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Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study. [ABSTRACT FROM AUTHOR]

This article presents a multi‐dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification‐based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime‐prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real‐world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases. [ABSTRACT FROM AUTHOR]

With the emergence in the field of crime prediction, researchers found that crime shows geographical patterns. These patterns can be useful to predict crime before it happens and allows police to take proactive measures. Crime prediction finds application in areas like predictive policing, Hotspot evaluation and geographic profiling. Each category of crime holds some relation with time, weather, location, census parameters like annual income, literacy rate of the area. All these serve as indicators for predicting crime. In this work, historic crime events are used as indicators to predict crime. In this paper, a spatiotemporal crime prediction technique based on machine learning coupled with 2-Dimensional Hotspot analysis is proposed. For performing 2-Dimensional Hotspot analysis clustering is used. Performance of the proposed model is compared when it used state of the art classification techniques without hotspot analysis and with hotspot analysis and it is found that model with hotspot analysis achieves better performance. [ABSTRACT FROM AUTHOR]

In this paper, the convection dynamics of super paramagnetic iron oxide nanoparticles (MNPs) in blood fluid is studied. The system under consideration consists of a fluid which contains the layer of blood with MNPs having iron oxide core (Fe3O4) nanoparticle which is moving through the cavity of the blood vessel which has depositions of cholesterol on the inner lining. In its passing through the blood vessel it is subjected to external magnetic field and heat. The nonlinear three dimensional governing equations for the system under study are derived from the partial differential equations of conservation of momentum and energy. The effect of magnetic field for Hartman number on the chaotic convection of the MNPs in blood fluid is studied using phase portrait, time series and stability analysis. It is assumed that a minimum magnetic field is necessary to stabilise the convection of the MNPs to regulate its flow in specific directions so that the drug can be delivered to specific locations by magnetic dragging. Rectangular cavity structure is considered for the study based on non-uniform cholesterol deposition. It is observed that with magnetisation by external applied magnetic field the convection of MNPs are stabilised in a particular direction. This indicates towards the growing utilization as contrast agents in non-invasive imaging technique of magnetic resonance imaging (MRI) and magnetic drug delivery (MDD). [ABSTRACT FROM AUTHOR]

This paper deals with the dynamic response of a single-wall carbon nanotube (CNT) based on thin-walled shell mode. Following the subsequent motion of zigzag single-wall CNT under-surface roughness effect, a non-linear dynamic response may occur. The results carried out using Phase Plot through variation of surface roughness parameter which shows that the surface roughness parameter plays an important role in changing the regular motion into the chaotic. The principle point of interest is the conditions of significant interaction to occur and the increased chaos associated with the altered surface roughness parameter in zigzag CNT. The surface roughness parameter plays a significant role in changing the regular motion from revolution to liberation and infinite period separatrix for zigzag CNT which can be utilised for different application in engineering, environmental sciences, nanotechnology, tribology, medicines, pharmaceuticals and in industry. [ABSTRACT FROM AUTHOR]

This paper describes the theoretical analysis of the surface roughness and slip velocity effect on magnetic fluid based porous-pivoted slider bearing. The bearing surface is defined by stochastic random variable with skewness, variance and non-zero mean. The concerned stochastically averaged Reynolds equation is solved numerically to get the pressure distribution. The expression for dimensionless centre of pressure is obtained in form of integrals. The value of centre of pressure is calculated and plotted numerically. It is observed that the value of centre of pressure depends on surface roughness, magnetic, slip and permeability parameters. It is concluded that slip and permeability parameters play a vital role for increasing the life of bearing system roughness. [ABSTRACT FROM AUTHOR]