Hot Spot Effect Research Articles

Signs on glasses: LiDAR data voids, hotspot effect, and reflection artifacts

A key challenge in terrestrial laser scanning (TLS) based as-built survey is the presence of data voids, reflection artifacts, and hotspot effect on glasses. This paper investigates the effects of scanning range, illumination condition, instrument height, and spatial offset of an occluded object between the scanner and glasses with respect to these data artifacts. Experimental results show that ordinary float glass encounters a high percentage of data voids (> 93%) and reflective glass backscatters laser pulses with greater than 50%. Hotspot effect is found notable at azimuth scanning angle between ± 2 and 5° on both types of glasses. Ordinary float glass never results in any reflection artifacts, while they likely emerge when the scanner is set up at a certain height (> 0.7 m) and a closer range (≤ 7.5 m) on the reflective glass. These findings shed light on the future algorithmic development of glass detection, classification, and defects removal.

A fluorescence and SERS dual-mode biosensor for quantification and imaging of Mucin1 in living cells

Mucin1 (MUC1) is a cell surface transmembrane protein overexpressed in multiple types of tumor cells, which is generally considered as a tumor-associated biomarker. Thus, quantifying and imaging of MUC1 in tumor cells is of great significance for the diagnosis and biological therapy of tumors. Herein, a fluorescence (FL) and surface-enhanced Raman scattering (SERS) dual-mode biosensor was developed for sensitive detection and imaging of MUC1 in living cells. The FL-SERS biosensor was based on self-assembled satellite structures mediated by the competition of MUC1 and complementary DNA modified on Au nanostars (AuNS-cDNA-PEG) with aptamer modified on quantum dots (QD-PDDA-Apt). This biosensor achieved dual-mode quantification and imaging of MUC1 by quenching the FL signal of QD and significantly enhancing the SERS signal of PDDA through the metal FL quenching effect and hotspot effect of AuNS, respectively. The dual-mode biosensor exhibited high sensitivity to MUC1, with a detection limit of 1.19 fg/mL under FL mode and 1.16 fg/mL under SERS mode. Moreover, this biosensor displayed good selectivity, nice biological stability and low cytotoxicity. Importantly, this biosensor possessed an excellent MUC1 dual-mode imaging capability with high specificity in different tumor cells, providing a new idea for clinical diagnosis of tumors.

Mechanistic basis of atypical TERT promoter mutations

Non-coding mutations in the TERT promoter (TERTp), typically at one of two bases −124 and −146 bp upstream of the start codon, are among the most prevalent driver mutations in human cancer. Several additional recurrent TERTp mutations have been reported but their functions and origins remain largely unexplained. Here, we show that atypical TERTp mutations arise secondary to canonical TERTp mutations in a two-step process. Canonical TERTp mutations create de novo binding sites for ETS family transcription factors that induce favourable conditions for DNA damage formation by UV light, thus creating a hotspot effect but only after a first mutational hit. In agreement, atypical TERTp mutations co-occur with canonical driver mutations in large cancer cohorts and arise subclonally specifically on the TERTp driver mutant chromosome homolog of melanoma cells treated with UV light in vitro. Our study gives an in-depth view of TERTp mutations in cancer and provides a mechanistic explanation for atypical TERTp mutations.

Measuring and modelling directional effects in the frame of TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope)

Abstract. TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.

Temperature dependence of gold nanostar particles morphology and its SERS application

ABSTRACT Gold nano-star particles (GNS) have broad application prospects in Raman enhancement, photovoltaic, and catalysis fields because of the hot spot effect of their tip structure. However, metal nanoparticles will accumulate heat during applications of high-power laser-focused irradiation, which may lead to the morphological degradation of the nanoparticles. In this paper, we have studied the effect of annealing temperature on the surface tip structure of GNS. Firstly, GNS with the multi-tip structure were synthesised by the two-step method, and GNS thin films were prepared by self-assembly technique without auxiliaries. By means of SEM characterisation, the film is found to be compact and uniform. Secondly, we systematically studied the morphology degradation of GNS at 100°C, 150°C, 200°C and 250°C. The results show that the tip of GNS does not change obviously until the temperature reaches 200°C. Then the four kinds of GNS films were used as surface-enhanced Raman scattering (SERS) substrates, and their Raman enhancement properties have been studied with adenine as the analyte, and stronger Raman enhancement effect can be found for the tip of nanoparticles. For a SERS substrate corresponding to an annealing temperature of 100°C, we have obtained the limit sensitivity of adenine molecule of 10−9 M. Finally, the surface electric field distribution and local resonance spectrum curves of four kinds of gold nanoparticles with different tip sizes were simulated theoretically by using the finite element method, and the results are consistent with the experimental data.

