Intense Hot Spots Research Articles

CriPAV: Street-Level Crime Patterns Analysis and Visualization.

Extracting and analyzing crime patterns in big cities is a challenging spatiotemporal problem. The hardness of the problem is linked to two main factors, the sparse nature of the crime activity and its spread in large spatial areas. Sparseness hampers most time series (crime time series) comparison methods from working properly, while the handling of large urban areas tends to render the computational costs of such methods impractical. Visualizing different patterns hidden in crime time series data is another issue in this context, mainly due to the number of patterns that can show up in the time series analysis. In this article, we present a new methodology to deal with the issues above, enabling the analysis of spatiotemporal crime patterns in a street-level of detail. Our approach is made up of two main components designed to handle the spatial sparsity and spreading of crimes in large areas of the city. The first component relies on a stochastic mechanism from which one can visually analyze probable×intensive crime hotspots. Such analysis reveals important patterns that can not be observed in the typical intensity-based hotspot visualization. The second component builds upon a deep learning mechanism to embed crime time series in Cartesian space. From the embedding, one can identify spatial locations where the crime time series have similar behavior. The two components have been integrated into a web-based analytical tool called CriPAV (Crime Pattern Analysis and Visualization), which enables global as well as a street-level view of crime patterns. Developed in close collaboration with domain experts, CriPAV has been validated through a set of case studies with real crime data in São Paulo - Brazil. The provided experiments and case studies reveal the effectiveness of CriPAV in identifying patterns such as locations where crimes are not intense but highly probable to occur as well as locations that are far apart from each other but bear similar crime patterns.

Fast Topology Optimization for Near-Field Focusing All-Dielectric Metasurfaces Using the Discrete Dipole Approximation.

Using an efficient implementation of the discrete dipole approximation and topology optimization, we design all-dielectric metasurfaces capable of focusing light into intense deep subwavelength hotspots. The light focusing of these metasurfaces far outweighs conventional lenses and can provide dramatic enhancements of processes that depend superlinearly on light intensity, such as light-powered membrane distillation and photocatalysis. Our approach can easily be generalized to optimize metasurfaces for other functionalities, such as nonlinear optics or photothermal conversion.

Optimizing Plasmodium vivax serological surveillance within a coendemic epidemiological landscape

Serological surveillance is a useful tool for revealing hotspots of transmission intensity or cryptic asymptomatic reservoirs, especially as malaria transmission declines. Such approaches can help us to understand malaria epidemiology, but also to guide interventions. Recently, Longley et al. refined a panel for Plasmodium vivax serological surveillance to aid in malaria elimination.

Visualizing changes in physical activity behavioral patterns after redesigning urban infrastructure.

The aim of this study was to explore effects of a major urban reconstruction on physical activity (PA) behavior by comparing PA intensity hotspots before and after the tunneling of a highway with a new infrastructure prioritized for walking and cycling. In total, 126 individuals participated before and after the tunneling. GPS loggers and accelerometers were used to assess location and PA levels. A geographic information system (GIS) was used to perform optimized hotspot analyses on PA data, both on transport and stationary data points. The results showed several changes in PA hotspots on trip data, even if total PA levels did not change. At follow-up, PA intensity hotspots were more connected, with the new infrastructure as a central connection. This was true for higher and lower educated individuals. Therefore, if changes in the built environment do not result in changes on population-level outcomes, this does not imply that they have no impact on behavior.

APPORT DE LA GEOMATIQUE ET DES STATISTIQUES SPATIALES POUR LA CARTOGRAPHIE ET LANALYSE DES DEPRESSIONS EN LIEN AVEC LES POTENTIELS GITES LARVAIRES DANS LE DISTRICT SANITAIRE DE COCODY-BINGERVILLE

