An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments

Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user's preference and location.This work was supported by the National Natural Science Foundation of China under Grant 61372187. G. Min’s work was partly supported by the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939. L. T. Yang is the corresponding author

Optical Activity Enhanced by Strong Inter-molecular Coupling in Planar Chiral Metamaterials

The polarization of light can be rotated in materials with an absence of molecular or structural mirror symmetry. While this rotating ability is normally rather weak in naturally occurring chiral materials, artificial chiral metamaterials have demonstrated extraordinary rotational ability by engineering intra-molecular couplings. However, while in general, chiral metamaterials can exhibit strong rotatory power at or around resonances, they convert linearly polarized waves into elliptically polarized ones. Here, we demonstrate that strong inter-molecular coupling through a small gap between adjacent chiral metamolecules can lead to a broadband enhanced rotating ability with pure rotation of linearly polarized electromagnetic waves. Strong inter-molecular coupling leads to nearly identical behaviour in magnitude, but engenders substantial difference in phase between transmitted left and right-handed waves

SU(7) Unification of SU(3)_C*SU(4)_W* U(1)_{B-L}

We propose the SUSY SU(7) unification of the SU(3)_C* SU(4)_W* U(1)_{B-L} model. Such unification scenario has rich symmetry breaking chains in a five-dimensional orbifold. We study in detail the SUSY SU(7) symmetry breaking into SU(3)_C* SU(4)_W* U(1)_{B-L} by boundary conditions in a Randall-Sundrum background and its AdS/CFT interpretation. We find that successful gauge coupling unification can be achieved in our scenario. Gauge unification favors low left-right and unification scales with tree-level \sin^2\theta_W=0.15. We use the AdS/CFT dual of the conformal supersymmetry breaking scenario to break the remaining N=1 supersymmetry. We employ AdS/CFT to reproduce the NSVZ formula and obtain the structure of the Seiberg duality in the strong coupling region for 3/2N_c<N_F<3N_C. We show that supersymmetry is indeed broken in the conformal supersymmetry breaking scenario with a vanishing singlet vacuum expectation value.Comment: 25 pages, 1 figure

Non-Invasive Brain-to-Brain Interface (BBI): Establishing Functional Links between Two Brains

Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0 +/- 3.0% accuracy, with a time delay of 1.59 +/- 1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.open12

Facile control of nanoporosity in Cellulose Acetate using Nickel(II) nitrate additive and water pressure treatment for highly efficient battery gel separators

We succeed in fabricating nearly straight nanopores in cellulose acetate (CA) polymers for use as battery gel separators by utilizing an inorganic hexahydrate (Ni(NO3)2??6H2O) complex and isostatic water pressure treatment. The continuous nanopores are generated when the polymer film is exposed to isostatic water pressure after complexing the nickel(II) nitrate hexahydrate (Ni(NO3)2??6H2O) with the CA. These results can be attributed to the manner in which the polymer chains are weakened because of the plasticization effect of the Ni(NO3)2??6H2O that is incorporated into the CA. Furthermore, we performed extensive molecular dynamics simulation for confirming the interaction between electrolyte and CA separator. The well controlled CA membrane after water pressure treatment enables fabrication of highly reliable cell by utilizing 2032-type coin cell structure. The resulting cell performance exhibits not only the effect of the physical morphology of CA separator, but also the chemical interaction of electrolyte with CA polymer which facilitates the Li-ion in the cell.ope

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

Properties of Graphene: A Theoretical Perspective

In this review, we provide an in-depth description of the physics of monolayer and bilayer graphene from a theorist's perspective. We discuss the physical properties of graphene in an external magnetic field, reflecting the chiral nature of the quasiparticles near the Dirac point with a Landau level at zero energy. We address the unique integer quantum Hall effects, the role of electron correlations, and the recent observation of the fractional quantum Hall effect in the monolayer graphene. The quantum Hall effect in bilayer graphene is fundamentally different from that of a monolayer, reflecting the unique band structure of this system. The theory of transport in the absence of an external magnetic field is discussed in detail, along with the role of disorder studied in various theoretical models. We highlight the differences and similarities between monolayer and bilayer graphene, and focus on thermodynamic properties such as the compressibility, the plasmon spectra, the weak localization correction, quantum Hall effect, and optical properties. Confinement of electrons in graphene is nontrivial due to Klein tunneling. We review various theoretical and experimental studies of quantum confined structures made from graphene. The band structure of graphene nanoribbons and the role of the sublattice symmetry, edge geometry and the size of the nanoribbon on the electronic and magnetic properties are very active areas of research, and a detailed review of these topics is presented. Also, the effects of substrate interactions, adsorbed atoms, lattice defects and doping on the band structure of finite-sized graphene systems are discussed. We also include a brief description of graphane -- gapped material obtained from graphene by attaching hydrogen atoms to each carbon atom in the lattice.Comment: 189 pages. submitted in Advances in Physic

