Min-O-Mee: A Proximity Based Network Application Leveraging The AllJoyn Framework

Close proximity of mobile devices can be utilized to create ad hoc and dynamic networks. These mobile Proximity Based Networks (PBNs) are Opportunistic Networks that enable devices to identify and communicate with each other without relying on any communication infrastructure. In addition, these networks are self organizing, highly dynamic and facilitate effective real-time communication. These characteristics render them very useful in a wide variety of complex scenarios such as vehicular communication, e-health, disaster networks, mobile social networks etc. In this work we employ the AllJoyn framework from Qualcomm which facilitates smooth discovery, attachment and data sharing between devices in close proximity. We develop Min-O-Mee, a Minutes-of-Meeting app prototype in the Android platform, utilizing the AllJoyn framework. Min-O-Mee allows one of the participants to create a minutes-of-meeting document which can be shared with and edited by the other participants in the meeting. The app harnesses the spatial proximity of participants in a meeting and enables seamless data exchange between them. This characteristic allows Min-O-Mee to share not just minutes-of-meeting, but any data that needs to be exchanged among the participants, making it a versatile app. Further, we extend the basic AllJoyn framework to enable multi-hop communication among the devices in the PBN. We devise a novel routing mechanism that is suited to a proximity centric wireless network as it facilitates data routing and delivery over several hops to devices that are at the fringe of the PBN

Characterization,Estimation, and Mitigation of Interference in Multi- Radio Multi- Channel Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have evolved into a wireless communication technology of im-mense interest. But technological advancements in WMNs have inadvertently spawned a plethora of network performance bottlenecks, caused primarily by the rise in prevalent interference. The benefits that multi-radio multichannel(MRMC) WMNs offer viz., augmented network capacity, uninterrupted connectivity and reduced latency, are depreciated by the detrimental effect of prevalent interference. Interference mitigation is thus a prime objective in WMN deployments. Conflict Graphs are indispensable tools used to theoretically represent and estimate the interference in wire- less networks. This interference is multidimensional, radio co-location interference (RCI) being a crucial aspect that is seldom addressed in conflict graph generation approaches suggested in re- search studies. Further, designing high performance channel assignment (CA) schemes to harness the potential of MRMC deployments in WMNs is an active research domain. A pragmatic channel assignment approach strives to maximize network capacity by restraining the endemic interference and mitigating its adverse impact on network performance metrics. However, numerous CA schemes have been proposed in research literature and there is a lack of CA performance prediction techniques which could assist in choosing a suitable CA for a given WMN

Interference mitigation in wireless mesh networks through radio co-location aware conflict graphs

Wireless Mesh Networks (WMNs) have evolved into a wireless communication technology of immense interest. But technological advancements in WMNs have inadvertently spawned a plethora of network performance bottlenecks, caused primarily by the rise in prevalent interference. Conflict Graphs are indispensable tools used to theoretically represent and estimate the interference in wireless networks. We propose a generic algorithm to generate conflict graphs which is independent of the underlying interference model. Further, we propose the notion of radio co-location interference, which is caused and experienced by spatially co-located radios in multi-radio multi-channel WMNs. We experimentally validate the concept, and propose a new all-encompassing algorithm to create a radio co-location aware conflict graph. Our novel conflict graph generation algorithm is demonstrated to be significantly superior and more efficient than the conventional approach, through theoretical interference estimates and comprehensive experiments. The results of an extensive set of ns-3 simulations run on the IEEE 802.11g platform strongly indicate that the radio co-location aware conflict graphs are a marked improvement over their conventional counterparts. We also question the use of total interference degree as a reliable metric to predict the performance of a Channel Assignment scheme in a given WMN deployment

Malignant mandibular tumors: two case reports of rare mandibular tumors in a single institution

Mandibular lesions can be benign or malignant, malignant being less common. The most common malignant tumor of mandible is squamous cell carcinoma. Others are ameloblastic carcinoma,  osteosarcoma, chondrosarcoma, fibrosarcoma, malignant fibrous histiocytoma and metastasis. Osteosarcoma is a bone tumor. It can occur in any bone, usually in the long bones of the extremities, but osteosarcoma of mandible is rare. In the initial phase, they may present as nondescript bony swellings with an indolent growth, only to become malignant towards the later stages. Osteosarcomas of the jaw are rare and they differ from osteosarcomas of the long bones in their biological behavior, even though they have the same histological appearance. Malignant fibrous histiocytoma (MFH) is the most common soft-­‐tissue sarcoma, but  relatively uncommon in head and neck region with only 30 reported cases till date. The purpose of this report is to present two cases of rare malignant mandibular tumors in a single institution.KEY WORDS: Osteosarcoma; Malignant fibrous histiocytoma; Mandibl