Contribution of geomatics and spatial statistics for the mapping and analysis of depressions related to potential larvate locations in the health district of cocody-bingerville.The depressions or basins in the landscape constitute zones of water stagnation and present themselves as potential favorite zones for the female Anopheles mosquito to lay her eggs. This increases the vectorial capacity with the consequence of a risk of malaria attack for the population in the health district of Cocody-Bingerville. The objective of this study is to analyze the geostatistical distribution of depressions in order to contribute to vector control.Sheis based on geospatial and statistical analysis of depressions in said health district. Thus the integration and processing of AlosPalsar data of 12.5 m spatial resolution in a GIS and spatial autocorrelation techniques have been applied for the modeling of depression zones. The presence of local clusters was further assessed using Morans statistical index to assess the intensity of hot spots (High-High Cluster) and cold spots (Low-Low cluster).Local statistics from Moran I were used to perform cluster and value analysis in the spatial distribution of depressions in the landscape. It reveals a z-score of 56.91 and a Morans index of 0.07. These positive statistical values attest that the observed phenomenon is highly dependent on the topography, hence the groupings or clustering of the depressions.

Research and Development of Source Term Activity Reconstruction System Based on Deep Learning

The radiation damage weakens the efficiency of decommissioning nuclear facilities, and the source term is hard to be located and measured because it often distributes in or inside objectives. In practice, the hot spot and source intensity are measured by the gamma camera then the radiation field is estimated by particle transport algorithm as a reference in scheme planning. This technology is expensive in measuring and simulating. Hence, this paper presents a source term activity reconstruction (hereinafter referred to as STAR) method based on deep learning to solve this problem. Firstly, a UNET liked framework is constructed to establish the correlation between the radiation field and source activity. Secondly, a source activity reconstruction problem is used to validate the framework. Then we deploy it to the Raspberry Pi with a γ-ray detector to verify it can be applied in practical works. The verification results show that Raspberry Pi can complete source term activity reconstruction in a few seconds without consuming too much computing resources. In framework validating, 5000 samples consist of randomly generated activity distribution and its grid dose value which is calculated by the Monte-Carlo program. The results show that the average reconstruction error is less than 15% and the trained framework is performing well in Raspberry Pi. This method reduces the requirements for instruments and the dose detection is parallelable. Therefore, it can be widely used in nuclear facility decommissioning to improve the efficiency of source reconstruction.

Analysis of thermo-hydraulic characteristics for flow of MWCNT-Fe3O4/H2O hybrid nanofluid through a wavy channel under magnetic field

We investigate computationally the transport characteristics for flow of MWCNT-Fe3O4/H2O hybrid nanofluid through a wavy channel under the influence of an externally applied magnetic field. The flow and temperature fields are analyzed in terms of streamlines, isotherms, average Nusselt number ([Formula: see text]), magnetic enhancement ratio ( ERm), and magnetic performance factor ( PFm) for the following range of parameters: 100[Formula: see text]Re[Formula: see text]500, 0[Formula: see text] Ha[Formula: see text]10, and 0% [Formula: see text]0.3%. The strength of the reverse flow in the recirculatory zones decreases with Ha, and beyond a critical Ha, the flow becomes attached even in the diverging part of the channel. The hot spot intensity near the walls decreases and hence, the value of [Formula: see text] increases with the increase in volume fraction of nano-particles and Ha. The rate of increment of [Formula: see text] is steeper at lower values of Ha. It further reveals that PFm monotonically decreases with Ha for lower Re ( = 100). For higher values of Re, PFm decreases with Ha for its lower and higher values, while for the intermediate range of Ha, PFm increases with Ha.

Herscheldiscovery of far-infrared emission from the hotspot D in the radio galaxy Cygnus A

ABSTRACTThe far-infrared counterpart of hotspot D, the terminal hotspot of the eastern jet hosted by the radio galaxy Cygnus A, is detected with Herschel Aperture photometry of the source performed in 5 photometric bands covering the wavelength range of 70–350 $\mu$m. After removing the contamination from another nearby hotspot, E, the far-infrared intensity of hotspot D is derived as 83 ± 13 and 269 ± 66 mJy at 160 and 350 $\mu$m, respectively. Since the far-infrared spectrum of the object smoothly connects to the radio one, the far-infrared emission is attributed to the synchrotron radiation from the radio-emitting electron population. The radio-to-near-infrared spectrum is confirmed to exhibit a far-infrared break feature at the frequency of $\nu _\mathrm{br}=2.0^{+1.2}_{-0.8} \times 10^{12}$ Hz. The change in energy index at the break (Δα = 0.5) is interpreted as the impact of radiative cooling on an electron distribution sustained by continuous injection from diffusive shock acceleration. By ascribing the derived break to this cooling break, the magnetic field, B, in the hotspot is determined as a function of its radius, R within a uniform one-zone model combined with the strong relativistic shock condition. An independent B–R constraint is obtained by assuming the X-ray spectrum is wholly due to synchrotron self-Compton emission. By combining these conditions, the two parameters are tightly determined as B = 120–150 μG and R = 1.3–1.6 kpc. A further investigation into the two conditions indicates the observed X-ray flux is highly dominated by the synchrotron self-Compton emission.

Target-triggered core-satellite self-assemblies based on strong dipole plasmon Pt-tipped Au triangular nanoprism for dual-signal detection of telomerase and enhanced phototherapy

The detection and imaging of human telomerase in living cells is of great significance for early diagnosis and treatment of cancers. Herein, we designed a new promising target-induced self-assembled core-satellite (C-S) structure for label-free Raman detection and fluorescence imaging of telomerase in cancer cells and enhanced photothermal and photodynamic treatment of tumors. Nanoprobe would be triggered by telomerase to form C-S structure to improve the intensity of hot spots and thus to generate intense SERS signal change of adenine (A) for label free Raman detection of telomerase, then the signal of chlorin e6 (Ce6) would be released for in situ fluorescence imaging. Our experimental findings proved that such a strategy exhibited great sensitivity and selectivity for telomerase. In addition, the Pt nanozymes decorated on the tip of Au TNPs showed good catalase-like capacity and stability which can convert endogenous H2O2 to produce O2 to avoid cell hypoxia and promote the generation of cytotoxic singlet oxygen (1O2) for enhanced photodynamic therapy of tumors. This nanoprobe was successfully employed for in situ imaging of telomerase in different kinds of cells and for combination therapy of tumors. It further proves that this is a new biological strategy with far-reaching significance for early cancer diagnosis and treatment.

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients.

Simple SummaryTumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine.This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Integrated Molecular Optomechanics with Hybrid Dielectric-Metallic Resonators.

Molecular optomechanics describes surface-enhanced Raman scattering using the formalism of cavity optomechanics as a parametric coupling of the molecule’s vibrational modes to the plasmonic resonance. Most of the predicted applications require intense electric field hotspots but spectrally narrow resonances, out of reach of standard plasmonic resonances. The Fano lineshapes resulting from the hybridization of dielectric–plasmonic resonators with a broad-band plasmon and narrow-band cavity mode allow reaching strong Raman enhancement with high-Q resonances, paving the way for sideband resolved molecular optomechanics. We extend the molecular optomechanics formalism to describe hybrid dielectric–plasmonic resonators with multiple optical resonances and with both free-space and waveguide addressing. We demonstrate how the Raman enhancement depends on the complex response functions of the hybrid system, and we retrieve the expression of Raman enhancement as a product of pump enhancement and the local density of states. The model allows prediction of the Raman emission ratio into different output ports and enables demonstrating a fully integrated high-Q Raman resonator exploiting multiple cavity modes coupled to the same waveguide.

Plasmonic Core-Shell-Satellites with Abundant Electromagnetic Hotspots for Highly Sensitive and Reproducible SERS Detection.

In this work, we develop a Ag@Al2O3@Ag plasmonic core–shell–satellite (PCSS) to achieve highly sensitive and reproducible surface-enhanced Raman spectroscopy (SERS) detection of probe molecules. To fabricate PCSS nanostructures, we employ a simple hierarchical dewetting process of Ag films coupled with an atomic layer deposition (ALD) method for the Al2O3 shell. Compared to bare Ag nanoparticles, several advantages of fabricating PCSS nanostructures are discovered, including high surface roughness, high density of nanogaps between Ag core and Ag satellites, and nanogaps between adjacent Ag satellites. Finite-difference time-domain (FDTD) simulations of the PCSS nanostructure confirm an enhancement in the electromagnetic field intensity (hotspots) in the nanogap between the Ag core and the satellite generated by the Al2O3 shell, due to the strong core–satellite plasmonic coupling. The as-prepared PCSS-based SERS substrate demonstrates an enhancement factor (EF) of 1.7 × 107 and relative standard deviation (RSD) of ~7%, endowing our SERS platform with highly sensitive and reproducible detection of R6G molecules. We think that this method provides a simple approach for the fabrication of PCSS by a solid-state technique and a basis for developing a highly SERS-active substrate for practical applications.