Urinary levels of N-nitroso compounds in relation to risk of gastric cancer: Findings from the Shanghai cohort study

Background: N-Nitroso compounds are thought to play a significant role in the development of gastric cancer. Epidemiological data, however, are sparse in examining the associations between biomarkers of exposure to N-nitroso compounds and the risk of gastric cancer. Methods: A nested case-control study within a prospective cohort of 18,244 middle-aged and older men in Shanghai, China, was conducted to examine the association between urinary level of N-nitroso compounds and risk of gastric cancer. Information on demographics, usual dietary intake, and use of alcohol and tobacco was collected through in-person interviews at enrollment. Urinary levels of nitrate, nitrite, N-nitroso-2-methylthiazolidine-4-carboxylic acid (NMTCA), N-nitrosoproline (NPRO), N-nitrososarcosine (NSAR), N-nitrosothiazolidine-4-carboxylic acid (NTCA), as well as serum H. pylori antibodies were quantified in 191 gastric cancer cases and 569 individually matched controls. Logistic regression method was used to assess the association between urinary levels of N-nitroso compounds and risk of gastric cancer. Results: Compared with controls, gastric cancer patients had overall comparable levels of urinary nitrate, nitrite, and N-nitroso compounds. Among individuals seronegative for antibodies to H. pylori, elevated levels of urinary nitrate were associated with increased risk of gastric cancer. The multivariate-adjusted odds ratios for the second and third tertiles of nitrate were 3.27 (95% confidence interval = 0.76-14.04) and 4.82 (95% confidence interval = 1.05-22.17), respectively, compared with the lowest tertile (P for trend = 0.042). There was no statistically significant association between urinary levels of nitrite or N-nitroso compounds and risk of gastric cancer. Urinary NMTCA level was significantly associated with consumption of alcohol and preserved meat and fish food items. Conclusion: The present study demonstrates that exposure to nitrate, a precursor of N-nitroso compounds, may increase the risk of gastric cancer among individuals without a history of H. pylori infection

Potential applications of nanotechnology in thermochemical conversion of microalgal biomass

The rapid decrease in fossil reserves has significantly increased the demand of renewable and sustainable energy fuel resources. Fluctuating fuel prices and significant greenhouse gas (GHG) emission levels have been key impediments associated with the production and utilization of nonrenewable fossil fuels. This has resulted in escalating interests to develop new and improve inexpensive carbon neutral energy technologies to meet future demands. Various process options to produce a variety of biofuels including biodiesel, bioethanol, biohydrogen, bio-oil, and biogas have been explored as an alternative to fossil fuels. The renewable, biodegradable, and nontoxic nature of biofuels make them appealing as alternative fuels. Biofuels can be produced from various renewable resources. Among these renewable resources, algae appear to be promising in delivering sustainable energy options. Algae have a high carbon dioxide (CO2) capturing efficiency, rapid growth rate, high biomass productivity, and the ability to grow in non-potable water. For algal biomass, the two main conversion pathways used to produce biofuel include biochemical and thermochemical conversions. Algal biofuel production is, however, challenged with process scalability for high conversion rates and high energy demands for biomass harvesting. This affects the viable achievement of industrial-scale bioprocess conversion under optimum economy. Although algal biofuels have the potential to provide a sustainable fuel for future, active research aimed at improving upstream and downstream technologies is critical. New technologies and improved systems focused on photobioreactor design, cultivation optimization, culture dewatering, and biofuel production are required to minimize the drawbacks associated with existing methods. Nanotechnology has the potential to address some of the upstream and downstream challenges associated with the development of algal biofuels. It can be applied to improve system design, cultivation, dewatering, biomass characterization, and biofuel conversion. This chapter discusses thermochemical conversion of microalgal biomass with recent advances in the application of nanotechnology to enhance the development of biofuels from algae. Nanotechnology has proven to improve the performance of existing technologies used in thermochemical treatment and conversion of biomass. The different bioprocess aspects, such as reactor design and operation, analytical techniques, and experimental validation of kinetic studies, to provide insights into the application of nanotechnology for enhanced algal biofuel production are addressed

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer