Showing papers by "Northeastern State University published in 2017"

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Abstract: The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Nattokinase: An Oral Antithrombotic Agent for the Prevention of Cardiovascular Disease

Abstract: Natto, a fermented soybean product, has been consumed as a traditional food in Japan for thousands of years. Nattokinase (NK), a potent blood-clot dissolving protein used for the treatment of cardiovascular diseases, is produced by the bacterium Bacillus subtilis during the fermentation of soybeans to produce Natto. NK has been extensively studied in Japan, Korea, and China. Recently, the fibrinolytic (anti-clotting) capacity of NK has been recognized by Western medicine. The National Science Foundation in the United States has investigated and evaluated the safety of NK. NK is currently undergoing a clinical trial study (Phase II) in the USA for atherothrombotic prevention. Multiple NK genes have been cloned, characterized, and produced in various expression system studies. Recombinant technology represents a promising approach for the production of NK with high purity for its use in antithrombotic applications. This review covers the history, benefit, safety, and production of NK. Opportunities for utilizing plant systems for the large-scale production of NK, or for the production of edible plants that can be used to provide oral delivery of NK without extraction and purification are also discussed.

Top-Priority Directions of Implementing New Internet Technologies on the Territories of Rapid Economic Development

Abstract: The purpose of the work is to determine the top-priority directions of implementing the new Internet technologies on the territories of rapid economic development by the example of modern Russia. In order to determine the effectiveness of application of financial tools for stimulating the growth of territories of rapid economic development, the authors use the classical method of evaluation of effectiveness, which supposes finding the ratio of results to expenditures. The indicators of the result include the volume of manufactured product in the territories of rapid economic development and the volume of taxes paid into state budgets of these territories, and the indicator of expenditures is the volume of investments into territories of rapid economic development. The authors come to the conclusion that financial tools (investments) of stimulating the growth of territories of rapid economic development are peculiar for low effectiveness, as they lead to high load on the federal budget. Non-financial tools – including Internet technologies – have wide perspectives and high effectiveness in the sphere of stimulating the acceleration of growth rate of territories of rapid development. The authors determine top-priority directions of implementation of new Internet technologies on the territories of rapid development and develop the mechanism of activation of growth of the territories of rapid economic development on the basis of implementing the new Internet technologies by the example of modern Russia.

Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks.

Abstract: In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs' movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.

Binge Drinking, Greek-Life Membership, and First-Year Undergraduates: The “Perfect Storm” for Drugging Victimization

Abstract: Drugging victimization is an understudied phenomenon. This study examines the relationship between the campus party culture and drugging victimization. Campus party culture has been shown to influence risk of other types of victimization, and there is reason to believe it may also influence drugging victimization. Using three behavioral indicators of participation in the campus party culture—binge drinking, Greek life membership, and first-year student status—this study estimates the main effect of each indicator, and their interaction, on undergraduates’ rates of drugging victimization. Estimating a generalized linear model using a large random sample (N = 6,931) from three universities, results show that each party culture variable and their three-way interaction significantly influenced students’ rate of drugging victimization. Sorority members who were first-year students and binge drank at least once in the past month had significantly higher rates of drugging victimization compared to all ot...

Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks

Abstract: Unlike conventional scalar sensors, camera sensors at different positions can capture a variety of views of an object. Based on this intrinsic property, a novel model called full-view coverage was proposed. We study the problem that how to select the minimum number of sensors to guarantee the full-view coverage for the given region of interest (ROI). To tackle this issue, we derive the constraint condition of the sensor positions for full-view neighborhood coverage with the minimum number of nodes around the point. Next, we prove that the full-view area coverage can be approximately guaranteed, as long as the regular hexagons decided by the virtual grid are seamlessly stitched. Then we present two solutions for camera sensor networks in two different deployment strategies. By computing the theoretically optimal length of the virtual grids, we put forward the deployment pattern algorithm (DPA) in the deterministic implementation. To reduce the redundancy in random deployment, we come up with a local neighboring-optimal selection algorithm (LNSA) for achieving the full-view coverage. Finally, extensive simulation results show the feasibility of our proposed solutions.

A Game-Based Localized Multi-Objective Topology Control Scheme in Heterogeneous Wireless Networks

Abstract: In wireless networks, network topology may change at any time. Therefore, topology control is one of the effective methods to get and keep the desired topology performance. The most existing topology control methods assume that nodes are altruistic. Although there are some game-based topology control schemes to stimulate cooperation between nodes, they only consider a single objective (e.g., energy consumption or network lifetime), which cannot be adaptive to the variation of demand on topology performance. To address these weaknesses, we present the notion of link lifetime and model the multi-objective weight sum of any link as the function with respect to transmission power, link delay and link lifetime. Then the proposed game-based localized multi-objective topology control ensures that the desired topology property exists in resulting topology, in which the presented Improved LOCAL $\delta $ -Improvement Algorithm (LDIA) algorithm not only stimulates nodes’ cooperation on topology control operation and ensures network’s convergence to a steady state, but also has the better performance with respect to executing time and communication overhead than a classic algorithm, i.e., LDIA. Finally, the simulation results show that, by employing appropriate weight values, when compared with some typical schemes considering only energy efficiency, the proposed scheme is the most efficient in regard to average link delay and link lifetime. When compared with a typical scheme considering only network lifetime, the proposed scheme has advantage over average link lifetime, but it is slightly worse in terms of average link delay. Although the proposed scheme is less efficient in terms of average transmission power, where the shortage may be alleviated by adjusting weight values, it satisfies diversified demands for applications due to its flexibility.

"A Psychopath by Any Other Name?": Juror Perceptions of the DSM-5 "Limited Prosocial Emotions" Specifier.

Abstract: DSM-5 recently added the specifier “Limited Prosocial Emotions” (LPE) to the Conduct Disorder (CD) diagnosis, yet little is known about how these traits will affect attitudes toward CD youth. Laypersons attending jury duty (N = 326) were randomly assigned to one of four case vignette conditions in which a male juvenile offender was identified as having (a) CD symptoms only, (b) CD symptoms plus a diagnostic label, (c) CD symptoms plus a diagnostic label and description of LPE traits, or (d) CD symptoms plus a description of LPE traits and a “psychopath” label. LPE traits led to more negative perceptions of the youth (e.g., more dangerous, evil, and psychopathic) and adding the psychopath label to the LPE specifier resulted in somewhat stronger support for punishment and mandated treatment. The LPE specifier may provide useful diagnostic information, but these findings raise serious concerns that it will stigmatize youth in the legal system.

Mandatory Reporting of Sexual Misconduct at College: A Critical Perspective.

Abstract: Many universities and colleges now require all “responsible employees,” including faculty, to report known or suspected sexual misconduct to designated Title IX administrators. The intention of these mandatory reporting policies is to ensure institutional accountability and compliance with Title IX’s prohibition against sexual and gender-based discrimination. Yet, critics argue that such policies are overreaching, paternalistic and, ironically, discriminatory. Drawing from prior research on sexual victimization and original exploratory data on gender-based college harassment, this article provides a critical perspective that delineates both the intended goals and unintended consequences of Title IX’s mandatory reporting policies, specifically focusing on three overlapping issues: ambiguous definitions, reporting risks, and faculty’s role in disclosure. We conclude by proposing alternative strategies for achieving Title IX’s objectives.

Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Abstract: Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs .

A privacy preserving authentication scheme for roaming in ubiquitous networks

Abstract: Ubiquitous networks enable mobile users to communicate with each other efficiently and independently without the need of inventing agent. This approach is proved to be delay and spectral efficient. Due to the nature of underlying Big data, such networks are prone to several security and privacy challenges. Because such gigantic data is not only difficult to store, maintain and manipulate but Big data’s open architecture makes the security threats inevitable. Therefore, incorporating authentication between mobile node and foreign network during roaming in ubiquitous networks has become a tedious task. Very recently, Farash et al. found a number of weaknesses in the schemes proposed by Wen et al. and Shin et al. Furthermore, Farash et al. proposed an enhanced scheme for roaming user in ubiquitous network. However, after thorough investigation, we show fragilities of Farash et al.’s scheme against (1) mobile user anonymity violation; (2) disclosure of secret parameter of mobile node; (3) session key disclosure; and (4) mobile node impersonation attacks. Therefore, we propose an improved scheme to fix these fragilities. We analyze the security of proposed scheme using popular automated verification tool ProVerif. The analysis confirms that the proposed scheme resists the known attacks while having quite low overhead as compared with Farash et al.’s scheme. Therefore, in order to get better performance proposed scheme is a suitable candidate to be employed along with supercomputing systems for dealing the security challenges of big data in ubiquitous networks.

Non-convex sparse and low-rank based robust subspace segmentation for data mining

Abstract: Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l₁-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating lp-norm and Schatten p-norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms.

Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks.

Abstract: Measurement of time series complexity and predictability is sometimes the cornerstone for proposing solutions to topology and congestion control problems in sensor networks. As a method of measuring time series complexity and predictability, multiscale entropy (MSE) has been widely applied in many fields. However, sample entropy, which is the fundamental component of MSE, measures the similarity of two subsequences of a time series with either zero or one, but without in-between values, which causes sudden changes of entropy values even if the time series embraces small changes. This problem becomes especially severe when the length of time series is getting short. For solving such the problem, we propose flexible multiscale entropy (FMSE), which introduces a novel similarity function measuring the similarity of two subsequences with full-range values from zero to one, and thus increases the reliability and stability of measuring time series complexity. The proposed method is evaluated on both synthetic and real time series, including white noise, 1/f noise and real vibration signals. The evaluation results demonstrate that FMSE has a significant improvement in reliability and stability of measuring complexity of time series, especially when the length of time series is short, compared to MSE and composite multiscale entropy (CMSE). The proposed method FMSE is capable of improving the performance of time series analysis based topology and traffic congestion control techniques.

Adapting Identities: Religious Conversion and Partisanship Among Asian American Immigrants:

Abstract: Asian Americans constitute the largest group of new immigrants and the fastest growing ethnic group in the United States. While Asian American immigrants have experienced greater economic success than other minority groups, this has not necessarily led to greater political incorporation such as identification with a political party. Political parties have made little substantive outreach to Asian Americans, leaving a void in political socialization that other institutions, such as churches, have sought to fill. Yet the U.S. religious landscape is often quite different from that of Asian immigrants’ sending countries, providing opportunities for changes in religious identity through conversion. Leveraging data from the 2012 Pew Asian American Survey, we show that conversion from Buddhism to Christianity among Asian American immigrants facilitates the development of partisan political identities. We demonstrate that conversion functions as an adaptation in identity that helps facilitate subsequent changes i...

Cortisol and Alpha-amylase changes during an Ultra-Running Event.

Abstract: Elevated stress hormone concentrations can positively affect an athlete's overall performance during a competition, and in many cases, are necessary to be able to perform exercise. During extreme exercise, the body's ability to utilize energy efficiently can affect an athlete's performance. Elevated hormonal concentrations can have many benefits in regards to an athlete's overall performance during a competition. The purpose of this study was to examine the effects of long distance running, such as seen during an ultra-running event (distances beyond 26.2 miles), on the activity of the hypothalamic-pituitary-adrenocortical (HPA) axis production of cortisol (CORT) as compared to autonomic nervous system production of salivary alpha-amylase (AA). Despite the well-known effects of exercise on CORT and AA response, it is unclear what effect running beyond the marathon distance has on these levels. This study investigates what effect long duration cardio exercise, such as running up to 100K (kilometers) distance, has on the neuroendocrine system, by means of saliva samples provided by participants signed up for an ultra-marathon event. The findings of this study show that the autonomic nervous system may present a response signal during physical stress that is independent of the HPA axis response. At distances beyond the marathon length, the production of CORT and AA was found to be suppressed for athletes, which could help them in their continued performance. Furthermore, this study recognizes a difference in the overall male and female response to stress in regards to CORT and AA production.

Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints.

Abstract: Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks.

Abstract: The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider's server contains a lot of valuable resources. LoBSs' users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs' risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs' risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing.

Promoting religious acceptance: the relationship between intercultural competence and religious attitudes among Third Culture Kids

Abstract: The study explored whether intercultural competence was a significant predictor of religious attitudes among Third Culture Kids (TCKs), individuals who had spent their developmental years abroad. I...

Liquid Structure of Bis(trifluoromethylsulfonyl)imide-Based Ionic Liquids Assessed by FT-IR Spectroscopy.

Abstract: Ionic liquids are a fertile and active area of research, in part, due to the unique properties these solvents offer over traditional molecular solvents. Because these properties are rooted in the fundamental ion–ion interactions that govern their liquid structure, there is a strong motivation to characterize the liquid structure of ionic liquids. Infrared spectroscopy is a standard analytical tool for assessing liquid structures, for the intramolecular vibrational modes of the ions composing the materials are often quite sensitive to their local potential energy environment. In this work, we demonstrate that the band asymmetry for the νa(SNS) anion mode of N(Tf)2–-based ionic liquids originates from the dynamic coupling of vibrationally induced dipole moments of anions across a quasilattice. The magnitude of TO–LO splitting is linearly correlated with the number densities of the ionic liquids; an observation that is in accord with the predictions of dipolar coupling theory. Dipole moment derivatives of νa...

New results of U-Pb SHRIMP dating of zircons from upper Wuchiapingian (Upper Permian) deposits in northeastern Russia

Abstract: The first results are presented for U-Pb SHRIMP-II dating of zircons from the upper part of the Khivachian regional horizon (stage) of the Regional Stratigraphic Scale (RSS) of the Permian in northeastern Russia. The obtained isotope age of 255 ± 2 Ma is close to that of the present boundary between the Wuchiapingian and Changhsingian stages of the Permian system in the International Stratigraphic Scale (254.1 Ma). Based on the distribution of bivalves—Intomodesma spp. and Claraioides aff. primitivus (Yin)—in the sections considered, their relations to the stratigraphic positions of the samples considered and dated formerly, and in view of the interregional correlation of recent δ13Сorg data for clayey rocks, one may assume with certainty that most of the regional zone of Intomodesma costatum corresponds to the upper part of the Wuchiapingian stage. Here, the Changhsingian stage in northeastern Asia complies only with the uppermost part of this zone within the I. postevenicum subzone and, partially, of Otoceras layers within the Otoceras concavum zone.

Transient Expression of Lumbrokinase (PI239) in Tobacco (Nicotiana tabacum) Using a Geminivirus-Based Single Replicon System Dissolves Fibrin and Blood Clots

Abstract: Lumbrokinases, a group of fibrinolytic enzymes extracted from earthworm, have been widely used to prevent and treat various cardiovascular diseases. They specifically target fibrin to effectively degrade thrombi without major side effects. Plant expression systems are becoming potential alternative expression platforms for producing pharmaceutical proteins. In this work, a lumbrokinase (PI239) was produced from a plant system. Both wild-type (WT) and plant codon-optimized (OP) PI239 gene sequences were synthesized and cloned into a geminivirus-based single-vector DNA replicon system. Both vectors were independently expressed in tobacco (Nicotiana tabacum) leaves transiently by agroinfiltration. Overexpressed PI239 resulted in sudden tissue necrosis 3 days after infiltration. Remaining proteins were purified through His-tag affinity chromatography and analyzed with SDS-PAGE and Western blot methods. Purified PI239 successfully degraded artificial fibrin with relative activity of 13,400 U/mg when compared with commercial lumbrokinase product. In vitro tests demonstrated that plant-derived PI239 dissolved human blood clots and that the plant expression system is capable of producing functional PI239.

Modified human glucagon-like peptide-1 (GLP-1) produced in E. coli has a long-acting therapeutic effect in type 2 diabetic mice.

Abstract: Glucagon-like peptide 1 (GLP-1) is a very potent insulinotropic hormone secreted into the blood stream after eating. Thus, it has potential to be used in therapeutic treatment of diabetes. The half-life of GLP-1, however, is very short due to its rapid cleavage by dipeptidyl peptidase IV (DPP-IV). This presents a great challenge if it is to be used as a therapeutic drug. GLP-1, like many other small peptides, is commonly produced through chemical synthesis, but is limited by cost and product quantity. In order to overcome these problems, a sequence encoding a six codon-optimized tandem repeats of modified GLP-1 was constructed and expressed in the E. coli to produce a protease-resistant protein, 6×mGLP-1. The purified recombinant 6×mGLP-1, with a yield of approximately 20 mg/L, could be digested with trypsin to obtain single peptides. The single mGLP-1 peptides significantly stimulated the proliferation of a mouse pancreatic β cell line, MIN6. The recombinant peptide also greatly improved the oral glucose tolerance test of mice, exerted a positive glucoregulatory effect, and most notably had a glucose lowering effect for as long as 16.7 hours in mice altered to create a type 2 diabetic condition and exerted a positive glucoregulatory effect in db/db mice. These results indicate that recombinant 6×mGLP-1 has great potential to be used as an effective and cost-efficient drug for the treatment of type 2 diabetes.

Speech-Language Assessment Considerations for American Indian and Alaska Native Children Who Are Dual Language Learners

Abstract: The overrepresentation of American Indian and Alaska Native (AI/AN) children in special education, including children who are dual language learners (DLLs), is a major concern. Speech-language path...

‘Ryot Upon Ryot:’ Sedition During the London Shrieval Election of 1682

Abstract: The London shrieval election of 1682 was more than a selection of local civil servants as it erupted into an episode of violent resistance against the Crown as the Whig liverymen asserted liberty and citizens’ rights against arbitrary government. Without a Parliament, both the Tory and Whig factions were forced to find a new forum by which to further their agendas, and in 1682, the City of London served this purpose. Charles sought to assert the Crown’s authority over the City, silencing those who led the resistance to his policies. The shrieval election represented the perfect opportunity for Charles to do just this as he could have the Tory Lord Mayor appoint a fellow Tory as sheriff to choose the jurors who would be responsible for deciding the fate of those Charles found treasonous. Throughout the electoral process from June to September, Whig partisans were already beginning to plot against what they viewed to be an arbitrary government. The events of the election resulted in more than mere plotting,...

Political Identity, Type of Victim, and Hate Crime-Related Beliefs as Predictors of Views Concerning Hate Crime Penalty Enhancement Laws

Abstract: Little empirical work exists examining predictors of support or opposition of hate crime penalty enhancement laws despite such laws remaining a socially and politically contentious issue. Grounded in legal and philosophical arguments concerning hate crime laws, we tested political identity, victim minority status, and hate crime-related beliefs as predictors of perceptions concerning penalty enhancement laws for bias-motivated crime. Jury-eligible Texas community members (n = 382) participating in a community survey of social and legal attitudes took part in the present study. Participants read a vignette of a hate-motivated homicide that varied victim type (African American, gay, transgender) and answered questions regarding demographics, political identity, and hate crime penalty enhancement support. Qualitatively coded hate crime-related beliefs yielded the following categories: legal arguments, moral statements, victim-related beliefs, offender-related beliefs, and an “other” category. Political identity, legal arguments, victim-related and offender-related beliefs all predicted views of penalty enhancement laws in logistic regression analyses. Exploratory mediation tests identified two pathways: (1) political conservatism–legal arguments–penalty enhancement opposition, and (2) political liberalism–offender beliefs–penalty enhancement support. Implications concerning social justice, public policy, trial consultation practice, and future research are discussed.