Showing papers by "Virginia Tech published in 2012"

Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology

Abstract: This paper extends the unified theory of acceptance and use of technology (UTAUT) to study acceptance and use of technology in a consumer context. Our proposed UTAUT2 incorporates three constructs into UTAUT: hedonic motivation, price value, and habit. Individual differences — namely, age, gender, and experience — are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. Results from a two-stage online survey, with technology use data collected four months after the first survey, of 1,512 mobile Internet consumers supported our model. Compared to UTAUT, the extensions proposed in UTAUT2 produced a substantial improvement in the variance explained in behavioral intention (56 percent to 74 percent) and technology use (40 percent to 52 percent). The theoretical and managerial implications of these results are discussed.

Observation of Electron-Antineutrino Disappearance at Daya Bay

Abstract: The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle θ13 with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW_(th) reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43 000 ton–GW_(th)–day live-time exposure in 55 days, 10 416 (80 376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is R=0.940± 0.011(stat.)±0.004(syst.). A rate-only analysis finds sin^22θ_(13)=0.092±0.016(stat.)±0.005(syst.) in a three-neutrino framework.

H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations

Abstract: The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

Experimental design

Abstract: Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Averting biodiversity collapse in tropical forest protected areas

Abstract: The rapid disruption of tropical forests probably imperils global biodiversity more than any other contemporary phenomenon(1-3). With deforestation advancing quickly, protected areas are increasingly becoming final refuges for threatened species and natural ecosystem processes. However, many protected areas in the tropics are themselves vulnerable to human encroachment and other environmental stresses(4-9). As pressures mount, it is vital to know whether existing reserves can sustain their biodiversity. A critical constraint in addressing this question has been that data describing a broad array of biodiversity groups have been unavailable for a sufficiently large and representative sample of reserves. Here we present a uniquely comprehensive data set on changes over the past 20 to 30 years in 31 functional groups of species and 21 potential drivers of environmental change, for 60 protected areas stratified across the world's major tropical regions. Our analysis reveals great variation in reserve 'health': about half of all reserves have been effective or performed passably, but the rest are experiencing an erosion of biodiversity that is often alarmingly widespread taxonomically and functionally. Habitat disruption, hunting and forest-product exploitation were the strongest predictors of declining reserve health. Crucially, environmental changes immediately outside reserves seemed nearly as important as those inside in determining their ecological fate, with changes inside reserves strongly mirroring those occurring around them. These findings suggest that tropical protected areas are often intimately linked ecologically to their surrounding habitats, and that a failure to stem broad-scale loss and degradation of such habitats could sharply increase the likelihood of serious biodiversity declines.

Psi4: an open-source ab initio electronic structure program

Abstract: The Psi4 program is a new approach to modern quantum chemistry, encompassing Hartree–Fock and density-functional theory to configuration interaction and coupled cluster. The program is written entirely in C++ and relies on a new infrastructure that has been designed to permit high-efficiency computations of both standard and emerging electronic structure methods on conventional and high-performance parallel computer architectures. Psi4 offers flexible user input built on the Python scripting language that enables both new and experienced users to make full use of the program's capabilities, and even to implement new functionality with moderate effort. To maximize its impact and usefulness, Psi4 is available through an open-source license to the entire scientific community. © 2011 John Wiley & Sons, Ltd.

Regulation of insulin synthesis and secretion and pancreatic Beta-cell dysfunction in diabetes

Abstract: Pancreatic β-cell dysfunction plays an important role in the pathogenesis of both type 1 and type 2 diabetes. Insulin, which is produced in β-cells, is a critical regulator of metabolism. Insulin is synthesized as preproinsulin and processed to proinsulin. Proinsulin is then converted to insulin and C-peptide and stored in secretary granules awaiting release on demand. Insulin synthesis is regulated at both the transcriptional and translational level. The cis-acting sequences within the 5' flanking region and trans-activators including paired box gene 6 (PAX6), pancreatic and duodenal homeobox- 1(PDX-1), MafA, and β-2/Neurogenic differentiation 1 (NeuroD1) regulate insulin transcription, while the stability of preproinsulin mRNA and its untranslated regions control protein translation. Insulin secretion involves a sequence of events in β-cells that lead to fusion of secretory granules with the plasma membrane. Insulin is secreted primarily in response to glucose, while other nutrients such as free fatty acids and amino acids can augment glucose-induced insulin secretion. In addition, various hormones, such as melatonin, estrogen, leptin, growth hormone, and glucagon like peptide-1 also regulate insulin secretion. Thus, the β-cell is a metabolic hub in the body, connecting nutrient metabolism and the endocrine system. Although an increase in intracellular [Ca2+] is the primary insulin secretary signal, cAMP signaling- dependent mechanisms are also critical in the regulation of insulin secretion. This article reviews current knowledge on how β-cells synthesize and secrete insulin. In addition, this review presents evidence that genetic and environmental factors can lead to hyperglycemia, dyslipidemia, inflammation, and autoimmunity, resulting in β-cell dysfunction, thereby triggering the pathogenesis of diabetes.

The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry.

Abstract: The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally-centered endeavor aimed at creating a large-scale (N>1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6-85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

Low-Dose X-ray CT Reconstruction via Dictionary Learning

Abstract: Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

An Algorithm for Intelligent Home Energy Management and Demand Response Analysis

Abstract: A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.

Observation of two charged bottomoniumlike resonances in Υ(5S) decays.

Abstract: We report the observation of two narrow structures in the mass spectra of the pi(+/-) Y(nS) (n = 1, 2, 3) and pi(+/-) h(b)(mP) (m = 1, 2) pairs that are produced in association with a single charged pion in Y(5S) decays The measured masses and widths of the two structures averaged over the five final states are M-1 = (10 6072 +/- 20) MeV/c(2), Gamma(1) =(184 +/- 24) MeV, and M-2 = (10 6522 +/- 15) MeV/c(2), Gamma(2) = (115 +/- 22) MeV The results are obtained with a 1214 fb(-1) data sample collected with the Belle detector in the vicinity of the Y(5S) resonance at the KEKB asymmetric-energy e(+)e(-) collider

Hierarchical Trust Management for Wireless Sensor Networks and its Applications to Trust-Based Routing and Intrusion Detection

Abstract: We propose a highly scalable cluster-based hierarchical trust management protocol for wireless sensor networks (WSNs) to effectively deal with selfish or malicious nodes. Unlike prior work, we consider multidimensional trust attributes derived from communication and social networks to evaluate the overall trust of a sensor node. By means of a novel probability model, we describe a heterogeneous WSN comprising a large number of sensor nodes with vastly different social and quality of service (QoS) behaviors with the objective to yield "ground truth" node status. This serves as a basis for validating our protocol design by comparing subjective trust generated as a result of protocol execution at runtime against objective trust obtained from actual node status. To demonstrate the utility of our hierarchical trust management protocol, we apply it to trust-based geographic routing and trust-based intrusion detection. For each application, we identify the best trust composition and formation to maximize application performance. Our results indicate that trust-based geographic routing approaches the ideal performance level achievable by flooding-based routing in message delivery ratio and message delay without incurring substantial message overhead. For trust-based intrusion detection, we discover that there exists an optimal trust threshold for minimizing false positives and false negatives. Furthermore, trust-based intrusion detection outperforms traditional anomaly-based intrusion detection approaches in both the detection probability and the false positive probability.

Predictors of caregiver burden in caregivers of individuals with dementia

Abstract: kim h., chang m., rose k. & kim s. (2011) Predictors of caregiver burden in caregivers of individuals with dementia. Journal of Advanced Nursing 68(4), 846–855. Abstract Aims. This article is a report on a study of the multidimensional predictors of caregiver burden in caregivers of individuals with dementia using nationally representative data. Background. Caregiver burden affects the health of both caregivers and their care-recipients. Although previous studies identified various predictors of caregiver burden, these predictors have not been confirmed in nationally representative population. Methods. Data for this secondary analysis was provided by the National Alliance for Caregiving, American Association of Retired Persons. The data were collected through a telephone survey of randomly selected adults in seven states in 2003 (weight adjusted n = 302). Descriptive statistics, inter-correlation analysis and a hierarchical multiple regression analysis were performed. Results/findings. Disease-related factors were the most significant predictors, explaining 16% of caregiver burden; these were followed by caregiver socio-demographical factors and caregiving-related factors (F = 21·28, P < 0·01). Significant individual predictors were impairment of activities of daily living or instrumental activities of daily living, the number of hours of caregiving, use of coping strategies, co-residence, spousal status and caregiver gender (P < 0·05). Conclusion. Impaired function in care-recipients predicts caregiver burden, and also interacts with demographical- and caregiving-related factors. Thus, it will be beneficial to both care-recipients and caregivers to target nursing interventions and community services to improve the functional abilities of individuals with dementia.

Securing Personal Health Records in Cloud Computing: Patient-Centric and Fine-Grained Data Access Control in Multi-owner Settings

Abstract: Online personal health record (PHR) enables patients to manage their own medical records in a centralized way, which greatly facilitates the storage, access and sharing of personal health data. With the emergence of cloud computing, it is attractive for the PHR service providers to shift their PHR applications and storage into the cloud, in order to enjoy the elastic resources and reduce the operational cost. However, by storing PHRs in the cloud, the patients lose physical control to their personal health data, which makes it necessary for each patient to encrypt her PHR data before uploading to the cloud servers. Under encryption, it is challenging to achieve fine-grained access control to PHR data in a scalable and efficient way. For each patient, the PHR data should be encrypted so that it is scalable with the number of users having access. Also, since there are multiple owners (patients) in a PHR system and every owner would encrypt her PHR files using a different set of cryptographic keys, it is important to reduce the key distribution complexity in such multi-owner settings. Existing cryptographic enforced access control schemes are mostly designed for the single-owner scenarios.

Correlation Between Upstream Human Activities and Riverine Antibiotic Resistance Genes

Abstract: Antimicrobial resistance remains a serious and growing human health challenge. The water environment may represent a key dissemination pathway of resistance elements to and from humans. However, quantitative relationships between landscape features and antibiotic resistance genes (ARGs) have not previously been identified. The objective of this study was to examine correlations between ARGs and putative upstream anthropogenic sources in the watershed. sul1 (sulfonamide) and tet(W) (tetracycline) were measured using quantitative polymerase chain reaction in bed and suspended sediment within the South Platte River Basin, which originates from a pristine region in the Rocky Mountains and runs through a gradient of human activities. A geospatial database was constructed to delineate surface water pathways from animal feeding operations, wastewater treatment plants, and fish hatchery and rearing units to river monitoring points. General linear regression models were compared. Riverine sul1 correlated with upstream capacities of animal feeding operations (R(2) = 0.35, p < 0.001) and wastewater treatment plants (R(2) = 0.34, p < 0.001). Weighting for the inverse distances from animal feeding operations along transport pathways strengthened the observed correlations (R(2) = 0.60-0.64, p < 0.001), suggesting the importance of these pathways in ARG dissemination. Correlations were upheld across the four sampling events during the year, and averaging sul1 measurements in bed and suspended sediments over all events yielded the strongest correlation (R(2) = 0.92, p < 0.001). Conversely, a significant relationship with landscape features was not evident for tet(W), which, in contrast to sul1, is broadly distributed in the pristine region and also relatively more prevalent in animal feeding operation lagoons. The findings highlight the need to focus attention on quantifying the contribution of water pathways to the antibiotic resistance disease burden in humans and offer insight into potential strategies to control the spread of ARGs.

Regional frameworks applied to hydrology: can landscape-based frameworks capture the hydrologic variability?

Abstract: Regional frameworks have been used extensively in recent years to aid in broad-scale management. Widely used landscape-based regional frameworks, such as hydrologic landscape regions (HLRs) and physiographic provinces, may provide predictive tools of hydrologic variability. However, hydrologic-based regional frameworks, created using only streamflow data, are also available and have been created at various scales; thus, relating frameworks that share a common purpose can be informative. In addition, identifying how the relative importance of variables change in governing streamflow with respect to scale can also be informative. The purpose of this study was to determine whether landscape-based frameworks could explain variation in streamflow classifications and in the hydrologic variables used in their creation. We also evaluated how climate and watershed-based variables govern the divergence of different flow classifications at two different scales. HLRs and physiographic provinces poorly predicted flow class affiliation within our study and for the entire USA, although physiographic provinces explained more variability. We also found that HLRs explained very little variation in individual hydrologic parameters. Using variables summarized at the watershed scale, we found that climate will play a larger role in influencing hydrology across the entire US, whereas soils may govern variation in hydrology at smaller scales. Our results suggest that predictor variables, developed at the watershed scale, may be the most appropriate at explaining hydrology and that the variables used in creating regional landscape-based frameworks may be more useful than the frameworks themselves. In addition, managers should be careful when using landscape-based regional classifications for stream management because the scale of their construction may be too broad to capture differences in flow variability. Copyright © 2011 John Wiley & Sons, Ltd.

Ultraviolet disinfection of antibiotic resistant bacteria and their antibiotic resistance genes in water and wastewater.

Abstract: Disinfection of wastewater treatment plant effluent may be an important barrier for limiting the spread of antibiotic-resistant bacteria (ARBs) and antibiotic resistance genes (ARGs). While ideally disinfection should destroy ARGs, to prevent horizontal gene transfer to downstream bacteria, little is known about the effect of conventional water disinfection technologies on ARGs. This study examined the potential of UV disinfection to damage four ARGs, mec(A), van(A), tet(A), and amp(C), both in extracellular form and present within a host ARBs: methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecium (VRE), Escherichia coli SMS-3-5, and Pseudomonas aeruginosa 01, respectively. An extended amplicon-length quantitative polymerase chain reaction assay was developed to enhance capture of ARG damage events and also to normalize to an equivalent length of target DNA (∼1000 bp) for comparison. It was found that the two Gram-positive ARBs (MRSA and VRE) were more resistant to UV disinfection than the two Gram-negative ARBs (E. coli and P. aeruginosa). The two Gram-positive organisms also possessed smaller total genome sizes, which could also have reduced their susceptibility to UV because of fewer potential pyrimidine dimer targets. An effect of cell type on damage to ARGs was only observed in VRE and P. aeruginosa, the latter potentially because of extracellular polymeric substances. In general, damage of ARGs required much greater UV doses (200-400 mJ/cm² for 3- to 4-log reduction) than ARB inactivation (10-20 mJ/cm² for 4- to 5-log reduction). The proportion of amplifiable ARGs following UV treatment exhibited a strong negative correlation with the number of adjacent thymines (Pearson r 0.85; p < 0.0001). This suggests that formation of thymine dimers is not the sole mechanism of ARB inactivation. Overall, the results indicate that UV is limited in its potential to damage ARGs and other disinfection technologies should be explored.

Making sensor networks immortal: an energy-renewal approach with wireless power transfer

Abstract: Wireless sensor networks are constrained by limited battery energy. Thus, finite network lifetime is widely regarded as a fundamental performance bottleneck. Recent breakthrough in the area of wireless power transfer offers the potential of removing this performance bottleneck, i.e., allowing a sensor network to remain operational forever. In this paper, we investigate the operation of a sensor network under this new enabling energy transfer technology. We consider the scenario of a mobile charging vehicle periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions. We study an optimization problem, with the objective of maximizing the ratio of the wireless charging vehicle (WCV)'s vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and provide a number of important properties. Subsequently, we develop a near-optimal solution by a piecewise linear approximation technique and prove its performance guarantee.

Cycling to work in 90 large American cities: new evidence on the role of bike paths and lanes

Abstract: This article analyzes the variation in bike commuting in large American cities, with a focus on assessing the influence of bike paths and lanes, which have been the main approach to increasing cycling in the USA. To examine the role of cycling facilities, we used a newly assembled dataset on the length of bike lanes and paths in 2008 collected directly from 90 of the 100 largest U.S. cities. Pearson’s correlation, bivariate quartile analysis, and two different types of regressions were used to measure the relationship between cycling levels and bikeways, as well as other explanatory and control variables. Ordinary Least Squares and Binary Logit Proportions regressions confirm that cities with a greater supply of bike paths and lanes have significantly higher bike commute rates—even when controlling for land use, climate, socioeconomic factors, gasoline prices, public transport supply, and cycling safety. Standard tests indicate that the models are a good fit, with R2 ranging between 0.60 and 0.65. Computed coefficients have the expected signs for all variables in the various regression models, but not all are statistically significant. Estimated elasticities indicate that both off-street paths and on-street lanes have a similar positive association with bike commute rates in U.S. cities. Our results are consistent with previous research on the importance of separate cycling facilities and provide additional information about the potentially different role of paths vs. lanes. Our analysis also revealed that cities with safer cycling, lower auto ownership, more students, less sprawl, and higher gasoline prices had more cycling to work. By comparison, annual precipitation, the number of cold and hot days, and public transport supply were not statistically significant predictors of bike commuting in large cities.

Extreme Ultraviolet Variability Experiment (EVE) on the Solar Dynamics Observatory (SDO): Overview of Science Objectives, Instrument Design, Data Products, and Model Developments

Abstract: The highly variable solar extreme ultraviolet (EUV) radiation is the major energy input to the Earth’s upper atmosphere, strongly impacting the geospace environment, affecting satellite operations, communications, and navigation. The Extreme ultraviolet Variability Experiment (EVE) onboard the NASA Solar Dynamics Observatory (SDO) will measure the solar EUV irradiance from 0.1 to 105 nm with unprecedented spectral resolution (0.1 nm), temporal cadence (ten seconds), and accuracy (20%). EVE includes several irradiance instruments: The Multiple EUV Grating Spectrographs (MEGS)-A is a grazing-incidence spectrograph that measures the solar EUV irradiance in the 5 to 37 nm range with 0.1-nm resolution, and the MEGS-B is a normal-incidence, dual-pass spectrograph that measures the solar EUV irradiance in the 35 to 105 nm range with 0.1-nm resolution. To provide MEGS in-flight calibration, the EUV SpectroPhotometer (ESP) measures the solar EUV irradiance in broadbands between 0.1 and 39 nm, and a MEGS-Photometer measures the Sun’s bright hydrogen emission at 121.6 nm. The EVE data products include a near real-time space-weather product (Level 0C), which provides the solar EUV irradiance in specific bands and also spectra in 0.1-nm intervals with a cadence of one minute and with a time delay of less than 15 minutes. The EVE higher-level products are Level 2 with the solar EUV irradiance at higher time cadence (0.25 seconds for photometers and ten seconds for spectrographs) and Level 3 with averages of the solar irradiance over a day and over each one-hour period. The EVE team also plans to advance existing models of solar EUV irradiance and to operationally use the EVE measurements in models of Earth’s ionosphere and thermosphere. Improved understanding of the evolution of solar flares and extending the various models to incorporate solar flare events are high priorities for the EVE team.

Understanding the Impact of Convergent Validity on Research Results

Abstract: Using different measures of constructs in research to develop robust evidence of relationships and effects is seen as good methodological practice. This assumes these measures possess high convergent validity. However, proxies—alternative measures of the same construct—are rarely perfectly convergent. Although some convergence is preferred to none, this study demonstrates that even modest departures from perfect convergent validity can result in substantial differences in the magnitudes of findings, creating challenges for the accumulation and interpretation of research. Using data from published research, the authors find that substantial differences in findings between studies using desired and proxy variables occur even at levels of convergent validity as high as r = .85. Implications of using measures with less-than-ideal convergent validity for the interpretability of research results are examined. Convergent validities above r = .70 are recommended, whereas those below r = .50 should be avoided. Res...

Reactor electron antineutrino disappearance in the Double Chooz experiment

Abstract: The Double Chooz experiment has observed 8,249 candidate electron antineutrino events in 227.93 live days with 33.71 GW-ton-years (reactor power x detector mass x livetime) exposure using a 10.3 cubic meter fiducial volume detector located at 1050 m from the reactor cores of the Chooz nuclear power plant in France. The expectation in case of theta13 = 0 is 8,937 events. The deficit is interpreted as evidence of electron antineutrino disappearance. From a rate plus spectral shape analysis we find sin^2 2{\theta}13 = 0.109 \pm 0.030(stat) \pm 0.025(syst). The data exclude the no-oscillation hypothesis at 99.9% CL (3.1{\sigma}).

Grid Integration of Electric Vehicles and Demand Response With Customer Choice

Abstract: As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration may bring higher peak demand at the distribution level. This may cause potential transformer overloads, feeder congestions, and undue circuit faults. This paper focuses on the impact of charging EVs on a residential distribution circuit. Different EV penetration levels, EV types, and charging profiles are considered. In order to minimize the impact of charging EVs on a distribution circuit, a demand response strategy is proposed in the context of a smart distribution network. In the proposed DR strategy, consumers will have their own choices to determine which load to control and when. Consumer comfort indices are introduced to measure the impact of demand response on consumers' lifestyle. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration.

Predictability of biomass burning in response to climate changes

Abstract: Climate is an important control on biomass burning, but the sensitivity of fire to changes in temperature and moisture balance has not been quantified. We analyze sedimentary charcoal records to show that the changes in fire regime over the past 21,000 yrs are predictable from changes in regional climates. Analyses of paleo- fire data show that fire increases monotonically with changes in temperature and peaks at intermediate moisture levels, and that temperature is quantitatively the most important driver of changes in biomass burning over the past 21,000 yrs. Given that a similar relationship between climate drivers and fire emerges from analyses of the interannual variability in biomass burning shown by remote-sensing observations of month-by-month burnt area between 1996 and 2008, our results signal a serious cause for concern in the face of continuing global warming.

The major volatile organic compound emitted from Arabidopsis thaliana flowers, the sesquiterpene (E)-β-caryophyllene, is a defense against a bacterial pathogen.

Abstract: Flowers have a high risk of pathogen attack because of their rich nutrient and moisture content, and high frequency of insect visitors. We investigated the role of (E)-β-caryophyllene in floral defense against a microbial pathogen. This sesquiterpene is a common volatile compound emitted from flowers, and is a major volatile released from the stigma of Arabidopsis thaliana flowers. Arabidopsis thaliana lines lacking a functional (E)-β-caryophyllene synthase or constitutively overexpressing this gene were challenged with Pseudomonas syringae pv. tomato DC3000, which is a bacterial pathogen of brassicaceous plants. Flowers of plant lines lacking (E)-β-caryophyllene emission showed greater bacterial growth on their stigmas than did wild-type flowers, and their seeds were lighter and misshapen. By contrast, plant lines with ectopic (E)-β-caryophyllene emission from vegetative parts were more resistant than wild-type plants to pathogen infection of leaves, and showed reduced cell damage and higher seed production. Based on in vitro experiments, (E)-β-caryophyllene seems to act by direct inhibition of bacterial growth, rather than by triggering defense signaling pathways. (E)-β-Caryophyllene thus appears to serve as a defense against pathogens that invade floral tissues and, like other floral volatiles, may play multiple roles in defense and pollinator attraction.