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Objectives Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.

Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems.

Justin D'Ambrosio and I have recently and independently defended perceptual adverbialism from Frank Jackson's well-known Many-Properties Problem. Both of us make use of a similar strategy: characterizing ways of perceiving by using the language of objects, and not just of properties. But while D'Ambrosio's view does indeed validate the inferences that Jackson's challenge highlights, it does so at the price of validating additional, invalid inferences, such as the inference from the claim that a small child hallucinates a bottle of aspirin to the claim that the child hallucinates a bottle of acetylsalicylic acid. My view avoids this. The crucial difference is that D'Ambrosio appeals to success conditions, which are extensional, while I appeal to informational content, which is not.

Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.

Every month in every neighborhood in Chicago, residents, teachers, school principals, and police officers gather to deliberate about how to improve their schools and make their streets safer. Residents of poor neighborhoods participate as much or more as those from wealthy ones. All voices are heard. Since the meetings began more than a dozen years ago, they have led not only to safer streets but also to surprising improvements in the city's schools. Chicago's police department and school system have become democratic urban institutions unlike any others in America. Empowered Participationis the compelling chronicle of this unprecedented transformation. It is the first comprehensive empirical analysis of the ways in which participatory democracy can be used to effect social change. Using city-wide data and six neighborhood case studies, the book explores how determined Chicago residents, police officers, teachers, and community groups worked to banish crime and transform a failing city school system into a model for educational reform. The author's conclusion: Properly designed and implemented institutions of participatory democratic governance can spark citizen involvement that in turn generates innovative problem-solving and public action. Their participation makes organizations more fair and effective. Though the book focuses on Chicago's municipal agencies, its lessons are applicable to many American cities. Its findings will prove useful not only in the fields of education and law enforcement, but also to sectors as diverse as environmental regulation, social service provision, and workforce development.

Prediction of the properties of chemical compounds by mathematical methods of pattern recognition is considered. The investigation was carried out by the example of the activity of cell division enzyme inhibitors. An approach based on mixtures of algorithms is used as the method for the construction of recognition models. A two-phase solution procedure for the structure–property problem is analyzed. The local classifier based on the nearest neighbor algorithm and the method of clustering sets is also described. New algorithms for the construction of classifier mixtures are compared. The methods of coordinated prediction of the activity of new compounds are examined. A comparison of mathematical modeling results with molecular design methods based on the coordination of compounds with known structures of therapeutic targets is also presented. An experimental study of the biological activity is conducted.

A new approach to analysis of the molecule-descriptor matrix in the structure-property problem, based on the fuzzy cluster structure of the training sample, is developed. Methods for constructing fast prediction rejection rules and for the search of outliers in a training sample are described. To that end, a special space of easily computed descriptors is introduced. Optimization of the classifying function with respect to the parameters of fuzzy classification is considered. Prognostic models with a high quality of prediction, based on this approach, are proposed. Comparison of models is performed, which shows the efficiency of the described methods.

A method for solving the “structure-property” problem is proposed based on the adaptive choice of molecule description and automatic selection of a feature space according to the characteristics of the learning sample. A problem of combinatory explosion is solved using the method for group account of arguments. The algorithms for cluster analysis are used to improve the predictive capability of the model. The results of model construction using specific learning samples of chemical compounds are discussed.

Cooperatives are of particular interest to economists because of their unique ownership structure and the incentives this structure creates. In addition to the so-called property rights problems (e.g., freerider, horizon, and portfolio problems), the analysis of agricultural cooperatives has focused on issues of market power, agency, product quality, and increasingly producer and consumer heterogeneity. These last three elements are important features of the industrialization of the agrifood system. This article highlights the key concepts required for examination of cooperatives now and in the future and incorporates these concepts into a framework that can be used to examine the myriad situations and problem settings in which agricultural cooperatives are likely to be found. A key finding of the paper is that the procompetitive and distributional impacts of cooperatives depend critically on the sensitivity of price in the downstream retail market, the nature of the cooperative's governance structure, and the open or closed nature of cooperative membership. The article also provides a discussion of new areas in which an understanding of cooperatives and collective action would be valuable, as well as a discussion of the applicability of the proposed framework to these areas.