Modelling the Effect of Resin-Finishing Process Variables on the Dimensional Stability and Bursting Strength of Viscose Plain Knitted Fabric Using a Fuzzy Expert System

The application of cross-linking resin is an effective method for improving and controlling dimensional stability, such as the shrinkage of viscose single jersey knits. However, such treatment often leads to a significant deterioration in the bursting strength of treated fabrics. In this regard, resin treatment using a softening agent can be an additional potential solution for retaining the bursting strength of treated fabrics. Resin treatment is one kind of chemical finishing process that inhibits cellulosic textile fibre swelling during wetting, provides fibre resistance to deformation and prevents shrinkage. The key objective of this study was to model the effect of resin-finishing process variables for predicting the shrinkage control and bursting strength of viscose single jersey knitted fabrics. The MATLAB (Version 8.2.0.701) fuzzy expert system was used to model the optimum resin and softener concentrations, as well as the best curing time for the prediction of maximum shrinkage control with a minimum loss in fabric bursting strength. The optimal process variables were found to be a resin concentration of 75 g/l, a softener concentration of 45 g/l and a curing time of 225 seconds. The fuzzy expert model developed in this study was validated using experimental data. It was found that the model has the ability and accuracy to predict fabric shrinkage and bursting strength effectively in the non-linear field

Medicinal potential of isoflavonoids: Polyphenols that may cure diabetes

In recent years, there is emerging evidence that isoflavonoids, either dietary or obtained from traditional medicinal plants, could play an important role as a supplementary drug in the management of type 2 diabetes mellitus (T2DM) due to their reported pronounced biological effects in relation to multiple metabolic factors associated with diabetes. Hence, in this regard, we have comprehensively reviewed the potential biological effects of isoflavonoids, particularly biochanin A, genistein, daidzein, glycitein, and formononetin on metabolic disorders and long-term complications induced by T2DM in order to understand whether they can be future candidates as a safe antidiabetic agent. Based on in-depth in vitro and in vivo studies evaluations, isoflavonoids have been found to activate gene expression through the stimulation of peroxisome proliferator-activated receptors (PPARs) (α, γ), modulate carbohydrate metabolism, regulate hyperglycemia, induce dyslipidemia, lessen insulin resistance, and modify adipocyte differentiation and tissue metabolism. Moreover, these natural compounds have also been found to attenuate oxidative stress through the oxidative signaling process and inflammatory mechanism. Hence, isoflavonoids have been envisioned to be able to prevent and slow down the progression of long-term diabetes complications including cardiovascular disease, nephropathy, neuropathy, and retinopathy. Further thoroughgoing investigations in human clinical studies are strongly recommended to obtain the optimum and specific dose and regimen required for supplementation with isoflavonoids and derivatives in diabetic patients

Bangladeshi medicinal plant dataset

Medicinal plants have been used to treat diseases since ancient times. Plants used as raw materials for herbal medicine are known as medicinal plants [2]. The U. S. Forest Service estimates that 40% of pharmaceutical drugs in the Western world are derived from plants [1]. Seven thousand medical compounds are derived from plants in the modern pharmacopeia. Herbal medicine combines traditional empirical knowledge with modern science [2]. A medicinal plant is considered an important source of prevention against various diseases [2]. The essential medicine component is extracted from different parts of the plants [8]. In underdeveloped countries, people use medicinal plants as a substitute for medicine. There are various species of plants in the world. Herbs are one of them, which are of different shapes, colors, and leaves [5]. It is difficult for ordinary people to recognize these species of herbs. People use more than 50000 plants in the world for medicinal purposes. There are 8000 medicinal plants in India with evidence of medicinal properties [7]. Automatic classification of these plant species is important because it requires intensive domain knowledge to manually classify the proper species. Machine learning techniques are extensively used in classifying medicinal plant species from photographs, which is challenging but intriguing to academics. Artificial Neural Network classifiers’ effective performance depends on the quality of the image dataset [4]. This article represents a medicinal plant dataset: an image dataset of ten different Bangladeshi plant species. Images of medicinal plant leaves were from various gardens, including the Pharmacy Garden at Khwaja Yunus Ali University and the Khwaja Yunus Ali Medical College & Hospital in Sirajganj, Bangladesh. Images were collected by taking pictures with high-resolution mobile phone cameras. Ten medicinal species, 500 images per species are included in the data set, namely, Nayantara (Catharanthus roseus), Pathor kuchi (Kalanchoe pinnata), Gynura procumbens (Longevity spinach), Bohera (Terminalia bellirica), Haritaki (Terminalia chebula), Thankuni (Centella asiatica), Neem (Azadirachta indica), Tulsi (Ocimum tenniflorum), Lemon grass (Cymbopogon citratus), and Devil backbone (Euphorbia tithymaloides). This dataset will benefit researchers applying machine learning and computer vision algorithms in several ways. For example, training and evaluation of machine learning models with this well-curated high-quality dataset, development of new computer vision algorithms, automatic medicinal plant identification in the field of botany and pharmacology for drug discovery and conservation, and data augmentation. Overall, this medicinal plant image dataset can provide researchers in the field of machine learning and computer vision with a valuable resource to develop and evaluate algorithms for plant phenotyping, disease detection, plant identification, drug development, and other tasks related to medicinal plants

Electric and lighting energy audit: a case study of selective commercial buildings in Dhaka

This conference paper was presented in the IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015; Bangladesh University of Engineering and Technology (BUET) Dhaka; Bangladesh; 19 December 2015 through 20 December 2015 [© 2015 Institute of Electrical and Electronics Engineers Inc.] The conference paper's definite version is available at: http://10.1109/WIECON-ECE.2015.7443923Energy audit can be one of the fastest and cheapest solutions to mitigate the gap between energy demand and supply since it identifies the energy loss and improvement areas. The paper shows a detailed analysis on building energy audit of some commercial buildings in Dhaka, evaluate and provide techniques to reduce energy loss, and a tool that has been developed to carry out energy audit of commercial buildings. The tool is developed using Microsoft Visual Basic Application and named »EnergyWise». Data entered by user forms is processed, summarized and transferred automatically to the excel spread sheets. The tool identifies the areas of electrical load consumption and their share as a percentage of the total load. The study shows that the electrical energy use in commercial buildings in Dhaka are quite inefficient and the consumption of electrical energy can be significantly reduced; up to 8%-15% energy reduction in electrical equipment and up to 28%-45% in lighting by replacing them with more efficient components

Species classification of brassica napus based on flowers, leaves, and packets using deep neural networks

Deep learning (DL) has gradually taken the lead as the most effective approach in the agricultural fields due to the early identification and classification of plant species and diseases for improving the quality of crop production because of recent technological breakthroughs, which have had a significant impact on agriculture. Plenty of complicated problems in farming, including species classification, plant disorder identification, yield approximation, and weather and soil moisture prediction, are made simple using deep neural networks. Thus, this proposed study aims to classify Brassica Napus (B. Napus) rapeseed species based on their most significant features, like flowers, leaves, and packets. The study has adopted two types of rapeseed such as B. Rapa and B. Alba. Five contemporary deep learning-based Convolutional Neural Network (CNN) models have also been assessed for distinguishing rapeseed species. These models are DenseNet201, VGG19, InceptionV3, Xception, and ResNet50. Initially, the researchers collected data from the agricultural field, and then image pre-processing is performed to create our dataset. After that, CNN models were applied to this dataset and enumerated the experimental data accordingly. Our DenseNet201 model successfully classified both species with the highest accuracy of 100% for flowers and 97% for both packets and leaves. A comprehensive analysis with companion studies confirmed the efficacy of our preferred paradigm for the near future. Nevertheless, future studies will compare these methodologies to data from a separate metabolomics dataset from comparable crops

An advanced Bangladeshi local fish classification system based on the combination of deep learning and the internet of things (IoT)

