An Architecture for an Infrastructure as a Service for the Internet of Things

Internet of things (IoT) refers to things such as sensors and actuators interacting with each other to reach common goals. It enables multiple applications in sectors ranging from agriculture to health. Nowadays, applications and IoT infrastructure are tightly coupled and this may lead to the deployment of redundant IoT infrastructures, thus, cost inefficiency. Cloud computing can help in tackling the problem. It is a paradigm to quickly provision configured resources (computing, network, memory) on demand for cost efficiency. It has three layers, the infrastructure as a service (IaaS), the platform as a service (PaaS) and the software as a service (SaaS). Through the IaaS, configured hardware resources (CPU, storage, etc.) are provisioned on demand. However, designing and implementing an IoT IaaS architecture for the provisioning of IoT resource on demand remains very challenging. An example of a challenge is using the appropriate publishing and discovery mechanism suitable for IoT devices. Orchestrating a virtualized IoT device over several physical IoT devices is another challenge that needs to be addressed. The main contribution of this thesis is twofold. First, a novel IoT IaaS architecture is proposed where IoT devices can be provisioned as a configured infrastructure resource on demand via node virtualization. Second, the architecture is prototyped and evaluated using real-life sensors that support node virtualization. Node level virtualization achieves resource efficiency in contrast to middleware solutions. The essential architectural features, such as publication, discovery, and orchestration are identified and proposed. Two sets of a high-level interface are also introduced. A low-level uniform interface is suggested to decouple the IoT devices from the applications by allowing the applications to access the heterogeneous devices in a uniform way. In addition, a cloud management interface is proposed to expose the IoT IaaS to the cloud consumers (for example - the PaaS, the application, etc.) and allow them to provision the IoT resources. By allowing the capability sharing of the IoT devices using the node virtualization, the cost efficiency and energy efficiency are achieved in the proposed architecture. Addressing other challenges allowed the proposed architecture to expose the IoT devices to the IaaS in a more abstract manner. Thus allowing the application to provision the IoT resources on demand as well as handling the IoT device heterogeneity in the IaaS

Framework for transfer learning: Maximization of quadratic mutual information to create discriminative subspaces

In the area of pattern recognition and computer vision, Transfer learning has become an emerging topic in recent years. It is motivated by the mechanism of human vision system that is capable of accumulating previous knowledge or experience to unveil a novel domain. Learning an effective model to solve a classification or recognition task in a new domain (dataset) requires sufficient data with ground truth information. Visual data are being generated in an enormous amount every moment with the advance of photo capturing devices. Most of these data remain unannotated. Manually collecting and annotating training data by human intervention is expensive and hence the learned model may suffer from performance bottleneck because of poor generalization and label scarcity. Also an existing trained model may become outdated if the distribution of training data differs from the distribution where the model is tested. Traditional machine learning methods generally assume that training and test data are sampled from the same distribution. This assumption is often challenged in real life scenario. Therefore, adapting an existing model or utilizing the knowledge of a label-rich domain becomes inevitable to overcome the issue of continuous evolving data distribution and the lack of label information in a novel domain. In other words, a knowledge transfer process is developed with a goal to minimize the distribution divergence between domains such that a classifier trained using source dataset can also generalize over target domain. In this thesis, we propose a novel framework for transfer learning by creating a common subspace based on maximization of non-parametric quadratic mutual information (QMI) between data and corresponding class labels. We extend the prior work of QMI in the context of knowledge transfer by introducing soft class assignment and instance weighting for data across domains. The proposed approach learns a class discriminative subspace by leveraging soft-labeling. Also by employing a suitable weighting scheme, the method identifies samples with underlying shared similarity across domains in order to maximize their impact on subspace learning. Variants of the proposed framework, parameter sensitivity, extensive experiments using benchmark datasets and also performance comparison with recent competitive methods are provided to prove the efficacy of our novel framework

Resistance Pattern of Levofloxacin against Uropathogens Causing Urinary Tract Infection in Selected Areas of Dhaka city, Bangladesh

A crucial public health problem in developing country like Bangladesh is resistance of antibiotics to different types of bacteria and the rates of these bacterial resistances are changing for various antibiotic therapy. Our aim was to assess the susceptible pattern of Levofloxacin a 3rd generation Quinolone antibiotic against uropathogens. A total of 12943 urine samples were collected in 2016 (Jan-Dec) and out of which 1236 (9.55%) were bacteriologically positive. Among the isolated uropathogens, 95.1% were gram negative and 4.9% gram positive organism. Male were found more prone to get UTI under 10 years and between 51-90 years of age and female were more affected in 10 to 50 years and over 90 years of age group. E. coli was the most prevalent (83.9%) isolate followed by Klebsiella spp. (6.7%), Staphylococcus aureus (2.6%), Pseudomonas spp. (2.2%), Enterococcus spp. (2.0%) and Proteus spp. (1.1%). The most predominant Levofloxacin sensitive organisms were found in male Enterobacter spp. (100%) and in female patients Serretia spp. (100%), Citrobacter spp. (100%) and Streptococcus Group B (100%). On the other hand the most predominant Levofloxacin resistant organisms were found in male and female both Acinetobacter spp. (100%). Around (61.7%) male and (46.9%) female were found resistant to E. coli. Keywords: Levofloxacin, Quinolone, UTI, Resistance, Uropathogen

Spherical and Rod-shaped Gold Nanoparticles for Surface Enhanced Raman Spectroscopy

Raman Spectroscopy offers an in-situ, rapid, and non-destructive characterization tool for chemical analysis of diverse samples with no or minimal preparation. However, due to the inherent weak signal of conventional Raman spectroscopy, surface plasmon resonance features of noble metal nanoparticles have been utilized to conduct Surface Enhanced Raman Spectroscopy (SERS) in detecting trace label contaminants in foods and foodstuffs. In this effort, we synthesized gold nanoparticles (AuNPs) by reduction of chloroauric acid (HAuCl4) with sodium citrate dehydrate. We prepared different sizes of AuNPs at a fixed temperature (100 oC) but with varying pHs of 4 and 8. The as-synthesized AuNPs were characterized by UV-Vis spectroscopy, dynamic light scattering (DLS), and Field Emission Scanning Electron Microscopy (FE-SEM). FE-SEM micrographs revealed spherical AuNPs with an average diameter of approx. 55 nm and rod-shaped AuNPs with an average length of approx. 170 nm for sample synthesis at pH 8 and 4, respectively. The effectiveness of the as-prepared AuNPs for SERS is tested by detecting Rhodamine 6G diluted at a trace level. This study suggests that plasmonic nanoparticles coupled with SERS have great potential for broad applications in detecting other trace amounts of hazardous chemicals in foods and foodstuffs.Comment: 4 pages, 5 figure

Anti-oxidant effect of Flemingia stricta Roxb. leaves methanolic extract

Aim of the study was to evaluate the possible anti-oxidant activity of Flemingia stricta leaf extract. In antioxidant study, plant crude methanol extract was evaluated for 1,1-diphenyl,2-picrylhydrazyl (DPPH) and reducing power capacity. Moreover, total phenolic and total flavonoid content of plant extracts were determined and expressed in mg of gallic acid equivalent per gram of dry sample (mg GAE/g dry weight). In the DPPH free radical scavenging assay, methanol extract showed concentration dependent inhibition of the free radicals. IC50 of ascorbic acid and F. stricta leaves were 4.25 µg/ml and 320.47 µg/ml respectively. In case of reducing capacity, the methanol extract at concentrations of 25, 50, 100, 200, 400 µg/ml, the absorbances were 0.56, 0.92, 1.41, 1.76, 2.23, respectively. Total phenolic content was estimated by gallic acid and expressed as milligrams of gallic acid equivalent (GAE). The methanol extracts contained a considerable amount of phenolic contents of 482±8.72 of GAE/g of extract and the total flavonoid content of the F. stricta leaf was estimated by using aluminium chloride colorimetric technique and found that the extract contained flavonoid content 340.625±4.50 of GAE/g of extract. These results suggested that the methanol extract of F. stricta Roxb. possess anti-oxidant activity. DOI: http://dx.doi.org/10.5281/zenodo.146976

Complications of Ilizarov method treatment for infected nonunion femoral shaft fracture

Background: Infected non-union of the femur is complicated by the involvement of soft tissue and bone, long-term resistant multi-bacterial infection, limb length discrepancy, deformities, joint stiffness, and multiple draining sinuses, and poses a challenge for orthopedic surgeons. This study aimed to analyze the complications of Ilizarov method treatment for infected non-union femoral shaft fracture. Methods: This prospective observational study was conducted at the national institute of traumatology and orthopedic rehabilitation (NITOR), Dhaka, Bangladesh, from May 2018 to August 2020. A total of 20 patients were selected as study subjects by purposive sampling technique. All data were collected using a pre-formed questionnaire. Data were processed and analyzed using computer software program SPSS version 22.0. Result: The mean bone gap created during the operation was 2.7±1.7 cm of them, in 12 (60%) patients, it was 0 to 2 cm, and in 8 (40%) patients it was more than 2 cm. The mean time needed for radiological union was 7.85±2.1 months ranging from 5 months to 11 months. In 10 (50%) patients, union was achieved within 4 to 7 months, and in 10 (50%) patients it was 8 to 11 months. Regarding limb length discrepancy, in 5 (25%) cases there was no limb length discrepancy (LLD). Twelve patients had 1 cm to 2.4 cm LLD and 3 (15%) patients had ≥2.5 cm LLD. The mean LLD was 1.2±0.9 cm. Regarding complications, in 10 (50%) cases, there was no complication and 10 (50%) patients had complications. The complications were pin tract infection in 7 (35%) patients and wire loosening in 3 (15%) patients. Conclusions: The study concludes that while the Ilizarov ring fixator proves to be a dependable and successful method for stabilizing, correcting length discrepancies, and eliminating infections, it is not without its share of complications. The findings of this research indicate a 50% complication rate among the patients undergoing this treatment

SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation

Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0 role= presentation \u3eR0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario