Community Detection Detailed for Online Social Networks

Ever since the internet became publicly available it has allowed users to interact with each other across virtual networks. With this large amounts of data being collected the clustering of this information has become an even more powerful tool for recognize patterns and trends in a network. In this research we look build a model for Community Detection in these online social networks. We combine the ideas from both discrete mathematics and sociology, to build an algorithm with the specific intent on discovering communities that exist in an online social network. We present many of the sociology theories behind the patterns and clusters from in data and how our to identify overlapping and hierarchical communities. We investigate the properties of the objective function Modularity present by M. Girvan and M.E.J. Newman while use information from both the structures of the network and that of a spectral partitioning algorithm to return covers of a network

Environmental Factors That Predict Adolescent Smoking Behavior: The Influence of Parent, Peer, and Sibling Smoking

The majority of adult smoking begins during adolescence, and in order for prevention programs to be optimally effective it is critical to understand the influences of smoking initiation during this developmental period. However, little research has focused on how environmental factors, such as parent and peer smoking. influence smoking initiation exclusively within a rural population. The current study surveyed students from 23 middle schools in rural Virginia and New York State at the end of the sixth grade and then one-year later at the end of the seventh grade. Logistic regressions were used to predict changes in levels of adolescent smoking from factors such as parent smoking, peer smoking, sibling smoking, self-efficacy to refuse cigarettes, and whether the adolescent resided in a tobacco-growing area. Results from this study indicated that having a best friend who smokes was more important for trying smoking, whereas the number of friends who smoke was more important for experimental and higher levels of smoking. Two variables, having a mother who smokes and an adolescent’s self-efficacy to refuse cigarettes, were found to be a consistent influence across all stages of smoking behavior. Ethnicity had a slightly different impact on smoking behavior than demonstrated in previous research. African Americans were actually at a higher risk for trying smoking than Caucasians, and there were no differences for ethnicity among those who moved to experimental or higher levels of smoking. In addition, living in a tobacco-growing county was significantly related to adolescents trying smoking, but was not related to adolescents at this age moving to experimental or higher levels of smoking. The findings from this study suggest that there are unique aspects to the smoking behavior of rural adolescents, and suggestions for prevention are made

The Impact of a Peer-Led Program on the Peer Leaders\u27 Leadership-Related Skills

Leadership and goal setting skills were examined for 49 high school students who implemented a peer-led health and life skills program for sixth grade students. Participants completed surveys that included a leadership scale and a goal setting scale constructed for this study. and an adapted version of the Goals Inventory. Surveys were administered to the participants prior to a 3-day training (Time Point I), immediately following the training (Time Point 2). and at the completion of leading 12. 1-hour workshops (Time Point 3). The results indicate that high school peer leaders perceived an increase in both their leadership and goal setting skills. Results from retrospective pretest measures of the leadership and goal setting scales also indicate that the participants had overestimated those skills prior to their peer-leadership experience. The findings suggest that improved leadership and goal setting skills are ways that adolescents can benefit from a peer leadership experience

Stress Evaluation of Welded Steel Bridges on Coal-Haul Routes

This report describes the procedure developed and being employed to determine and assess live-load stresses in structural members of welded steel bridges on extended· weight coal haul routes. Those bridges are routinely subjected to loads from coal trucks in excess of those permitted on other routes. Those elevated loads may result in high stresses in bridge members. Of principal concern are certain weld details on steel bridges that are susceptible to fatigue cracking when subject to high live-load stresses. Seventeen welded steel bridges on extended-weight coal haul routes have been identified for investigation under this study. The study test procedure consists of 1) a review of coal-haul data and plans to identify lanes of a bridge subject to greatest coal-truck loading, 2) identification of weld details of interest for analysis on portions of the bridge superstructure subject to high live-load stresses, 3) field application of strain gages to measure live-load stresses at locations of interest on a bridge, 4) continuous monitoring of live stresses from routine traffic for an extended period and 5) data retrieval and reduction and fatigue analysis. Fatigue analysis is based on the number of stress cycles measured during the field test and the equivalent resolved live-load stress. That is compared to the 1992 AASHTO fatigue performance data for applicable structural details (e.g. welded connections). An exemplary use of the study test procedure is given for the KY 15 bridge over the North Fork of the Kentucky River and KY SO in Perry Co. This report describes the test locations, test procedures and results of the derived test data. The field tests will indicate the level of live-load stresses to which the bridges are exposed. Additionally, the fatigue analyses may indicate whether welded steel bridges on extended-weight coal haul routes are susceptible to fatigue damage

Summary of Stress Evaluations of Welded Steel Bridges on Coal-Haul Routes

Stress analyses were performed on continuous girder welded steel bridges on extended weights coal-haul routes. The tests were intended to determine whether extended weight coal trucks pose fatigue problems to those bridges. Measurements were performed by strain gaging selected bridges subject to high coal transport tonnages. Stress measurements were conducted on fatigue-prone weld details or test sites where high tensile stresses were anticipated. Test sites on the bridges were instrumented with strain gages. Strains induced by routine traffic including coal trucks were monitored for periods of one to two weeks. Unattended monitoring of the variable amplitude strain data was performed using rainflow counting. Eighteen successful tests were performed on 15 coal-haul route bridges and one interstate bridge. The derived strain data are provided as stress histograms. Fatigue analyses were performed by expressing the stress histogram data as single-value equivalent stresses. The accumulated number of stress cycles was estimated using 3 different assumptions based upon variations in traffic. Accumulated stress cycles were determined over the current age of each weld detail and a projected service life of 75 years. Susceptibility to fatigue was determined by superimposing the equivalent resolved stresses and total number of cycles as accumulated damage on AASHTO fatigue design curves for the applicable structural details. The fatigue analyses indicate that none of the test bridges with fatigue-prone weld details is susceptible to fatigue cracking either at their current age or over their project 75-year service lives. While coal trucks may induce high live stresses on those bridges, the number of those stress applications was not sufficient to pose fatigue problems. The equivalent resolved stresses measured on the interstate bridge were similar in magnitude to those measured on coal-haul routes. However, the number of stress cycles was greater for the interstate bridge than most of the coal-haul route bridges

Visualization and Characterization of Agricultural Sprays Using Machine Learning based Digital Inline Holography

Accurate characterization of agricultural sprays is crucial to predict in field performance of liquid applied crop protection products. Here we introduce a robust and efficient machine learning (ML) based Digital In-line Holography (DIH) to accurately characterize the droplet field for a wide range of agricultural spray nozzles. Compared to non-ML methods, our method enhances accuracy, generalizability, and processing speed. Our approach employs two neural networks: a modified U-Net to obtain the 3D droplet field from the numerically reconstructed optical field, followed by a VGG16 classifier to reduce false positives from the U-Net prediction. The modified U-Net is trained using holograms generated using a single spray nozzle at three spray locations; center, half-span, and the spray edge to create training data with various number densities and droplet size ranges. VGG16 is trained via the minimum intensity projection of the droplet 3D point spread function. Data augmentation is used to increase the efficiency of classification and make the algorithm generalizable for different measurement settings. The model is validated via NIST traceable glass beads and six agricultural spray nozzles representing various spray characteristics. The results demonstrate a high accuracy rate, with over 90% droplet extraction and less than 5% false positives. Compared to traditional spray measurement techniques, our method offers a significant leap forward in spatial resolution and generalizability. In particular, our method can extract the real cumulative volume distribution of the NIST beads, where the laser diffraction is biased towards droplets moving at slower speeds. Additionally, the ML-based DIH enables the estimation of mass and momentum flux at different locations and the calculation of relative velocities of droplet pairs, which are difficult to obtain via conventional techniques.Comment: 24 pages, 12 figure

Balancing near-field enhancement, absorption, and scattering for effective antenna-reactor plasmonic photocatalysis