Fish classification leads to the automated machine-based fish separation system. In terms of classification and real-time data monitoring, deep learning and the Internet of Things (IoT) each provides an efficient solution. This paper focuses on the development of an embedded system based on the principles of Deep Learning and IoT. The proposed methodology is classified into interconnected parts. The first part describes the working principles of DL with along the dataset building, model analysis and overall system architecture. A new dataset from eight different Bangladeshi fish species. In the process of DL, First, two sets of datasets have been created namely, setup-1(S1) containing original images and setup-2(S2) containing Unsharp masked photos. Then, seven conventional ImageNet pertained state-of-the-art deep learning models on both benchmarking setups: InceptionV3, Xception, DenseNet121, DenseNet169, DenseNet201, InceptionResNetV2, and ResNet152V2. In the process of IoT, the architectural design of a smart contained has been deployed with the aid of several kinds of sensors and microcontrollers. This research has found satisfactory results with the DL models and IoT-based components. The best benchmark accuracy for setup-1 was 96% for all of the DenseNet121, DenseNet169, and DenseNet201 architecture, and for setup-2, it was 96% for the Xception model. Finally, we have constructed a hybrid (CNN + Convolutional LSTM) model, for which the accuracy was 97%, outperforming all of the abovementioned state-of-the-art methods. Besides, the research has performed some experiments with the IoT-based Solution. Though the proposed solution has exhibited some drawbacks, but it can be practicable in real-time solutions

A transcriptome study of p53-pathway related prognostic gene signature set in bladder cancer

p53 pathway is important in tumorigenesis. However, no study has been performed to specifically investigate the role of p53 pathway genes in bladder cancer (BLCA). In this study, transcriptomics data of muscle invasive bladder cancer patients (n = 411) from The Cancer Genome Atlas (TCGA) were investigated. Using the hallmark p53 pathway gene set, the Non-Negative Matrix factorization (NMF) analysis identified two subtypes (C1 and C2). Clinical, survival, and immunological analysis were done to validate distinct characteristics of the subtypes. Pathway enrichment analysis showed the subtype C1 with poor prognosis having enrichment in genes of the immunity related pathways, where C2 subtype with better prognosis being enriched in genes of the steroid synthesis and drug metabolism pathways. A signature gene set consisting of MDGA2, GNLY, GGT2, UGT2B4, DLX1, and DSC1 was created followed by a risk model. Their expressions were analyzed in RNA extracted from the blood and matched tumor tissues of BLCA patients (n = 10). DSC1 had significant difference of expression (p = 0.005) between the blood and tumor tissues in our BLCA samples. Contrary to the usual normal bladder tissue to blood ratio, DLX1 expression was lower (p = 0.02734) in tumor tissues than in blood. Being the first research of p53 pathway related signature gene set in bladder cancer, this study potentially has a substantial impact on the development of biomarkers for BLCA

Antidiabetic Potential of Commonly Available Fruit Plants in Bangladesh: Updates on Prospective Phytochemicals and Their Reported MoAs

Diabetes mellitus is a life-threatening disorder affecting people of all ages and adversely disrupts their daily functions. Despite the availability of numerous synthetic-antidiabetic medications and insulin, the demand for the development of novel antidiabetic medications is increasing due to the adverse effects and growth of resistance to commercial drugs in the long-term usage. Hence, antidiabetic phytochemicals isolated from fruit plants can be a very nifty option to develop life-saving novel antidiabetic therapeutics, employing several pathways and MoAs (mechanism of actions). This review focuses on the antidiabetic potential of commonly available Bangladeshi fruits and other plant parts, such as seeds, fruit peals, leaves, and roots, along with isolated phytochemicals from these phytosources based on lab findings and mechanism of actions. Several fruits, such as orange, lemon, amla, tamarind, and others, can produce remarkable antidiabetic actions and can be dietary alternatives to antidiabetic therapies. Besides, isolated phytochemicals from these plants, such as swertisin, quercetin, rutin, naringenin, and other prospective phytochemicals, also demonstrated their candidacy for further exploration to be established as antidiabetic leads. Thus, it can be considered that fruits are one of the most valuable gifts of plants packed with a wide spectrum of bioactive phytochemicals and are widely consumed as dietary items and medicinal therapies in different civilizations and cultures. This review will provide a better understanding of diabetes management by consuming fruits and other plant parts as well as deliver innovative hints for the researchers to develop novel drugs from these plant parts and/or their phytochemicals