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Imagine using an evidence-based risk management model that enables researchers and practitioners alike to analyze the spatial dynamics of crime, allocate resources, and implement custom crime and risk reduction strategies that are transparent, measurable, and effective. Risk Terrain Modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate forecasts of where crime will occur at the microlevel. RTM informs decisions about how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce worst effects. As a diagnostic method, RTM offers a statistically valid way to identify vulnerable places. To learn more, visit http://www.riskterrainmodeling.com and begin using RTM with the many free tutorials and resources.
Crime analysis --- Spatial analysis (Statistics) --- Crime --- Crime forecasting --- Crime prevention. --- Crime prevention --- Prevention of crime --- Public safety --- Forecasting, Crime --- Social prediction --- Criminal behavior, Prediction of --- City crime --- Crime and criminals --- Crimes --- Delinquency --- Felonies --- Misdemeanors --- Urban crime --- Social problems --- Criminal justice, Administration of --- Criminal law --- Criminals --- Criminology --- Transgression (Ethics) --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- Police administration --- Statistical methods. --- Environmental aspects. --- Geographic information systems. --- Prevention --- Government policy --- Forecasting --- Social aspects --- Analysis --- contexts of crime. --- crime emergence. --- crime exposure. --- crime persistence. --- crime prediction. --- crime. --- criminal behavior. --- criminals. --- custom strategies. --- exposure. --- forecasting crimes. --- identifying risk. --- minimizing risk. --- modeling methods. --- risk management. --- risk reduction. --- rtm. --- spatial dynamics of crime. --- surveillance. --- theory of risky places. --- vulnerable places.
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Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measurement
n/a --- inosine --- immune checkpoint inhibitor --- geometric singular perturbation theory --- simulation --- BioModels Database --- ADAR --- calcium current --- bifurcation analysis --- bacterial biofilms --- nonlinear dynamics --- explanatory model --- turning point bifurcation --- oscillator --- workflow --- bioreactor integrated modeling --- modeling methods --- elementary flux modes visualization --- multiscale systems biology --- evolutionary algorithm --- metabolic model --- differential evolution --- reduced-order model --- computational model --- gut microbiota dysbiosis --- canard-induced EADs --- computational biology --- metabolic modelling --- methods --- SREBP-2 --- mechanistic model --- systems modeling --- biological networks --- macromolecular composition --- provenance --- flux balance analysis --- immunotherapy --- compartmental modeling --- immuno-oncology --- metabolic network visualization --- mechanism --- bistable switch --- Clostridium difficile infection --- bioreactor operation optimization --- microRNA targeting --- CFD simulation --- biomass reaction --- RNA editing --- ordinary differential equation --- metabolic modeling --- mass-action networks --- hybrid model --- multiple time scales --- quantitative systems pharmacology (QSP) --- mathematical modeling --- microRNA --- cancer --- parameter optimization --- Hopf bifurcation --- breast
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