APPLICATION OF SUPPORT VECTOR MACHINES FOR FODDER CROP ASSESSMENT

Identification of crop and its accuracy is an important aspect in predicting crop production using Remote Sensing technology. This study investigates the ability of Support Vector Machine (SVM) algorithm in discriminating fodder crops and estimating its area using moderate resolution multi-temporal Landsat-8 OLI data. SVM is a non-parametric statistical learning method and its accuracy is dependent on the parameters and the kernels used. The objective was to evaluate the feasibility of SVM in fodder classification and compare the results with traditional parametric Maximum Likelihood Classification (MLC). Fodder crops are available over small fields in the study area thus having large number of pure fodder pixels over small area is difficult. Hence, SVM has an advantage over MLC as it works well with less training data sets also. Three kernels (linear, polynomial and radial based function) were used with SVM classification. Comparative analysis showed that higher overall accuracy was observed in SVM in comparison to MLC. Temporal change in the spectral properties of the crops derived through Normalized Difference Vegetation Index (NDVI) from multi-temporal Landsat-8 was found to be the most important information that affects accuracy of classification. The classification accuracies for SVM with radial based function, polynomial, linear kernel and MLC were 90.09%, 89.9%, 88.9% and 82.4% respectively. The result suggested that SVM including three kernels performed significantly better than MLC. India has low livestock productivity due to unavailability of fodder hence this study could help in strengthening the fodder productivity

Detection of xenoestrogens in serum after immunoprecipitation of endogenous steroidal estrogens.

In this article we report a simple and efficient method for detecting nonsteroidal estrogens in a biologic sample. This method uses polyclonal antibodies to estradiol (E2) to immunoprecipitate these major biologically active steroidal estrogens, leaving behind the nonsteroidal estrogens, which are then detected in a cell-based transcriptional activation bioassay for estrogen receptor agonist. The immunoprecipitation method efficiently removed 99% of radiolabeled E2 and estrone (E1) from human serum. In experiments in which supraphysiologic concentrations of E2 and E1 to human serum, all of the immunoreactive estrogens were still removed by the immunoprecipitation protocol. We carried out an in vivo validation study of this method in which we treated female macaques with the xenoestrogen nonylphenol (NP), during the late follicular phase of the menstrual cycle. We used blood samples collected before and after treatment to evaluate and characterize endogenous and exogenous serum estrogens. An immunoassay for E2 did not detect the NP in treated monkeys. The cell-based bioassay also did not detect the estrogenic activity of NP because of its saturation by the endogenous serum steroidal estrogens. However, when steroidal estrogens were removed by immunoprecipitation, we detected the estrogenic activity of NP in the bioassay. Thus, this approach is appropriate for detecting exogenous, nonsteroidal estrogens in serum samples

A systematic review on the effects of Echinacea supplementation on cytokine levels: Is there a role in COVID-19?

COVID-19 is the respiratory illness caused by the novel coronavirus, SARS-CoV-2. Cytokine storm appears to be a factor in COVID-19 mortality. Echinacea species have been used historically for immune modulation. A previous rapid review suggested that Echinacea supplementation may decrease the levels of pro-inflammatory cytokines involved in cytokine storm. The objective of the present systematic review was to identify all research that has assessed changes in levels of cytokines relevant to cytokine storm in response to administration of Echinacea supplementation. The following databases were searched: Medline (Ovid), AMED (Ovid), CINAHL (EBSCO), EMBASE (Ovid). Title and abstract screening, full text screening, and data extraction were completed in duplicate using a piloted extraction template. Risk of bias assessment was completed. Qualitative analysis was used to assess for trends in cytokine level changes. The search identified 279 unique publications. After full text screening, 105 studies met criteria for inclusion including 13 human studies, 24 animal studies, and 71 in vitro or ex vivo studies. The data suggest that Echinacea supplementation may be associated with a decrease in the pro-inflammatory cytokines IL-6, IL-8, and TNF, as well as an increase in the anti-inflammatory cytokine IL-10. The risk of bias in the included studies was generally high. While there is currently no substantive research on the therapeutic effects of Echinacea in the management of either cytokine storm or COVID-19, the present evidence related to the herb's impact on cytokine levels suggests that further research may be warranted in the form of a clinical trial involving patients with COVID-19

AN ALGORITHM FOR GENERATING NATURAL COLOR IMAGES FROM FALSE COLOR USING SPECTRAL TRANSFORMATION TECHNIQUE WITH HIGHER POLYNOMIAL ORDER

Satellite imageries in True color composite or Natural Color composite (NCC) serves the best combination for visual interpretation. Red, Green and Infrared channels form false color composite which might not be as useful as NCC to a non-remote sensing professional. As blue band is affected by large atmospheric scattering, satellites like IRS-LISS IV, SPOT do not have blue band. To generate NCC from such satellite data blue band must be simulated. Existing algorithms of spectral transformation do not provide robust coefficients leading to wrong NCC colors especially in water bodies. To achieve more robust coefficients, we have proposed new algorithm to generate NCC for IRS-LISS IV data using second order polynomial regression technique. Second order polynomial transformation functions consider even minor variability present in the image as compared to 1st order so that the derived coefficients are adjustable to accommodate spatial and temporal variability while generating NCC. In this study, Sentinel-2 image was used for deriving coefficients with blue band as dependent and green, red and infrared as independent variables. Simulated Sentinel band showed high accuracy with correlation of 0.93 and 0.97 for two test sites. Using the same coefficients, blue band was simulated for LISS-IV which also showed good correlation of 0.90 with sentinel original blue band. On comparing LISS-IV simulated NCC with simulated NCC from other algorithms, it was observed that higher order polynomial transformation was able to achieve higher accuracy especially for water bodies where expected color is green. Thus, proposed algorithms can be used for transforming false color image to natural color images