Design of a ferrite rod antenna for harvesting energy from medium wave broadcast signals

Radio frequency (RF) energy harvesting is an emerging technology that has the potential to eliminate the need for batteries and reduce maintenance costs of sensing applications. The antenna is one of the critical components that determines its performance and while antenna design has been well researched for the purpose of communication, the design for RF energy harvesting applications has not been widely addressed. The authors present an optimised design for such an antenna for harvesting energy from medium wave broadcast transmissions. They derive and use a model for computing the optimal antenna configuration given application requirements on output voltage and power, material costs and physical dimensions. Design requirements for powering autonomous smart meters have been considered. The proposed approach was used to obtain the antenna configuration that is able to deliver 1 mW of power to 1 kΩ load at a distance of up to 9 km, sufficient to replace batteries on low-power sensing applications. Measurements using a prototype device have been used to verify the authors simulations

Multi-Layer Cyber-Physical Security and Resilience for Smart Grid

The smart grid is a large-scale complex system that integrates communication technologies with the physical layer operation of the energy systems. Security and resilience mechanisms by design are important to provide guarantee operations for the system. This chapter provides a layered perspective of the smart grid security and discusses game and decision theory as a tool to model the interactions among system components and the interaction between attackers and the system. We discuss game-theoretic applications and challenges in the design of cross-layer robust and resilient controller, secure network routing protocol at the data communication and networking layers, and the challenges of the information security at the management layer of the grid. The chapter will discuss the future directions of using game-theoretic tools in addressing multi-layer security issues in the smart grid.Comment: 16 page

Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses

BackgroundDiabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis.MethodsWe used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test).ResultsBased on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 - ORAA: 1.62, 95% CI: 1.34-1.96; ORAG: 1.41, 95% CI: 1.30-1.54; ORGG: 1.22, 95% CI: 1.13-1.31; p-value(3-d.f.): 5.46 x 10(-11)) and 13q14.13 (rs9526201, LRCH1 - ORGG: 2.11, 95% CI: 1.56-2.83; ORGA: 1.52, 95% CI: 1.38-1.68; ORAA: 1.13, 95% CI: 1.06-1.21; p-value(2-d.f.): 7.84 x 10(-09)).DiscussionThese results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship

The field high-amplitude SX Phe variable BL Cam: results from a multisite photometric campaign. II. Evidence of a binary - possibly triple - system

Short-period high-amplitude pulsating stars of Population I ($\delta$ Sct stars) and II (SX Phe variables) exist in the lower part of the classical (Cepheid) instability strip. Most of them have very simple pulsational behaviours, only one or two radial modes being excited. Nevertheless, BL Cam is a unique object among them, being an extreme metal-deficient field high-amplitude SX Phe variable with a large number of frequencies. Based on a frequency analysis, a pulsational interpretation was previously given. aims heading (mandatory) We attempt to interpret the long-term behaviour of the residuals that were not taken into account in the previous Observed-Calculated (O-C) short-term analyses. methods heading (mandatory) An investigation of the O-C times has been carried out, using a data set based on the previous published times of light maxima, largely enriched by those obtained during an intensive multisite photometric campaign of BL Cam lasting several months. results heading (mandatory) In addition to a positive (161 $\pm$ 3) x 10$^{-9}$ yr$^{-1}$ secular relative increase in the main pulsation period of BL Cam, we detected in the O-C data short- (144.2 d) and long-term ($\sim$ 3400 d) variations, both incompatible with a scenario of stellar evolution. conclusions heading (mandatory) Interpreted as a light travel-time effect, the short-term O-C variation is indicative of a massive stellar component (0.46 to 1 M_{\sun}) with a short period orbit (144.2 d), within a distance of 0.7 AU from the primary. More observations are needed to confirm the long-term O-C variations: if they were also to be caused by a light travel-time effect, they could be interpreted in terms of a third component, in this case probably a brown dwarf star ($\geq$ 0.03 \ M_{\sun}), orbiting in $\sim$ 3400 d at a distance of 4.5 AU from the primary.Comment: 7 pages, 5 figures, accepted for publication in A&

Acrylamide and Glycidamide Hemoglobin Adducts and Epithelial Ovarian Cancer: A Nested Case-Control Study in Nonsmoking Postmenopausal Women from the EPIC Cohort

Background: Acrylamide was classified as “probably carcinogenic to humans (group 2A)” by the International Agency for Research on Cancer. Epithelial ovarian cancer (EOC) is the fourth cause of cancer mortality in women. Five epidemiological studies have evaluated the association between EOC risk and dietary acrylamide intake assessed using food frequency questionnaires, and one nested case–control study evaluated hemoglobin adducts of acrylamide (HbAA) and its metabolite glycidamide (HbGA) and EOC risk; the results of these studies were inconsistent. Methods: A nested case–control study in nonsmoking postmenopausal women (334 cases, 417 controls) was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Unconditional logistic regression models were used to estimate ORs and 95% confidence intervals (CI) for the association between HbAA, HbGA, HbAA+HbGA, and HbGA/HbAA and EOC and invasive serous EOC risk. Results: No overall associations were observed between biomarkers of acrylamide exposure analyzed in quintiles and EOC risk; however, positive associations were observed between some middle quintiles of HbGA and HbAA+HbGA. Elevated but nonstatistically significant ORs for serous EOC were observed for HbGA and HbAA+HbGA (ORQ5vsQ1, 1.91; 95% CI, 0.96–3.81 and ORQ5vsQ1, 1.90; 95% CI, 0.94–3.83, respectively); however, no linear dose–response trends were observed. Conclusion: This EPIC nested case–control study failed to observe a clear association between biomarkers of acrylamide exposure and the risk of EOC or invasive serous EOC. Impact: It is unlikely that dietary acrylamide exposure increases ovarian cancer risk; however, additional studies with larger sample size should be performed to exclude any possible association with EOC risk

