Housing Market Conditions and Neighborhood Concentrated Disadvantage: Impacts on Crime Victimization in Knoxville, Tennessee

Neighborhood concentrated disadvantage is a composite social factor that quantifies the quality of neighborhoods in urban areas. Criminal activity and victimization are more prevalent in disadvantaged neighborhoods. However, whether housing market factors (e.g., eviction, foreclosure, and subprime lending) represent an unrecognized dimension of neighborhood concentrated disadvantage remains unknown. I contribute to the neighborhood disadvantage literature by assessing whether three housing market factors (eviction, foreclosure, and subprime lending) are a neglected part of neighborhood concentrated disadvantaged that explains criminal activity and victimization. Furthermore, I investigate whether housing market factors mediate the relationship between concentrated disadvantage and crime. Last, using spatial analysis techniques, I examine the spatial patterns of neighborhood concentrated disadvantage and crime in terms of three housing market factors in the city of Knoxville, Tennessee. Data are collected from different agencies: the Knoxville Police Department, the Census Bureau’s American Community Survey, Knox County Civil Sessions Court, the Knox County Register of Deeds, and federal filings as part of the FFIEC Home Mortgage Disclosure Act. My results indicate that eviction, foreclosure, and subprime loan have a complex relationship to neighborhood concentrated disadvantage as it predicts crime. Moreover, although housing market factors are not mediating the relationship between concentrated disadvantage and crime, concentrated disadvantage mediates the relationship between eviction and crime. I find there are spatial differences in crime rates across 86 census tracts in Knoxville. Crime rates in Knoxville are spatially interdependent, suggesting that for crime increase in a census tract, it leads to crimes occurring in neighboring census tracts. Eviction and foreclosure are spatially clustered, while subprime loan shows a spatial dissimilar pattern across the city. High eviction and foreclosure census tracts are surrounded by high crime census tracts, but low subprime loan census tracts are surrounded by high crime census tracts. These neighborhoods are mainly in the downtown Knoxville and its outer areas

Deep Learning-Based Object Detection of Barley Seeds

Computer vision techniques have been widely used in the food manufacturing industry today due to their speed, convenience, and low labor cost. As an important raw material in beer, barley seeds largely determine the flavor and taste of the beer brewing process. To ensure the quality of beer, beer brewers will strictly screen the varieties of barley seeds and ensure the purity of malt. Traditional manual detection needs a lot of professional training, and because of the high similarity between different kinds of barley seeds, tedious and time-consuming manual detection leads to a high error rate. However, chemical testing requires professional equipment, reagents, and laboratories, which have high-cost performance. Thus, an efficient and accurate object detection technique is considered to replace manually distinguishing barley seed types. This thesis uses the deep learning-based object detection network YOLOv3 model to help automatically and accurately detecting barley locations and identifying seed types from iPhone-based images of barley seeds. The barley seed samples used in this project are all provided by our industrial collaborator, and captured by iPhone 11 or iPhone 11 pro in high-definition resolutions. A total of nine varieties of barleys are trained in this study, and the data set includes images of a single grain and images of multiple grains (multi-categories). In this experiment, the best mAP (mean Average Precision) value we obtained is 97.1%, the model recognition (localization and classification) precision is 91.2%, and the recall rate is 95.9%

Phenanthroline-Catalyzed 1,2-Cis Glycosylation: Scope And Mechanism

Phenanthroline, a rigid and planar organic compound with two fused pyridine rings, has been used as a powerful ligand for metals and a binding agent for DNA/RNA. We recently discovered that phenanthroline could be used as a nucleophilic catalyst to access high yielding and diastereoselective α-1,2-cis glycosides through the coupling of hydroxyl acceptors with α-glycosyl bromide donors. The utility of the phenanthroline catalysis is expanded to sterically hindered hydroxyl nucleophiles and chemoselective coupling of an alkyl hydroxyl group in the presence of a free C1-hemiacetal functionality. In addition, the phenanthroline-based catalyst has a pronounced effect on site-selective couplings of triol motifs and orthogonally activates the anomeric bromide leaving group over the anomeric fluoride and sulfide counterparts.An extensive mechanistic investigation showed two glycosyl phenanthrolinium ion intermediates, a 4C1 chair-liked β-conformer and a B2,5 boat-like α-conformer, in a ratio of 2:1 (β:α). Further, NMR studies show that a hydrogen bonding is formed between the second nitrogen atom of phenanthroline and the C1-anomeric hydrogen of sugar moiety to stabilize the phenanthrolinium ion intermediates. To obtain high levels of α-1,2-cis stereoselectivity, a Curtin-Hammett scenario was proposed wherein interconversion of the 4C1 β-conformer and B2,5 α-conformer is more rapid than nucleophilic addition. Hydroxyl attack takes place from the α-face of the more reactive 4C1 chair-like β-phenanthrolinium intermediate to give an α-anomeric product. The phenanthroline catalysis system is applicable to a number of furanosyl bromide donors to provide the challenging 1,2-cis substitution products in good yield with high anomeric selectivity. While arabinofuranosyl bromide provides β-1,2-cis products, xylo- and ribofuranosyl bromides favor α-1,2-cis products. NMR experiments and density-functional theory calculations support an associative mechanism in which the rate-determining step occurs from an invertive displacement of the faster reacting phenanthrolinium ion intermediate with alcohol nucleophile

Sociodemographic Homophily Within Friendship and Sequential Peer Victimization: A Longitudinal Dyadic Perspective

Bullying is embedded within peer social networks that involve more than just bullies and victims. Extant research mostly supports that victims’ friends–as the individuals closest to the victims in the peer network–can protect victims by reducing risk factors and promoting adjustment at the individual level (Bukowski et al., 2018). However, the effect of similarity between victims and their friends on peer victimization remains understudied. Homophily refers to the tendency that people to befriend similar others (Lazarsfeld & Merton,1964). The current thesis investigated how homophily–magnitude (i.e., similarity level) and direction (i.e., which party of the dyad has a high score in specific characteristics) in Emotionality, social status, and peer victimization experience–between youth and their mutual friends can impact the frequency of peer victimization, concurrently and over time. The data were extracted from a two-wave longitudinal study. The analytic sample included 207 Grade 5-9 participants (female 62.8%, Mage = 11.88, SD = 1.18), creating 424 friendship dyads. Regression analyses suggested that a higher level of similarity in peer victimization at Wave 1 and in social status at Wave 2 predicted the targeted youth’s lower frequency of peer victimization at Wave 2. Regarding homophily direction, befriending peers with lower Emotionality than oneself and with more peer victimization experience than oneself at Wave 1 predicted an increase in youth’s peer victimization at Wave 2. From a dyadic perspective, the current thesis supports the effect of friendship selection based on dyadic similarity and addresses the significant role of sociodemographic homophily within friendships. It also provides a more complete picture of how bullying operates in peer groups than the current bullying research has

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin