Media consumption effects on college students\u27 perceived risk of victimization and fear of crime: Does the usage of social media police scanners alter public perceptions of safety?

Although there is a robust body of research examining various predictors of fear of crime, there are still predictors of one’s perceived safety that have not been thoroughly assessed. Using primary data collected from a sample of college students (N = 662) enrolled at five universities in the United States the main objective of the study was to identify the factors more likely to predict variations in fear of crime, which is viewed here as a bidimensional concept that includes the affective side of fear (worries about becoming a victim) and the cognitive dimension of fear (i.e., personal judgment of safety). The study is informed by theoretical explanations of fear of crime (Ferraro, 1995; see Hale, 1996) and by Gerbner’s (1969) cultivation theory. In addition to frequently used predictors of fear of crime, such as social vulnerability, crime victimization experience, and public attitudes toward the police, this dissertation explores the impact of traditional and social media consumption on one’s perceived risk of victimization and fear of crime. The study also assessed the effect of membership in social media police scanner groups on variations in perceived safety. Those who are members of a social media police scanner groups can view and read about various types of violent and property crimes before the public is informed. Yet, to the author’s knowledge, the potential effect of police scanner membership/subscription on fear of crime has not been examined in the literature and research assessing the impact of social media on perceived risk of victimization is limited. By examining the effect of novel predictors of fear of crime (e.g., social media consumption; police scanner usage), the dissertation expanded fear of crime research. The data were analyzed using a simple mediation analysis that used perceived risk of victimization as a mediator. Results show that those who worry more about becoming victims of violent crime also tend to feel unsafe in their neighborhoods. As hypothesized, victims of crime, females, younger respondents, and students belonging to racial/ethnic minority groups tend to worry significantly more about becoming victims of violent crime and report higher levels of perceived unsafety. Conversely, those with positive perceptions of the police are less likely to fear victimization. While traditional media consumption does not appear to influence variations in perceived risk of victimization and/or perceived safety, social media consumption as well as membership in social media police scanner groups indirectly increase one’s fear of crime. The study limitations and the implications of the findings are also discussed

A Permutation Test and Spatial Cross-Validation Approach to Assess Models of Interspecific Competition Between Trees

Measuring species-specific competitive interactions is key to understanding plant communities. Repeat censused large forest dynamics plots offer an ideal setting to measure these interactions by estimating the species-specific competitive effect on neighboring tree growth. Estimating these interaction values can be difficult, however, because the number of them grows with the square of the number of species. Furthermore, confidence in the estimates can be overestimated if any spatial structure of model errors is not considered. Here we measured these interactions in a forest dynamics plot in a transitional oak-hickory forest. We analytically fit Bayesian linear regression models of annual tree radial growth as a function of that tree’s species, its size, and its neighboring trees. We then compared these models to test whether the identity of a tree’s neighbors matters and if so at what level: based on trait grouping, based on phylogenetic family, or based on species. We used a spatial crossvalidation scheme to better estimate model errors while avoiding potentially over-fitting our models. Since our model is analytically solvable we can rapidly evaluate it, which allows our proposed cross-validation scheme to be computationally feasible. We found that the identity of the focal and competitor trees mattered for competitive interactions, but surprisingly, identity mattered at the family rather than species-level

Simple, Inexpensive Technique for High-Quality Smartphone Fundus Photography in Human and Animal Eyes

Purpose. We describe in detail a relatively simple technique of fundus photography in human and rabbit eyes using a smartphone, an inexpensive app for the smartphone, and instruments that are readily available in an ophthalmic practice. Methods:. Fundus images were captured with a smartphone and a 20D lens with or without a Koeppe lens. By using the coaxial light source of the phone, this system works as an indirect ophthalmoscope that creates a digital image of the fundus. The application whose software allows for independent control of focus, exposure, and light intensity during video filming was used. With this app, we recorded high-definition videos of the fundus and subsequently extracted high-quality, still images from the video clip. Results:. The described technique of smartphone fundus photography was able to capture excellent high-quality fundus images in both children under anesthesia and in awake adults. Excellent images were acquired with the 20D lens alone in the clinic, and the addition of the Koeppe lens in the operating room resulted in the best quality images. Successful photodocumentation of rabbit fundus was achieved in control and experimental eyes. Conclusion:. The currently described system was able to take consistently high-quality fundus photographs in patients and in animals using readily available instruments that are portable with simple power sources. It is relatively simple to master, is relatively inexpensive, and can take advantage of the expanding mobile-telephone networks for telemedicine

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table

The Forestecology R Package for Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package\u27s functionality using data from the Smithsonian Conservation Biology Institute\u27s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results. The package features (a) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind

Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

Center-surround vs. distance-independent lateral connectivity in the olfactory bulb

Lateral neuronal interactions are known to play important roles in sensory information processing. A center-on surround-off local circuit arrangement has been shown to play a role in mediating contrast enhancement in the visual, auditory, and somatosensory systems. The lateral connectivity and the influence of those connections have been less clear for the olfactory system. A critical question is whether the synaptic connections between the primary projection neurons, mitral and tufted (M/T) cells, and their main inhibitory interneurons, the granule cells (GCs), can support a center-surround motif. Here, we study this question by injecting a “center” in the glomerular layer of the olfactory bulb (OB) with a marker of synaptic connectivity, the pseudorabies virus (PRV), then examines the distribution of labeling in the “surround” of GCs. We use a novel method to score the degree to which the data fits a center-surround model vs. distance-independent connectivity. Data from 22 injections show that M/T cells generally form lateral connections with GCs in patterns that lie between the two extremes

Triton binding energy calculated from the SU_6 quark-model nucleon-nucleon interaction

Properties of the three-nucleon bound state are examined in the Faddeev formalism, in which the quark-model nucleon-nucleon interaction is explicitly incorporated to calculate the off-shell T-matrix. The most recent version, fss2, of the Kyoto-Niigata quark-model potential yields the ground-state energy ^3H=-8.514 MeV in the 34 channel calculation, when the np interaction is used for the nucleon-nucleon interaction. The charge root mean square radii of the ^3H and ^3He are 1.72 fm and 1.90 fm, respectively, including the finite size correction of the nucleons. These values are the closest to the experiments among many results obtained by detailed Faddeev calculations employing modern realistic nucleon-nucleon interaction models.Comment: 10 pages, no figure