AGN−Host Interaction in IC 5063. I. Large-scale X-Ray Morphology and Spectral Analysis

Abstract We report the analysis of the deep (∼270 ks) X-ray Chandra data of one of the most radio-loud, Seyfert 2 galaxies in the nearby universe (z = 0.01135), IC 5063. The alignment of the radio structure with the galactic disk and ionized bicone, enables us to study the effects of both radio jet and nuclear irradiation on the interstellar medium (ISM). The nuclear and bicone spectra suggest a low photoionization phase mixed with a more ionized or thermal gas component, while the cross-cone spectrum is dominated by shocked and collisionally ionized gas emission. The clumpy morphology of the soft (<3 keV) X-ray emission along the jet trails, and the large (≃2.4 kpc) filamentary structure perpendicular to the radio jets at softer energies (<1.5 keV), suggest a large contribution of the jet−ISM interaction to the circumnuclear gas emission. The hard X-ray continuum (>3 keV) and the Fe Kα 6.4 keV emission are both extended to kpc size along the bicone direction, suggesting an interaction of nuclear photons with dense clouds in the galaxy disk, as observed in other Compton Thick (CT) active nuclei. The northwest cone spectrum also exhibits an Fe xxv emission line, which appears spatially extended and spatially correlated with the most intense radio hot-spot, suggesting jet−ISM interaction.

Identifying seasonal spatial patterns of crime in a small northern city

ObjectivesTo explore spatial patterns of crime in a small northern city, and assess the degree of similarity in these patterns across seasons.MethodsCalls for police service frequently associated with crime (theft, break and enter, domestic dispute, assault, and neighbor disputes) were acquired for a five year time span (2015–2019) for the city of North Bay, Ontario, Canada (population 50,396). Exploratory data analysis was conducted using descriptive statistics and a kernel density mapping technique. Andresen’s spatial point pattern test (SPPT) was then used to assess the degree of similarity between the seasonal patterns (spring, summer, autumn, winter) for each call type at two different spatial scales (dissemination area and census tract).ResultsExploratory data analysis of crime concentration at street segments showed that calls are generally more dispersed through the city in the warmer seasons of spring and summer. Kernel density mapping also shows increases in the intensity of hotspots at these times, but little overall change in pattern. The SPPT does find some evidence for seasonal differences in crime pattern across the city as a whole, specifically for thefts and break and enters. These differences are focused on the downtown core, as well as the outlying rural areas of the city.ConclusionsFor the various crime types examined, preliminary analysis, kernel density mapping, and the SPPT found differences in crime pattern consistent with the routine activities theory.

Mechanically Tunable Nanogap Antennas: Single‐Structure Effects and Multi‐Structure Applications (Advanced Optical Materials 20/2021)

Plasmonic nanogap antennas offer attractive properties in terms of their adjustable coupling and high electric near-fields within the gap region, e.g. for sensing purposes or plasmon rulers. By changing the width of a nanometer gap, the coupling condition, plasmon resonance wavelength, and hotspot intensity can be seamlessly modified. In their Review (article number 2100326), Florian Laible, Anke Horneber and Monika Fleischer highlight recent realizations and applications of single-structure and mechanically tunable nanogap antennas based on flexible substrates, break junctions, or alternative stimuli.

Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar

This study investigates the water – electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water – electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran’s I, Anselin Local Moran's I, and Getis-Ord Gi∗ statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people’s mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions.

Characterisation of SiPM Photon Emission in the Dark.