An epidemiological model for prediction of endometrial cancer risk in Europe

Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of data from 201,811 women (mostly aged 30–65 years) including 855 incident EC cases from eight countries in the European Prospective Investigation into Cancer and Nutrition cohort, a model to predict EC was developed. A step-wise model selection process was used to select confirmed predictive epidemiologic risk factors. Piece-wise constant hazard rates in 5-year age-intervals were estimated in a cause-specific competing risks model, five-fold-cross-validation was applied for internal validation. Risk factors included in the risk prediction model were body-mass index (BMI), menopausal status, age at menarche and at menopause, oral contraceptive use, overall and by different BMI categories and overall duration of use, parity, age at first full-term pregnancy, duration of menopausal hormone therapy and smoking status (specific for pre, peri- and post-menopausal women). These variables improved the discriminating capacity to predict risk over 5 years from 71 % for a model based on age alone to 77 % (overall C statistic), and the model was well-calibrated (ratio of expected to observed cases = 0.99). Our model could be used for the identification of women at increased risk of EC in Western Europe. To achieve an EC-risk model with general validity, a large-scale cohort-consortium approach would be needed to assess and adjust for population variation

Insulin-like growth factor I and risk of epithelial invasive ovarian cancer by tumour characteristics: results from the EPIC cohort

Background: Prospective studies on insulin-like growth factor I (IGF-I) and epithelial ovarian cancer (EOC) risk are inconclusive. Data suggest risk associations vary by tumour characteristics. Methods: We conducted a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC) to evaluate IGF-I concentrations and EOC risk by tumour characteristics (n=565 cases). Multivariable conditional logistic regression models were used to estimate associations. Results: We observed no association between IGF-I and EOC overall or by tumour characteristics. Conclusions: In the largest prospective study to date was no association between IGF-I and EOC risk. Pre-diagnostic serum IGF-I concentrations may not influence EOC risk

Asteroids' physical models from combined dense and sparse photometry and scaling of the YORP effect by the observed obliquity distribution

The larger number of models of asteroid shapes and their rotational states derived by the lightcurve inversion give us better insight into both the nature of individual objects and the whole asteroid population. With a larger statistical sample we can study the physical properties of asteroid populations, such as main-belt asteroids or individual asteroid families, in more detail. Shape models can also be used in combination with other types of observational data (IR, adaptive optics images, stellar occultations), e.g., to determine sizes and thermal properties. We use all available photometric data of asteroids to derive their physical models by the lightcurve inversion method and compare the observed pole latitude distributions of all asteroids with known convex shape models with the simulated pole latitude distributions. We used classical dense photometric lightcurves from several sources and sparse-in-time photometry from the U.S. Naval Observatory in Flagstaff, Catalina Sky Survey, and La Palma surveys (IAU codes 689, 703, 950) in the lightcurve inversion method to determine asteroid convex models and their rotational states. We also extended a simple dynamical model for the spin evolution of asteroids used in our previous paper. We present 119 new asteroid models derived from combined dense and sparse-in-time photometry. We discuss the reliability of asteroid shape models derived only from Catalina Sky Survey data (IAU code 703) and present 20 such models. By using different values for a scaling parameter cYORP (corresponds to the magnitude of the YORP momentum) in the dynamical model for the spin evolution and by comparing synthetics and observed pole-latitude distributions, we were able to constrain the typical values of the cYORP parameter as between 0.05 and 0.6.Comment: Accepted for publication in A&A, January 15, 201

Seismically induced landslide hazard and exposure modelling in Southern California based on the 1994 Northridge, California earthquake event

Quantitative modelling of landslide hazard, as opposed to landslide susceptibility, as a function of the earthquake trigger is vital in understanding and assessing future potential exposure to landsliding. Logistic regression analysis is a method commonly used to assess susceptibility to landsliding; however, estimating probability of landslide hazard as a result of an earthquake trigger is rarely undertaken. This paper utilises a very detailed landslide inventory map and a comprehensive dataset on peak ground acceleration for the 1994 Mw6.7 Northridge earthquake event to fit a landslide hazard logistic regression model. The model demonstrates a high success rate for estimating probability of landslides as a result of earthquake shaking. Seven earthquake magnitude scenarios were simulated using the Open Source Seismic Hazard Analysis (OpenSHA) application to simulate peak ground acceleration, a covariate of landsliding, for each event. The exposure of assets such as population, housing and roads to high levels of shaking and high probabilities of landsliding was estimated for each scenario. There has been urban development in the Northridge region since 1994, leading to an increase in prospective exposure of assets to the earthquake and landslide hazards in the event of a potential future earthquake. As the earthquake scenario magnitude increases, the impact from earthquake shaking initially increases then quickly levels out, but potential losses from landslides increase at a rapid rate. The modelling approach, as well as the specific model, developed in this paper can be used to estimate landslide probabilities as a result of an earthquake event for any scenario where the peak ground acceleration variable is available