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Textile fabrics with functional properties have a huge interest in many applications, including protective clothing, packaging materials, and healthcare, etc. This study aims to modify textile fabric’s surface and investigate the potentiality of Ag nanoparticles for the preparation of value-added and improved functional textiles. It demonstrates the mussel-inspired in-situ deposition of Ag nanoparticles on cotton fabric pre-modified by dopamine molecules. Surface characterization of nano-Ag deposited fabric is done by SEM (Scanning Electron Microscopy) and EDS (Energy Dispersive Spectroscopy) by which the well deposition of Ag nanoparticles is confirmed. The crystalline size of the Ag nanoparticles has been determined by SEM and X-ray diffraction spectroscopy. As functional properties, antimicrobial activity, UV protection, and crease resistance are investigated. The results reveal that the nano-Ag deposition introduces the excellent antibacterial property to cotton fabric against S. aureus (gram-positive) and E. coli (gram-negative). The fabric shows good UV protection power and significant crease resistance. The fabric has also been dyed with reactive dyestuff, and improved dyeing performance is found. Importantly, no significant changes in mechanical properties of the textile cotton fabric are found by surface modification and deposition of Ag nanoparticles.

The aim of the study was to determine serum ferritin level in transfusion dependent thalassaemia patients. A total of 64 transfusion dependent thalassaemia (TDT) patients was included in this cross sectional study from April, 2018 to September, 2019 according to selection criteria. Body iron load was estimated by serum ferritin level. The study sample consisted of 43 male and 21 female TDT patients, with a mean age of 25.5 years. Most of the patients (43.8%) patients had serum ferritin level in between 1000-2500 ng/ml. 12 patients (18.7%) had serum ferritin level >5000 ng/ml and 3 patients (4.7%) had serum ferritin in between 2501-5000 ng/ml. The mean serum ferritin was found 2462.6 ± 2792.7ng/ml with range from 207.0 -11891.2ng/m.

Unconventional modes of transport (UCMs) play a substantial role in many developing cities in meeting the mobility demand in spite of having negative image in the eyes of the authorities. This manuscript outlines a conjoint framework where various factors affecting sustainability and competitiveness of UCMs are identified through observation study and organized using PESTLE qualitative framework. Afterwards, it presents an AHP-TOPSIS based qualitative approach where inputs from experts as well as major stakeholders (owners/operators, drivers, conductors and passengers) are combined based on the PESTLE framework to fathom the competitiveness of UCMs. Along this process, the manuscript also unveils the business model of UCMs along with their area of operation, advantages and weaknesses. For this, the study uses Dhaka, the capital city of Bangladesh as study area and selects three popular UCMs – locally known as Rickshaw, Easy bike and Leguna. The findings suggest that economic, social and political factors respectively have the highest influence among all factors of PESTLE. Rickshaw was found to be the most competitive followed by Easy bike and Leguna. The study is expected to shed light into the perception and expectation of major stakeholders of UCMs which in turn will provide valuable insight for designing conventional public transport services, and/or, help in slowly integrating the UCMs into the mainstream transportation service through addressing their shortcomings.

Background: BCR-ABL translocation is the most common genetic abnormality associated with adult Acute Lymphoblastic Leukaemia (ALL) with poor outcome. Objective: The aim of the study was to determine the association of myeloid aberrant antigens and the presence of BCR-ABL gene rearrangements in acute lymphoblastic leukaemia (ALL) patients in our context. Method: A total of 38 ALL patients were included in this cross-sectional study from August 2018 to July 2019 according to selection criteria. BCR-ABL was detected by Real Time-Polymerase Chain Reaction (RT-PCR). Results: The median age at diagnosis was 27.5 years with male (76.3%) predominance. Aberrant myeloid markers, e.g.CD13 was present in 9(64.3%) patients who were BCR-ABL positive which was statistically significant (p

The aim of the study was to determine the frequency of hypothyroidism in transfusion dependent thalassaemia patients. A total of 64 transfusion dependent thalassaemia (TDT) patients were included in this cross-sectional study from April, 2018 to September, 2019 according to selection criteria. Thyroid hormone status was assessed by estimation of serum FT4 and serum TSH. Body iron load was estimated by serum ferritin level. The study sample consisted of 43 male and 21 female TDT patients, with a mean age of 25.5 years. Total 28% patients were found hypothyroid, 11% were overt hypothyroid and 17% were subclinical hypothyroid. Mean serum ferritin level was 2462.6 ng/ml. Significant correlation was not found between serum TSH and Serum ferritin level (p = 0.055). Total unit of transfusion (p=0.001) and duration of transfusion therapy (p=0.003) were significantly associated with development of hypothyroidism.