Efficient photocatalysis requires multifunctional materials that absorb photons and generate energetic charge carriers at catalytic active sites to facilitate a desired chemical reaction. Antenna–reactor complexes are an emerging multifunctional photocatalytic structure where the strong, localized near field of the plasmonic metal nanoparticle (e.g., Ag) is coupled to the catalytic properties of the nonplasmonic metal nanoparticle (e.g., Pt) to enable chemical transformations. With an eye toward sustainable solar driven photocatalysis, we investigate how the structure of antenna–reactor complexes governs their photocatalytic activity in the light-limited regime, where all photons need to be effectively utilized. By synthesizing core@shell/satellite (Ag@SiO_2/Pt) antenna–reactor complexes with varying Ag nanoparticle diameters and performing photocatalytic CO oxidation, we observed plasmon-enhanced photocatalysis only for antenna–reactor complexes with antenna components of intermediate sizes (25 and 50 nm). Optimal photocatalytic performance was shown to be determined by a balance between maximized local field enhancements at the catalytically active Pt surface, minimized collective scattering of photons out of the catalyst bed by the complexes, and minimal light absorption in the Ag nanoparticle antenna. These results elucidate the critical aspects of local field enhancement, light scattering, and absorption in plasmonic photocatalyst design, especially under light-limited illumination conditions

Leveraging Implementation Science to Understand Factors Influencing Sustained Use of Mental Health Apps: a Narrative Review

Mental health (MH) smartphone applications (apps), which can aid in self-management of conditions such as depression and anxiety, have demonstrated dramatic growth over the past decade. However, their effectiveness and potential for sustained use remain uncertain. This narrative review leverages implementation science theory to explore factors influencing MH app uptake. The review is guided by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework and discusses the role of the innovation, its recipients, context, and facilitation in influencing successful implementation of MH apps. The review highlights critical literature published between 2015 and 2020 with a focus on depression and anxiety apps. Sources were identified via PubMed, Google Scholar, and Twitter using a range of keywords pertaining to MH apps. Findings suggest that for apps to be successful, they must be advantageous over alternative tools, relatively easy to navigate, and aligned with users\u27 needs, skills, and resources. Significantly more attention must be paid to the complex contexts in which MH app implementation is occurring in order to refine facilitation strategies. The evidence base is still uncertain regarding the effectiveness and usability of MH apps, and much can be learned from the apps we use daily; namely, simpler is better and plans to integrate full behavioral treatments into smartphone form may be misguided. Non-traditional funding mechanisms that are nimble, responsive, and encouraging of industry partnerships will be necessary to move the course of MH app development in the right direction

The interaction of land-use legacies and hurricane disturbance in subtropical wet forest: twenty-one years of change

Disturbance shapes plant communities over a wide variety of spatial and temporal scales. How natural and anthropogenic disturbance interact to shape ecological communities is highly variable and begs a greater understanding. We used five censuses spanning the years 1990–2011 from the 16-ha Luquillo Forest Dynamics Plot (LFDP) in northeast Puerto Rico to investigate the interplay of human land-use legacies dating to the early 20th century and two recent hurricanes (Hugo, 1989 and Georges, 1998). The LFDP is a landscape mosaic comprised of an area of mature subtropical wet forest and three areas of secondary forest with differing past land-use intensities. We examined the degree to which hurricane disturbance–effect and subsequent community recovery varied across past land-use classes. We expected areas with greater intensity of human land use to be more affected by hurricane disturbance therefore exhibiting greater initial damage and longer successional recovery times. Structurally, areas of secondary forest contained smaller trees than old-growth areas; hurricanes caused widespread recruitment of shrubs and saplings that thinned with time since the first hurricane. Species richness of the plot declined over time, mostly due to the loss of rare species, but also due to the loss of some heliophilic, pioneer species that became abundant after the first hurricane. Species composition differed strongly between areas of secondary and mature forest, and these differences were largely constant over time, except for an increase in compositional differences following the second hurricane. An indicator species analysis attributed this pattern to the longer persistence of pioneer species in areas of greater past land-use intensity, likely due to the more open canopy in secondary forest. When secondary forest areas of differing past land-use intensity were considered separately, few species of low community rank were found as indicators. When these areas were combined, more and higher-ranked species emerged as indicators, creating ecologically meaningful indicator species combinations that better captured the broad-scale plant community response to past land use. Our findings support the idea that effects of past land use can persist for decades to centuries following land-use abandonment, illustrating the importance of land-use legacies in shaping regenerating tropical secondary forests

A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ϵ can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations