An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

open access articleWireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privac

Decision Support Capabilities of Enterprise Content Management Systems: An Empirical Investigation

Enterprise content management (ECM) systems help organizations cope with the increasing complexity and volume of data and information. Despite the growing popularity of ECM, published literature indicates that organizations primarily use ECM for operational benefits, while the strategic decision making capabilities are rarely considered. Thus, the most significant rewards of ECM implementation may be largely forgone. This study investigates the potential of ECM technology for decision support. A research model is proposed and validated via an empirical investigation. The results show that ECM positively influences problem identification and definition, decision making speed and analysis, decision quality, and decision makers’ satisfaction

The anti-adhesive effect of curcumin on Candida albicans biofilms on denture materials

The use of natural compounds as an alternative source of antimicrobials has become a necessity given the growing concern over global antimicrobial resistance. Polyphenols, found in various edible plants, offers one potential solution to this. We aimed to investigate the possibility of using curcumin within the context of oral health as a way of inhibiting and preventing the harmful development of Candida albicans biofilms. We undertook a series of adsorption experiments with varying concentrations of curcumin, showing that 50 ug/ml could prevent adhesion. This effect could be further synergised by the curcumin pretreatment of yeast cells to obtain significantly greater inhibition (>90, p<0.001). Investigation of the biological impact of curcumin showed that it preferentially affected immature morphological forms (yeast and germlings), and actively promoted aggregation of the cells. Transcriptional analyses showed that key adhesins were down-regulated (ALS1 and ALS3), whereas aggregation related genes (ALS5 and AAF1) were up-regulated. Collectively, these data demonstrated that curcumin elicits anti-adhesive effects and that induces transcription of genes integrally involved in the processes related to biofilm formation. Curcumin and associated polyphenols therefore have the capacity to be developed for use in oral healthcare to augment existing preventative strategies for candidal biofilms on the denture surface

Candida albicans biofilm heterogeneity does not influence denture stomatitis but strongly influences denture cleansing capacity

Approximately 20  % of the UK population wear some form of denture prosthesis, resulting in denture stomatitis in half of these individuals. Candida albicans is primarily attributed as the causative agent, due to its biofilm -forming ability. Recently, there has been increasing evidence of C. albicans biofilm heterogeneity and the negative impact it can have clinically; however, this phenomenon has yet to be studied in relation to denture isolates. The aims of this study were to evaluate C. albicans biofilm formation of clinical denture isolates in a denture environment and to assess antimicrobial activity of common denture cleansers against these tenacious communities. C. albicans isolated from dentures of healthy and diseased individuals was quantified using real-time PCR and biofilm biomass assessed using crystal violet. Biofilm development on the denture substratum poly(methyl methacrylate), Molloplast B and Ufi-gel was determined. Biofilm formation was assessed using metabolic and biomass stains, following treatment with denture hygiene products. Although C. albicans was detected in greater quantities in diseased individuals, it was not associated with increased biofilm biomass. Denture substrata were shown to influence biofilm biomass, with poly(methyl methacrylate) providing the most suitable environment for C. albicans to reside. Of all denture hygiene products tested, Milton had the most effective antimicrobial activity, reducing biofilm biomass and viability the greatest. Overall, our results highlight the complex nature of denture- related disease, and disease development cannot always be attributed to a sole cause. It is the distinct combination of various factors that ultimately determines the pathogenic outcome

Interkingdom interactions on the denture surface: implications for oral hygiene

Background: Evidence to support the role of Candida species in oral disease is limited. Often considered a commensal, this opportunistic yeast has been shown to play a role in denture related disease, though whether it is an active participant or innocent bystander remains to be determined. This study sought to understand the role of Candida species alongside the bacterial microbiome in a denture patient cohort, exploring how the microbiology of the denture was affected by oral hygiene practices. Materials and methods: In vitro denture cleansing studies were performed on a complex 9-species interkingdom denture biofilm model, with quantitative assessment of retained bacterial and fungal viable bioburdens. Patient hygiene measures were also collected from 131 patients, including OHIP, frequency of denture cleansing, oral hygiene measure and patient demographics. The bacterial microbiome was analysed from each patient, alongside quantitative PCR assessment of ITS (fungal) and 16S (bacterial) bioburden from denture, mucosa and intact dentition. Results: It was shown that following in vitro denture cleansing C. albicans were unresponsive to treatment, whereas bacterial biofilms could repopulate 100-fold, but were susceptible to subsequent treatment. Within the patient cohort, oral hygiene did not impact candidal or bacterial composition, nor diversity. The levels of Candida did not significantly influence the bacterial microbiome, though an observed gradient was suggestive of a microbial composition change in response to Candida load, indicating interkingdom interaction rather than an oral hygiene effect. Indeed, correlation analysis was able to show significant correlations between Candida species and key genera (Lactobacillus, Scardovia, Fusobacterium). Conclusions: Overall, this study has shown that the denture microbiome/mycobiome is relatively resilient to oral hygiene challenges, but that Candida species have potential interactions with key oral genera. These interactions may have a bearing on shaping community structure and a shift from health to disease when the opportunity arises

Can IT Resources Lead to Sustainable Competitive Advantage?

Gaining sustainable competitive advantage (SCA) is a desire for almost every organization; however, acquiring such advantage is not an easy task. Many organizations implement information technology (IT) systems to be competitive in the market, and many researchers have examined the link between IT and SCA. Organizations need to focus not only on IT resources but also on how to manage these resources to be competitive in the marketplace. In this paper, a new model is proposed based on resource-based view and strategic alignment model. By linking strategic IT resources to alignment mechanisms, this model shows the ability of IT as a facilitator in gaining competitive advantage

A linear algebra approach to graph melting

Robustness is often regarded as the ability of a given system to maintain its functionality when faced with some external perturbation or when some of its parts fail to operate. The ability of the system to cope with disturbances vary from system to system, and there are many examples in daily life which illustrate this concept. Communication networks, for example, transportation and telephone networks, or the internet, often manage to cope with errors or damage within some of their components without leading to the system failing entirely. As an example, within a social network of employees within a company, the absence of some employees within some given threshold will not lead to failure of the company. However, in a financial network, economic failure in some parts of the system could lead to the complete failure of the entire system. In order to understand how external perturbations or failures within particular parts of the system affect a network we can study the robustness of the network. The robustness of a complex system in graph theory, is the ability of the network to maintain its connectivity after the removal of some nodes or edges. The process of changing of a graph from being connected to being disconnected, via deletion of nodes or edges, is called graph melting. We introduce a melting phase transition for simple connected graphs and networks faced with external perturbations with positive second largest eigenvalue λ2 > 0. In order to calibrate our method of studying network robustness, we consider the network-theoretic representation of some materials which, in the real-world, are affected by melting. In particular, we will consider granular materials. A granular material is a material which is composed of discrete macroscopic solid particles, for example, sand, rice, and coffee. Granular materials are commonly used in a wide range of real world applications. There are already various models of granular materials within network theory, which has allowed us to study the structure and physical behaviour of such systems when they have an external perturbations applied to them. In this thesis, we represent granular solids by simple graphs capturing their topological structure and ordering, in order to study their robustness and the melting process. The melting process is related to the algebraic structure of the adjacency matrix of the graph and the concept of network communicability. At the melting phase transition, a graph in question transfers from being connected to being disconnected. We study melting in graphs with the second largest eigenvalue being positive, namely, in windmill graphs, dumbbell graphs and cycle graphs. Also, we investigate melting in complete multipartite graphs where the second largest eigenvalue is non-positive. We found a melting phase transition in simple connected graphs with λ2 > 0 and λ2 � λ3, which resembles the melting process of a given system. We found that there is no melting phase transition in complete multipartite graphs. Also, we found the spectral decomposition for dumbbell graphs and complete multipartite graphs, which until now have not been done. Moreover, in this thesis, we show that crystalline-like granular materials melt at lower temperatures and display a sharper transition between solid to liquid phases than amorphous granular materials. In addition, we show the evolution mechanism of melting in these granular materials with tools from network theory. In the particular case of crystalline materials, the process starts by melting the central core of the crystal network, then melting spreads out from the central core until the whole network (material) transfers into a liquid. We also investigate computationally the melting process in some real-world networks. We found that the melting process of a network correlates well with the topological structure of the network.Robustness is often regarded as the ability of a given system to maintain its functionality when faced with some external perturbation or when some of its parts fail to operate. The ability of the system to cope with disturbances vary from system to system, and there are many examples in daily life which illustrate this concept. Communication networks, for example, transportation and telephone networks, or the internet, often manage to cope with errors or damage within some of their components without leading to the system failing entirely. As an example, within a social network of employees within a company, the absence of some employees within some given threshold will not lead to failure of the company. However, in a financial network, economic failure in some parts of the system could lead to the complete failure of the entire system. In order to understand how external perturbations or failures within particular parts of the system affect a network we can study the robustness of the network. The robustness of a complex system in graph theory, is the ability of the network to maintain its connectivity after the removal of some nodes or edges. The process of changing of a graph from being connected to being disconnected, via deletion of nodes or edges, is called graph melting. We introduce a melting phase transition for simple connected graphs and networks faced with external perturbations with positive second largest eigenvalue λ2 > 0. In order to calibrate our method of studying network robustness, we consider the network-theoretic representation of some materials which, in the real-world, are affected by melting. In particular, we will consider granular materials. A granular material is a material which is composed of discrete macroscopic solid particles, for example, sand, rice, and coffee. Granular materials are commonly used in a wide range of real world applications. There are already various models of granular materials within network theory, which has allowed us to study the structure and physical behaviour of such systems when they have an external perturbations applied to them. In this thesis, we represent granular solids by simple graphs capturing their topological structure and ordering, in order to study their robustness and the melting process. The melting process is related to the algebraic structure of the adjacency matrix of the graph and the concept of network communicability. At the melting phase transition, a graph in question transfers from being connected to being disconnected. We study melting in graphs with the second largest eigenvalue being positive, namely, in windmill graphs, dumbbell graphs and cycle graphs. Also, we investigate melting in complete multipartite graphs where the second largest eigenvalue is non-positive. We found a melting phase transition in simple connected graphs with λ2 > 0 and λ2 � λ3, which resembles the melting process of a given system. We found that there is no melting phase transition in complete multipartite graphs. Also, we found the spectral decomposition for dumbbell graphs and complete multipartite graphs, which until now have not been done. Moreover, in this thesis, we show that crystalline-like granular materials melt at lower temperatures and display a sharper transition between solid to liquid phases than amorphous granular materials. In addition, we show the evolution mechanism of melting in these granular materials with tools from network theory. In the particular case of crystalline materials, the process starts by melting the central core of the crystal network, then melting spreads out from the central core until the whole network (material) transfers into a liquid. We also investigate computationally the melting process in some real-world networks. We found that the melting process of a network correlates well with the topological structure of the network

In Vitro Evaluation oF Aerosol Drug Delivery With And Without High Flow Nasal Cannula Using Pressurized Metered Dose Inhaler And Jet Nebulizer in Pediatrics