The study of interfacial properties of thin films such as polymers is an important area of surface science. The application of visible–infrared sum-frequency generation spectroscopy to such systems...

A recommendation system can assist the user to compose an understanding of requirements and propose informed decisions from a lot of complicated knowledge. Recommendation from an analysis of sentiments seems to be a great challenge as user-generated content is represented using human language in several complicated ways. Many studies have focused on common fields such as reviews of electrical items, films, and restaurants, but not enough on health and medical issues. Sentiment analysis of healthcare in general and that of the drug experiences of individuals, in particular, may shed considerable light on how to focus on improving public health and reach the correct decision. In this paper, we design and implement a drug recommender system framework that applies sentiment analysis technologies on drug reviews. The objective of this research is to build a decision-making support platform to help patients to achieve more significant choices in drug selection. Firstly, we propose a sentimental measurement approach to drug reviews and generate ratings on drugs. Secondly, we take how much the drug reviews are useful to users, patient's conditions, and dictionary sentiment polarity of drug reviews into consideration. Then, we fuse those factors into the recommendation system to list appropriate medications. Experiments have been carried out using Decision Tree, K-Nearest Neighbors, and Linear Support Vector Classifier algorithm in rating generation and Hybrid model in recommendation based on the given open dataset. The analysis is carried out to tune the parameters for each algorithm in order to achieve greater performance. Finally, Linear Support Vector Classifier is selected for rating generation to obtain a good trade-off among model accuracy, model efficiency, and model scalability.

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

Background Type 2 diabetes (T2D) is an interminable illness that makes many types of genetic dysfunction in the human body, particularly to the nerves, veins, kidneys, liver and uterus. T2D existence in a longer period of time may create the risk of building some comorbidities, however the indication of this risk is inadequately comprehended. To address this issue, we studied the transcriptomic data to recognize the link between T2D and its major comorbidities. Methods We developed a quantitative model to investigate the genetic links between T2D and its significant comorbidities. We analyzed gene expression omnibus (GEO) microarray data from T2D, liver cancer (LC), endometrial cancer (EC), xanthoma (Xa), myocardial infarction (MI), embolic stroke (ES), kidney failure (KF), xerostomia (Xe) and control datasets. We constructed gene-disease association networks (GDN), identified signaling and ontological pathways, formed protein-protein interaction (PPI) network employing neighborhood-based benchmarking and multilayer network topology. Results We observed, T2D shared 22, 15, 18, 21, 12, 14 and 13 differentially expressed genes (DEGs) with LC, EC, Xa, MI, ES, KF, and Xe respectively. DEG investigation, signaling and ontological pathways, and PPI network suggest significant links between T2D and the advancement of LC, EC, Xa, MI, ES, KF, and Xe. Conclusions Our systematic approach to identify the genetic links of T2D with the progression of LC, EC, Xa, MI, ES, KF, and Xe by understanding the causal influences of T2D may be helpful to develop therapeutic strategies for T2D and those comorbidities. This study will also be useful to predict comorbidities occurrence and build human awareness against the dangerous effects of T2D.

The characterization of dissolved organic matter (DOM) is an important parameter for the management of water resources. In this study, physical and chemical characteristics of DOM in Kushiro river, and its territory (Eastern Hokkaido, Japan) were analyzed using multiple analytical tools. Multiple samples were collected from diverse locations (Kushiro river, Kushiro wetland, compost fertilizer, forest and treated sewage) and compared them against their DOM characteristics. Among the samples, Kushiro wetland samples presented higher specific ultraviolet absorbance (SUVA) values demonstrating DOM derived from vascular plants which are rich in aromatic-components. The SUVA values of the two samples of the Kushiro river were relatively close to those of the wetland samples implying the fact that the river contains high amount of plant derived aromatic DOM compounds, originating from the wetland. The excitation–emission matrix (EEM) fluorescence results showed that generally all water samples have two peaks namely, A and C. These two peaks are related to humic-like substance typically derived from the breakdown of organic matters. The excitation–emission matrix (EEM) fluorescence results of the river samples were also similar to those of the wetland samples, characterized by no appearance of protein, amino acid, polysaccharide, and fluvic like matter, but more pronounced to the aromatic component rich humic acid. Principal component analysis revealed that approximately 88% of the variance in the DOM characteristics might be explained by the type of different sources. From the loadings on PC1 and PC2 in the scores plot, the properties of the DOM of the wetland appeared to be similar to those of the river DOM. Combined results exhibited that the quality of the Kushiro river water is fundamentally inherited from the Kushiro wetland, and the main structure of the river water is characterized by poly-aromatic humic like matter and higher molecular weight.

For many applications, it is desirable for a node to be able to identify the neighbor nodes currently within range. However, the identification procedures for terrestrial communication networks TCNs are inefficient when implemented in long-delay networks LDNs such as underwater acoustic networks or satellite networks, where the propagation time of a packet cannot be neglected. Here, we propose a time-efficient and power-efficient procedure, which is insensitive to propagation delay, for identifying neighbors in an LDN by optimizing the network offered load using either unslotted or slotted protocols. The procedure is adapted to the scenario when node cardinality and distribution are either known or unknown in advance. We achieve improvements as high as 80% in time and 45% in power consumption for our proposed approach compared with approaches developed previously for TCN. Furthermore, we analyze our proposed approach with the inclusion of the capture effect and packet receive time variations. We also provide a closed form formula for finding the optimum guard time and a procedure to estimate the packet receive time variations. Copyright © 2012 John Wiley & Sons, Ltd.

Evaluation of crash-related injuries by medical specialists in hospitals is believed to be more exact than rather a cursory evaluation made at the crash scene. Safety analysts sometimes reach for hospital data and use them in combination with the police crash data. One issue that needs to be addressed is the, so-called, selectivity (or selection) bias possible when data used in analysis are not coming from random sampling. If not properly addressed, this issue can lead to a considerable bias in both the model coefficient estimates and the model predictions. This paper investigates pedestrian injury severity factors using linked police-hospital data. A bivariate ordered probit model with sample selection is used to check for the presence of the selectivity bias and to account for it in the MAIS estimates on the Maximum Abbreviated Injury Scale (MAIS). The presence of the sample selection issue has been confirmed. The selectivity bias is considerable in predictions of low injury levels. The pedestrian injury analysis identified and estimated several severity factors, including pedestrian, road, and vehicle characteristics. Male and older pedestrians were found to be particularly exposed to severe injuries. Rural roads and high-speed urban roads appear to be more dangerous for pedestrians, particularly when crossing such roads. Crossing a road between intersections was found to be particularly dangerous behavior. The size and weight of the vehicle involved in a pedestrian crash were also found to have an effect on the pedestrian injury level. The relevant safety countermeasures that may improve pedestrian safety have been proposed.

In ad-hoc networks, at the beginning of network formation or later for routing purposes it is essential for a node to know how many neighbors it has. The capture effect, the effect of receiving a packet from a collision, can degrade the performance of the procedure of estimation of number of neighbors (ENN). The capture effect depends on different network parameters which are different in underwater communication networks (UCNs) than terrestrial communication networks (TCNs). Both electromagnetic (EM) and acoustic waves can be used in UCNs. Furthermore, underwater acoustic networks (UANs) have the problem of long propagation delays. These issues make the ENN procedure complicated. Here we propose an improved ENN procedure for UCNs as well as for TCNs which is not affected by the capture effect. The procedure requires fewer slots than earlier approaches and is much effective with packet reception time variations.

This paper presents a speech-based speaker identification system and an efficient approach for selection of acoustic parameters closely related to the vocal track shape of the speaker. Speech endpoint detection algorithm is developed in order to discard the room noise and non-speech signal to achieve high accuracy of the system. Windowing and Fast Fourier Transform (FFT) are used to determine the spectrum of the speech signal and PCA has been used to extract feature of speech of individual speaker. Eigenface algorithm has been used here as a classification and recognation tool. Eigenspace of individual speaker is generated by the feature of the speech signal. The experimental results show the noticeable performance of the proposed system.

This paper presents Bangla numeral Character Recognition System using supervised locally linear embedding algorithm and Support Vector Machine (SVM). The locally linear embedding (LLE) algorithm is an unsupervised technique proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). Where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. we combined it with support vector machine (SVM) for classifying handwritten digits from the On-Line Handwritten Bangla Numeral Database.

Technical and social performances of an arsenic-removal technology--the sono arsenic filter--in rural areas of Bangladesh were investigated. Results of arsenic field-test showed that filtered water met the Bangladesh standard (50 microg/L) after two years of continuous use. A questionnaire was administrated among 198 sono arsenic filter-user and 230 non-user families. Seventy-two percent of filters (n = 198) were working at the time of the survey. Another 28% of the filters were abandoned due to breakage. The abandonment percentage (28%) was lower than other mitigation options currently implemented in Bangladesh. Households were reluctant to repair the broken filters on their own. High cost, problems with maintenance of filters, weak sludge-disposal guidance, and slow flow rate were the other demerits of the filter. These results indicate that the implementation approaches of the sono arsenic filter suffered from lack of ownership and long-term sustainability. Continuous use of arsenic-contaminated tubewells by the non-user households demonstrated the lack of alternative water supply in the survey area. Willingness of households to pay (about 30%) and preference of household filter (50%) suggest the need to develop a low-cost household arsenic filter. Development of community-based organization would be also necessary to implement a long-term, sustainable plan for household-based technology.