MobiFuzzyTrust: An efficient fuzzy trust inference mechanism in mobile social networks

PublishedJournal Article© 2014 IEEE. Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.This work was partly supported by the National Nature Science Foundation of China under grant 61201219 and the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939

An optimized computational model for multi-community-cloud social collaboration

PublishedCommunity Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely MG3. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in MG3. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds

A tensor-based approach for big data representation and dimensionality reduction

PublishedJournal Article© 2013 IEEE. Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction

Molecular composition of particulate matter emissions from dung and brushwood burning household cookstoves in Haryana, India

Emissions of airborne particles from biomass burning are a significant source of black carbon (BC) and brown carbon (BrC) in rural areas of developing countries where biomass is the predominant energy source for cooking and heating. This study explores the molecular composition of organic aerosols from household cooking emissions with a focus on identifying fuel-specific compounds and BrC chromophores. Traditional meals were prepared by a local cook with dung and brushwood-fueled cookstoves in a village in Palwal district, Haryana, India. Cooking was done in a village kitchen while controlling for variables including stove type, fuel moisture, and meal. Fine particulate matter (PM2.5) emissions were collected on filters, and then analyzed via nanospray desorption electrospray ionization-high-resolution mass spectrometry (nano-DESI-HRMS) and high-performance liquid chromatography-photodiode array-high-resolution mass spectrometry (HPLC-PDA-HRMS) techniques. The nano-DESI-HRMS analysis provided an inventory of numerous compounds present in the particle phase. Although several compounds observed in this study have been previously characterized using gas chromatography methods a majority of the species in the nano-DESI spectra were newly observed biomass burning compounds. Both the stove (chulha or angithi) and the fuel (brushwood or dung) affected the composition of organic aerosols. The geometric mean of the PM2.5 emission factor and the observed molecular complexity increased in the following order: brushwood-chulha (7.3±1.8 g kg-1 dry fuel, 93 compounds), dung-chulha (21.1±4.2 g kg-1 dry fuel, 212 compounds), and dung-angithi (29.8±11.5 g kg-1 dry fuel, 262 compounds). The mass-normalized absorption coefficient (MACbulk) for the organic-solvent extractable material for brushwood PM2.5 was 3.7±1.5 and 1.9±0.8m2 g-1 at 360 and 405 nm, respectively, which was approximately a factor of two higher than that for dung PM2.5. The HPLC-PDA-HRMS analysis showed that, regardless of fuel type, the main chromophores were CxHyOz lignin fragments. The main chromophores accounting for the higher MACbulk values of brushwood PM2.5 were C8H10O3 (tentatively assigned to syringol), nitrophenols C8H9NO4, and C10H10O3 (tentatively assigned to methoxycinnamic acid)

The highly rearranged mitochondrial genomes of the crabs Maja crispata and Maja squinado (Majidae) and gene order evolution in Brachyura

Abstract We sequenced the mitochondrial genomes of the spider crabs Maja crispata and Maja squinado (Majidae, Brachyura). Both genomes contain the whole set of 37 genes characteristic of Bilaterian genomes, encoded on both \u3b1- and \u3b2-strands. Both species exhibit the same gene order, which is unique among known animal genomes. In particular, all the genes located on the \u3b2-strand form a single block. This gene order was analysed together with the other nine gene orders known for the Brachyura. Our study confirms that the most widespread gene order (BraGO) represents the plesiomorphic condition for Brachyura and was established at the onset of this clade. All other gene orders are the result of transformational pathways originating from BraGO. The different gene orders exhibit variable levels of genes rearrangements, which involve only tRNAs or all types of genes. Local homoplastic arrangements were identified, while complete gene orders remain unique and represent signatures that can have a diagnostic value. Brachyura appear to be a hot-spot of gene order diversity within the phylum Arthropoda. Our analysis, allowed to track, for the first time, the fully evolutionary pathways producing the Brachyuran gene orders. This goal was achieved by coupling sophisticated bioinformatic tools with phylogenetic analysis

The extraordinary evolutionary history of the reticuloendotheliosis viruses

The reticuloendotheliosis viruses (REVs) comprise several closely related amphotropic retroviruses isolated from birds. These viruses exhibit several highly unusual characteristics that have not so far been adequately explained, including their extremely close relationship to mammalian retroviruses, and their presence as endogenous sequences within the genomes of certain large DNA viruses. We present evidence for an iatrogenic origin of REVs that accounts for these phenomena. Firstly, we identify endogenous retroviral fossils in mammalian genomes that share a unique recombinant structure with REVs—unequivocally demonstrating that REVs derive directly from mammalian retroviruses. Secondly, through sequencing of archived REV isolates, we confirm that contaminated Plasmodium lophurae stocks have been the source of multiple REV outbreaks in experimentally infected birds. Finally, we show that both phylogenetic and historical evidence support a scenario wherein REVs originated as mammalian retroviruses that were accidentally introduced into avian hosts in the late 1930s, during experimental studies of P. lophurae, and subsequently integrated into the fowlpox virus (FWPV) and gallid herpesvirus type 2 (GHV-2) genomes, generating recombinant DNA viruses that now circulate in wild birds and poultry. Our findings provide a novel perspective on the origin and evolution of REV, and indicate that horizontal gene transfer between virus families can expand the impact of iatrogenic transmission events

Internet addiction, fatigue, and sleep problems among adolescent students: a large-scale study

Aim: The aim of the present study was to examine the association between Internet Addiction (IA), fatigue, and sleep problems among university students. Methods: A total of 3,000 Turkish students aged 18 to 25 years were approached and 2,350 students (78.3%) participated in this cross-sectional study from April 2017 to September 2017 in public and private universities in Istanbul. Data were collected via a structured questionnaire including socio-demographic details, lifestyle and dietary habits, Internet Addiction Test (IAT), Fatigue Scale, and Epworth Sleepiness Scale [ESS]. Descriptive statistics, multivariate and factorial analyses were performed. Results: The overall prevalence of IA among the studied population was 17.7%. There were significant differences between gender, family income, father’s occupation, school performance, frequency and duration of watching television, physical activity, internet use duration, and sleep duration (all p<0.001). Significant differences were also found between participants with IA and those without IA in having headaches, blurred vision, double vision, hurting eyes, hearing problems, and eating fast food frequently (all p<0.001). Using multivariate regression analysis, the duration of internet use, physical and mental symptoms, headache, hurting eyes, tired eyes, hearing problems and ESS scores were significantly associated with (and primary predictors of) IA. Conclusion: The present study demonstrated that IA was associated with poor dietary habits, sleep problems, and fatigue symptoms

Understanding the mechanisms of cooperative physico-chemical treatment and mechanical disintegration of biomass as a route for enhancing enzyme saccharification

A novel chemico-kinetic disintegration model has been applied to study the cooperative relationship between physico-chemical treatment and supplementary wet-state milling of biomass, as an efficient process route to achieve high enzyme accessibility. Wheat straw, Miscanthus and short-rotation willow were studied as three contrasting biomass species, which were subjected to controlled hydrothermal pretreatment using a microwave reactor, followed by controlled wet-state ball-milling. Comparative particle disintegration behaviour and related enzyme digestibilities have been interpreted on the basis of model parameters and with evaluation of textural and chemical differences in tissue structures, aided by the application of specific material characterisation techniques. Supplementary milling led to a 1.3×, 1.6× and 3× enhancement in glucose saccharification yield after 24 h for straw, Miscanthus and willow, respectively, following a standardised 10-min hydrothermal treatment, with corresponding milling energy savings of 98, 97 and 91% predicted from the model, compared to the unmilled case. The results confirm the viability of pretreatment combined with supplementary wet-milling as an efficient process route. The results will be valuable in understanding the key parameters for process design and optimisation and also the key phenotypical parameters for feedstock breeding and selection for highest saccharification yield

Label-free Detection of Influenza Viruses using a Reduced Graphene Oxide-based Electrochemical Immunosensor Integrated with a Microfluidic Platform

Reduced graphene oxide (RGO) has recently gained considerable attention for use in electrochemical biosensing applications due to its outstanding conducting properties and large surface area. This report presents a novel microfluidic chip integrated with an RGO-based electrochemical immunosensor for label-free detection of an influenza virus, H1N1. Three microelectrodes were fabricated on a glass substrate using the photolithographic technique, and the working electrode was functionalized using RGO and monoclonal antibodies specific to the virus. These chips were integrated with polydimethylsiloxane microchannels. Structural and morphological characterizations were performed using X-ray photoelectron spectroscopy and scanning electron microscopy. Electrochemical studies revealed good selectivity and an enhanced detection limit of 0.5 PFU mL(-1), where the chronoamperometric current increased linearly with H1N1 virus concentration within the range of 1 to 104 PFU mL(-1) (R-2 = 0.99). This microfluidic immunosensor can provide a promising platform for effective detection of biomolecules using minute samples.ope