In this paper, we report on the photon emission of Silicon Photomultipliers (SiPMs) from avalanche pulses generated in dark conditions, with the main objective of better understanding the associated systematics for next-generation, large area, SiPM-based physics experiments. A new apparatus for spectral and imaging analysis was developed at TRIUMF and used to measure the light emitted by the two SiPMs considered as photo-sensor candidates for the nEXO neutrinoless double-beta decay experiment: one Fondazione Bruno Kessler (FBK) VUV-HD Low Field (LF) Low After Pulse (Low AP) (VUV-HD3) SiPM and one Hamamatsu Photonics K.K. (HPK) VUV4 Multi-Pixel Photon Counter (MPPC). Spectral measurements of their light emissions were taken with varying over-voltage in the wavelength range of 450–1020 nm. For the FBK VUV-HD3, at an over-voltage of V, we measured a secondary photon yield (number of photons () emitted per charge carrier ()) of . The emission spectrum of the FBK VUV-HD3 contains an interference pattern consistent with thin-film interference. Additionally, emission microscopy images (EMMIs) of the FBK VUV-HD3 show a small number of highly localized regions with increased light intensity (hotspots) randomly distributed over the SiPM surface area. For the HPK VUV4 MPPC, at an over-voltage of V, we measured a secondary photon yield of . In contrast to the FBK VUV-HD3, the emission spectra of the HPK VUV4 did not show an interference pattern—likely due to a thinner surface coating. The EMMIs of the HPK VUV4 also revealed a larger number of hotspots compared to the FBK VUV-HD3, especially in one of the corners of the device. The photon yield reported in this paper may be limited if compared with the one reported in previous studies due to the measurement wavelength range, which is only up to 1020 nm.

Ubiquitiform Hotspot Ignition Model of PBX for Shock Initiation

AbstractIn this study, a ubiquitiform hotspot ignition model with cross‐scale characteristics is proposed to describe the ignition stage of PBX, which can account for heterogeneous effects. Firstly, a ubiquitiform model of hotspot intensity is obtained. The model can describe the hotspot information after PBX is impacted. In which, a hotspot distribution model based on the nested ubiquitiform model of explosives is used to characterize geometric properties of hotspot, including the size and location of hotspot; and the Weibull statistical distribution function is used to describe the distribution of hotspot intensity. Then, combining the ubiquitiform model of hotspot intensity with Arrhenius model, the ubiquitiform hotspot ignition model is developed to characterize the ignition stage of PBX. Finally, it is found that the equivalent reaction rate constants calculated by our ignition model are in good agreement with the previous experimental data.

The Kuroshio flowing over seamounts and associated submesoscale flows drive 100-km-wide 100-1000-fold enhancement of turbulence

Although previous studies reported that currents over topographic features, such as seamounts and ridges, cause strong turbulence in close proximity, it has been elusive how far intense turbulence spreads toward the downstream. Here, we conducted a series of intensive in-situ turbulence observations using a state-of-the-art tow-yo microstructure profiler in the Kuroshio flowing over the seamounts of the Tokara Strait, south of Kyusyu Japan, in November 2017, June 2018, and November 2019, and employed a high-resolution numerical model to elucidate the turbulence generation mechanisms. We find that the Kuroshio flowing over seamounts generates streaks of negative potential vorticity and near-inertial waves. With these long-persisting mechanisms in addition to other near-field mixing processes, intense mixing hotspots are formed over a 100-km scale with the elevated energy dissipation by 100- to 1000-fold. The observed turbulence could supply nutrients to sunlit layers, promoting phytoplankton primary production and CO2 uptake.

Nonlinear Hall effect as a local probe of plasmonic magnetic hot spots

Recently developed plasmonic nanostructures are able to generate intense and localized magnetic hot spots in a large spectral range from the terahertz to the visible. However, a direct measurement of the magnetic field at the hot spot has not been performed yet, due to the absence of magnetic field detectors that work at those high frequencies and that fit the hot-spot area. We propose to place a graphene ribbon in the hot spot of a plasmonic nanostructure driven by a laser beam, such that a current is generated due to both the magnetic field at the hot spot and the electric field of the laser. We demonstrate that a nonlinear Hall voltage, which can be measured by standard electrical means, builds up across the ribbon, making it possible to directly probe the magnetic field at the hot spot.