in this field focusing mainly on Latin characters. However, the domain of Arabic handwritten character recognition is still relatively unexplored. The inherent cursive nature of the Arabic characters and variations in writing styles across individuals makes the task even more challenging. We identified some probable reasons behind this and proposed a lightweight Convolutional Neural Network-based architecture for recognizing Arabic characters and digits. The proposed pipeline consists of a total of 18 layers containing four layers each for convolution, pooling, batch normalization, dropout, and finally one Global average pooling and a Dense layer. Furthermore, we thoroughly investigated the different choices of hyperparameters such as the choice of the optimizer, kernel initializer, activation function, etc. Evaluating the proposed architecture on the publicly available 'Arabic Handwritten Character Dataset (AHCD)' and 'Modified Arabic handwritten digits Database (MadBase)' datasets, the proposed model respectively achieved an accuracy of 96.93% and 99.35% which is comparable to the state-of-the-art and makes it a suitable solution for real-life end-level applications.Comment: Accepted in 25th ICCIT (6 pages, 4 tables, 4 figures

Evaluating Suitability of Glutaraldehyde Tanning in Conformity with Physical Properties of Conventional Chrome-Tanned Leather

Leather manufacturing involves a number of unit processes, out of which tanning is the most important in so far as it converts the putrescible hides/skins into non-putrescible leather. In this study, glutaraldehyde has been exploited as a means to reduce the use of basic chromium sulfate for the production of quality shoe upper crust leather. The paper consists in studying the physical properties of aldehyde-tanned leather and chrometanned leather. The aim is to find out the possibility of replacing the wet-blue leather, containing Cr(III) salts, with the glutaraldehyde-tanned wet-white leather. The physical properties of the aldehyde-tanned leather were evaluated, analyzed and compared with the conventional chrome-tanned shoe upper crust leather. Statistical analysis illustrated that the tensile strength, the percentage of elongation, stitch tear strength, Baumann tear strength and grain crack strength of the leather was 211±1 kg/cm2, 38±0.5 %, 89±0.11 kg/cm, 63±0.4 kg/cm and 23±0.4 kg respectively. It was observed that the property of the experimental leather was quite comparable with the conventional chrome-tanned leather and able to meet the requirements of the shoe upper crust leather after re-tanning. The shrinkage temperature of the experimental tanned leather was found to be 87 °C, lower than that of corresponding control, which indicates lesser tanning power of aldehyde. However, the morphology of the aldehyde-tanned leather was quite akin with the conventional leather. This study suggests that using glutaraldehyde in the tanning process in order to minimize the chromium load in the tanning and the re-tanning process during the production of shoe upper crust leather reduces the generation of toxic waste and its impact on the environment

Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech

Immunofluorescence pattern of antinuclear antibody and its association with autoantibody profile in systemic lupus erythematosus

Background: Antinuclear antibody (ANA) is useful in the diagnosis of systemic lupus erythematosus (SLE). Association of specific autoantibodies with the immunofluorescence pattern of ANA in SLE as noted in Western literature has been taken as reference in all over the world. However, in Bangladesh such research work or data correlating the autoantibodies and their ANA patterns is inadequate. Objective: To identify an association between immunofluorescence patterns of antinuclear antibody on HEp-2 cell and more specific antinuclear reactivities (e.g. anti-dsDNA and anti-extractable nuclear antigen) in the serum samples of SLE patients.Methods: Serum samples of 37 SLE patients who were diagnosed by ARA (American Rheumatism Association) classification criteria and laboratory tests, attending at lupus clinic of Bangabandhu Sheikh Mujib Medical University (BSMMU) during the study period of six months were subjected for ANA testing by Indirect Imrnunofluorescence (IIF) on HEp-2 cell, anti-dsDNA by ELISA and anti- extractable nuclear antigen (anti-ENA) by Dot Immunoblot. Dot blot strips were tested for anti-Sm, anti-RNP, anti-SSA/Ro, and anti-SSB/La. Results: Out of 37 SLE patients 32 (86.5%) cases were ANA positive by IIF on HEp-2 cell. ANA positive sera exhibited three fluorescence patterns such as speckled (43.7%), peripheral (34.3%) and homogenous pattern (21.8%). Peripheral pattern (100%) was strongly associated with anti-dsDNA (p<0.05) and homogenous pattern (85.7%) was also predominantly associated with anti-dsDNA (p<0.05). Speckled pattern (85.6%) was significantly associated with anti-ENA (p<0.05). Anti-dsDNA was positive in 75% of SLE cases and majority (45.8%) of which showed peripheral pattern whereas anti-ENA was positive in 48.6% cases and majority (70.5%) of which showed speckled pattern. The most commonly identified antinuclear autoreactivity was directed towards anti-RNP (22.2%) then anti-Sm (16.6%), anti-SSA (16.6%) and anti-SSB (11.1 %). Multiple anti-ENA reactivities were identified in 33.3% cases. Conclusion: Peripheral and homogenous pattern is strongly associated with anti-dsDNA therefore may be predicted that patients have active SLE and speckled pattern may predict anti-ENA (specially ribonucleoprotiens). Thus, ANA-IIF method may suffice and probably reduce the expense of detailed immunological work-up with minimal loss in diagnostic accuracy

Electrochemiluminescence nanoimmunosensor for CD63 protein using a carbon nanochips/iron oxide/nafion-nanocomposite modified mesoporous carbon interface

The detection of extracellular vesicles, or exosomes are important mediators in intercellular communication and often play a role in cancer progression. CD63 is a key exosomal protein due to its distinctive cellular functions and association with many cancers. This describes a label-free electrochemiluminescence (ECL) nanoimmunosensor for the detection of CD63 protein over mesoporous carbon screen-printed electrode (MC-SPE) modified with novel nanocomposite of carbon nanochips (CNCs), iron oxide (Fe3O4) and nafion (NAF) . Fourier-transform infrared spectroscopy and field emission scanning electron microscopy were used to analyse nanocomposite. All the analytical performance of fabricated CD63 immunosensor were conducted applying ECL. In spite of the simple fabrication strategies utilized, the fabricated immunosensor showcased a broad linear range to detect CD63 from 100 fg mL-1 to 10 ng mL-1, with a limit of detection of 100 fg mL-1, excellent selectivity, interference-resistance capability and potential to detect CD63 in real clinical samples

Recent advancement in sensitive detection of carcinoembryonic antigen using nanomaterials based immunosensors

Carcinoembryonic antigen (CEA) is a prominent cancer biomarker that allows for early diagnosis of various cancers. Present immunoassays techniques help quantify such target molecules in test samples via anti-antibody reaction. Despite their rapid usage, conventional immunoassay techniques demonstrate several limitations that can be easily overcome by employing nanomaterials in sensing assays. Thus, nanomaterial-based immunosensors have gained steady attention from the scientific community owing to their high specificity and low detection limit. Various nanomaterials like platinum, gold, silver and carbon exhibit exceptional properties have allowed promising results in the detection and diagnostics of CEA. Thus, the present review aims to explore the significance and the recent developments of nanomaterial-based biosensors for detecting CEA biomarkers with high sensitivity, selectivity, and specificity. After a brief introduction, we discussed the fundamentals of immunosensors immobilization strategies and common nanomaterials. In the next section, we highlighted the recent advances in the common immunosensors detection approaches for CEA alone and simultaneous detection of CEA with other biomarkers detection. Finally, we concluded the review by discussing the future perspectives of this promising field of biomarkers detection

Carbon stock and sequestration potential of an agroforestry system in Sabah, Malaysia

Total aboveground carbon (TAC) and total soil carbon stock in the agroforestry system at the Balung River Plantation, Sabah, Malaysia were investigated to scientifically support the sustaining of natural forest for mitigating global warming via reducing carbon in the atmosphere. Agroforestry, monoculture, and natural tropical forests were investigated to calculate the carbon stock and sequestration based on three different combinations of oil palm and agarwood in agroforestry systems from 2014 to 2018. These combinations were oil palm (27 years) and agarwood (seven years), oil palm (20 years) and agarwood (seven years), and oil palm (17 years) and agarwood (five years). Monoculture oil palm (16 years), oil palm (six years), and natural tropical forest were set as the control. Three randomly selected plots for agroforestry and monoculture plantation were 0.25 ha (50 × 50 m), respectively, whereas for the natural tropical forest it was 0.09 ha (30 × 30 m). A nondestructive sampling method followed by the allometric equation determined the standing biomass. Organic and shrub layers collected in a square frame (1 × 1 m) were analyzed using the CHN628 series (LECO Corp., MI, USA) for carbon content. Soil bulk density of randomly selected points within the three different layers, that is, 0 to 5, 5 to 10, and 10 to 30 cm were used to determine the total ecosystem carbon (TEC) stock in each agroforestry system which was 79.13, 85.40, and 78.28 Mg C ha−1 , respectively. The TEC in the monoculture oil palm was 76.44 and 60.30 Mg C ha−1 , whereas natural tropical forest had the highest TEC of 287.29 Mg C ha−1 . The forest stand had the highest TEC capacity as compared with the agroforestry and monoculture systems. The impact of planting systems on the TEC showed a statistically significant difference at a 95% confidence interval for the various carbon pools among the agroforestry, monoculture, and natural tropical forests. Therefore, the forest must be sustained because of its higher capacity to store carbon in mitigating global warming