Background: HFNC system is a novel device used with aerosol therapy and seems to be rapidly accepted. Although there are some studies conducted on HFNC and vibrating mesh nebulizer, the effect of HFNC on aerosol delivery using jet nebulizer or pressurized metered-dose inhaler (pMDI) has not been reported. In an effort to examine the effect of HFNC on aerosol deposition, this study was conducted to quantify aerosol drug delivery with or without a HFNC using either pMDI or jet nebulizer. Methodology: The SAINT model, attached to an absolute filter (Respirgard II, Vital Signs Colorado Inc., Englewood, CO, USA) for aerosol collection, was connected to a pediatric breathing simulator (Harvard Apparatus, Model 613, South Natick, MA, USA). To keep the filter and the SAINT model in upright position to collect aerosolized drug, an elbow adapter was connected between the absolute filter and the breathing simulator. An infant HFNC (Optiflow, Fisher & Paykel Healthcare LTD., Auckland, New Zealand) ran at 3 l/min O2 was attached to the nares of the SAINT model. Breathing parameters used in this study were Vt of 100 mL, RR of 30 breaths/min, and I:E ratio of 1: 1.4. Aerosol drug was administered using: 1) Misty-neb jet nebulizer (Allegiance Healthcare, McGaw Park, Illinois, USA) powered by air at 8 l/min using pediatric aerosol facemask (B&F Medical, Allied Healthcare Products, Saint Louis, MO, USA) to deliver albuterol sulfate (2.5 mg/3 mL NS), and 2) Four actuations of Ventolin HFA pMDI (90 μg/puff) (GlaxoSmithKline, Research Triangle Park, NC, USA) combined with VHC (AeroChamber plus with Flow-Vu, Monaghan Medical, Plattsburgh, NY, USA). Aerosol was administered to the model with and without the HFNC and another without (n=3). Drug was collected on an absolute filter, eluted and measured using spectrophotometry. Independent t tests were performed for data analysis. Statistical significance was determined with a p value of \u3c0.05. Results: The mean inhaled mass percent was greatest for pMDI with (p = 0.0001) or without HFNC (p = 0.003). Removing HFNC from the nares before aerosol treatment trended to increase drug delivery with the jet nebulizer (p = 0.024), and increased drug delivery by 6 fold with pMDI (p = 0.003). Conclusions: Aerosol drug may be administered in pediatrics receiving HFNC therapy using either jet nebulizer or pMDI. However, using pMDI, either with or without HFNC, is the best option. When delivering medical aerosol by mask, whether by jet nebulizer or pMDI, removing HFNC led to an increase in inhaled mass percent. However, the benefit of increased aerosol delivery must be weighed against the risk of lung derecruitment when nasal prongs are removed

Diabetic analytics: proposed conceptual data mining approaches in type 2 diabetes dataset

Diabetes is a fast spreading illness, which makes to worry millions of people around the globe. The people affected by type-2 diabetes are rapidly increasing and there are no effective diagnostic systems to control the diabetics. As per global health statistics, in western countries, population effected by type 2 diabetics are higher in rate and cost factor for treatment is increasing. There are no effective methods to eradicate the diabetes and it leads to carry out an investigative study on this disease. In existing reviews, researchers are using data analysis approaches to link the cause for diabetes with the patients based on the diet, life style, inheritance details, age factor, medical history, etc. to identify the root cause of the problem. By having multiple key factors and historical datasets, there are some data mining tools were developed, to generate new rules on the root cause of the disease and discover new knowledge from the past data’s, but the accuracy was not promising. The main objective of this paper is to carry out a detail literature review and design a conceptual data mining method at initial stage and implement it to improve the result accuracy compared to other classifiers. In this research, two data-mining algorithm were proposed at conceptual level: Self Organizing Map (SOM) and Random Forest Algorithm, which is applied on adult population datasets. The data set used for this research are from UCI machine Learning Repository: Diabetes Dataset. In this paper, data mining algorithms were discussed and implementation results were evaluated. Based on the result performance evaluation, Self-organizing maps have performed better compared to the Random Forest and other data mining algorithms such as naïve Bayes, decision tree, SVM and MLP for diagnosing the diabetes with better accuracy. In future, once system is implemented, it can be integrated with diabetic detector device for faster diagnosis of TYPE 2 diabetes disease