In this article, we argue that past efforts to distinguish among types of intimate partner violence in general survey data have committed a critical error--using data on current spouses to develop operationalizations of intimate terrorism and situational couple violence. We use ex-spouse data from the National Violence Against Women Survey (NVAWS) to develop new operationalizations. We then demonstrate that NVAWS current spouse data contain little intimate terrorism; we argue that this is likely to be the case for all general surveys. In addition, the ex-spouse data confirm past findings regarding a variety of differences between intimate terrorism and situational couple violence, including those predicted by feminist theories.
The main purpose of the present study is to demonstrate the structure, mechanisms, and efficacy of community policing and its impact on perceived disorder, crime, quality of life in the community, citizens’ fear, and satisfaction with the police. It compares traditional and community policing paradigms on three dimensions: goal, measurement of outcome, and approach to crime. It concludes that community policing has a comprehensive, community-oriented goal, targets both disorder and crime, and emphasizes both organizational and community measures in police evaluation. It also addresses the criticisms of community policing and tests the heatedly debated relationships concerning community policing, disorder, crime, citizen’s fear, and collective efficacy. The major findings of the study include (1) Harcourt’s falsification of Skogan’s findings is invalid because of the methodological flaws, and therefore does not negate the disorder-crime nexus; (2) Sampson and Raudenbush unintentionally demonstrate, through their reciprocal feedback models, that crime and disorder are indirectly related; (3) disorder has strong direct, indirect, and total effects on crime even with collective efficacy being controlled for; (4) contrary to intuition, disorder elicits more fear than crime; (5) community policing reduces crime indirectly; (6) collective efficacy plays a far less significant role in controlling disorder, crime, and fear than community policing; and (7) citizens’ fear and perceived life quality are significant predictors of citizen satisfaction with the police.
Homicide clearance rates in the United States have been steadily declining from the 1960s through the 1990s. Our study asks: (1) Are the factors commonly identified in homicide clearance research as being related to clearances consistent across time? (2) Can these factors shed light on the decline in homicide clearance rates during the past three decades? (3) How are community area characteristics related to clearances across time? Using Chicago data from 1966 to 1995, we find that the factors vary across time and space in terms of statistical significance and magnitude of their relationships. Specifically, the increasing significance of victim's race and firearm usage may account for some of the decrease in homicide clearance rates. Community area characteristics enhance our understanding of homicide clearances, although to a lesser extent than the victim and situational characteristics of a homicide case.
Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.
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