Substituting Wood with Nonwood Fibers in Papermaking: A Win-Win Solution for Bangladesh

Bangladesh is facing an acute shortage of fibrous raw materials for the production of pulp and paper. On the other hand, the demand for paper and paper products is increasing day by day. This study reviews the availability and suitability of nonwood raw materials for pulp production in Bangladesh. It shows that Bangladesh has a huge amount of unused jute fiber, which is highly suitable for papermaking in Bangladesh. Other agricultural wastes like rice straw, dhaincha, golpata fronds, cotton stalks, corn stalks, and kash are also available and may be used for some pulp production. Given the different properties of these different nonwood fibers, jute pulp can be used as a reinforcing agent with other nonwood pulps for the production of high quality paper in Bangladesh.Bangladesh, natural fibers, jute, paper making, pulp

Research Notes : Pakistan : Path-coefficient analysis of developmental and yield components in soybean

Abstract: Interrelationships among different characters were determined by simple correlations and path-coefficient analysis using 36 diverse and elite cultivars representing different geographical origin. The results revealed a highly significant positive association of the branches per plant and pods per plant with grain yield. The pods per plant also showed a high direct influence on grain yield. Thus, from this investigation, it is suggested that pods per plant and number of branches per plant are the primary yield components that should be given due emphasis in selecting high yielding genotypes in soybean

The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes

The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets. We describe a transfer learning use case for a domain with a data-starved regime, having fewer than 100 labeled target samples. We evaluate the effectiveness of convolutional feature extraction and fine-tuning of overparameterized models with respect to the size of target training data, as well as their generalization performance on data with covariate shift, or out-of-distribution (OOD) data. Our experiments show that both overparameterization and feature reuse contribute to successful application of transfer learning in training image classifiers in data-starved regimes.Comment: 3 pages, 1 figure, conferenc

Deep Slap Fingerprint Segmentation for Juveniles and Adults

Many fingerprint recognition systems capture four fingerprints in one image. In such systems, the fingerprint processing pipeline must first segment each four-fingerprint slap into individual fingerprints. Note that most of the current fingerprint segmentation algorithms have been designed and evaluated using only adult fingerprint datasets. In this work, we have developed a human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples and 6706 are samples drawn from children from ages 4 to 12. Subsequently, the dataset is used to evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects. Our results reveal the lower performance of NFSEG on slaps from juvenile subjects. Finally, we utilized our novel dataset to develop the Mask-RCNN based Clarkson Fingerprint Segmentation (CFSEG). Our matching results using the Verifinger fingerprint matcher indicate that CFSEG outperforms NFSEG for both adults and juvenile slaps. The CFSEG model is publicly available at \url{https://github.com/keivanB/Clarkson_Finger_Segment

Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction

Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity. Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process. Various types of fingerprint sensors, such as optical, capacitive, and ultrasonic sensors, employ distinct techniques to gather and analyze fingerprint data. This dependency on specific hardware or sensors creates a barrier or challenge for the broader adoption of fingerprint based biometric systems. This limitation hinders the widespread adoption of fingerprint authentication in various applications and scenarios. Border control, healthcare systems, educational institutions, financial transactions, and airport security face challenges when fingerprint sensors are not universally available. To mitigate the dependence on additional hardware, the use of contactless fingerprints has emerged as an alternative. Developing precise fingerprint segmentation methods, accurate fingerprint extraction tools, and reliable fingerprint matchers are crucial for the successful implementation of a robust contactless fingerprint authentication system. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools

Understanding the Dynamics of Fluorescence Emission During Zeolite Detemplation Using Time Resolved Photoluminescence Spectroscopy

Time-resolved photoluminescence spectroscopy (TRPS) shows potential as a sensitive, non-destructive, high throughput, label-free laser-based spectroscopy technique capable of analysing low concentrations of organic species adsorbed on and within zeolite pores. Here we report the results from a study that uses TRPS to characterise photoluminescence (PL) arising from synthesised chabazite framework zeolites at three different stages of the detemplation process (from an uncalcined, partially calcined, and calcined zeolite). Temporal resolution was used to demonstrate the steric confinement effects of OSDA within a zeolite framework and therefore to establish a signature region for determining the presence of the template. Gated spectra comparisons between an uncalcined and a partially calcined zeolite demonstrated the presence of template alongside the proliferation of template-derived combustion products. An analysis of lifetime values demonstrated the ability for TRPS to track depletion of OSDA and establish a characteristic PL spectrum for a clean zeolite