Machine learning-aided quantitative detection of two mixed antibiotics in a narrow concentration range based on oxygen incorporation-induced 3D SERS substrates

Amoxicillin (AMO) and ciprofloxacin (CIP) as frequently-used antibiotics are often simultaneously taken for anti-infection to protect pig and poultry production, while their effective therapeutic window is narrow. How to quantitatively detect the two antibiotics mixed in a narrow concentration range is essential to ensure their treatment efficacy and food safety. In this study, quantitative detection of AMO and CIP mixed in animal flesh in one order of magnitude concentration range was performed using a triple SERS signal amplification sensor combined with machine learning. The SERS sensor based on oxygen incorporation (Oi)-induced 3D NiO nanoflowers (NFs) coated with Ag nanoparticles included 3D hot spot effect from strong internal electromagnetic couple in NiO NFs, the Oi effect of NiO and synergistic charge transfer effect of between NiO and Ag. Based on triple enhancement effects, sensitive SERS detection of AMO and CIP was made with the limits of detection of 1 nM and 100 nM. Through the use of machine learning techniques like principal component analysis and partial least square regression, qualitative and quantitative detection of AMO and CIP in their mixture from 1.33 μM to 8.69 μM was successfully achieved, where the average proportion of absolute prediction error (p ≤ 0.15) for concentration was 98.06 %.

Evolution mechanism of Ti(C,N)-based cermet microstructure under microwave sintering

Experimental studies of microwave-sintered cermets were carried out at different sintering temperatures and holding times to investigate their microstructure evolution mechanism. Results of the study show that: (1) As sintering temperature increases, main microstructure evolution mechanisms of cermets are as follows: metal-phase melting, hard-phase skeleton filling of large pores in hard-phase skeletons, secondary rearrangement of the liquid phase (the liquid phase gradually fills the space between grains), and cooling down (precipitation–crystallization–reattachment); (2) As holding time increases, main microstructure evolution mechanisms of cermets are as follows: complete filling of large pores in the hard-phase skeleton, secondary rearrangement of the liquid phase, intense dissolution precipitation enhancing the formation of core-rim phase, and secondary homogeneous arrangement of the liquid phase. The special electromagnetic effects of microwave-sintered cermets and their influence on the sintering process were analyzed. It was found that both tip-discharge effect and hot-spot effect occurring during the microwave sintering process are conducive to the sintering process. The formation process of core-rim phase of cermets was also studied. Results show that: (1) Cermets form black core–white rim–grey rim structure via dissolution–precipitation and precipitation–crystallization phenomena; (2) Cermets form white core–grey rim structure through agglomeration of small particles, dissolution–precipitation, and precipitation–crystallization phenomena.

Investigation of heat and mass transfer properties of the wave staggered round table cooling flow field with a three-stage forked distribution zone

Research on proton exchange membrane fuel cells (PEMFC) has mainly focused on the design of gas channels, often neglecting the effects of distribution zones and cooling flow fields (CFF) on the cell. Therefore, a novel bipolar plate (BP) structure of metallic material is proposed in this paper, which adopts a three-stage forked distribution zone and a wave staggered round table cooling flow field (WSRTCFF). This study systematically investigates the effects of WSRTCFF presence or absence, flow direction, velocity, and inlet temperature on heat and mass transfer as well as PEMFC performance. The results show that introducing WSRTCFF significantly improves temperature distribution in the flow field. Compared to hydrogen flow direction, oxygen flow direction not only effectively mitigates local hot spot effects but enhances current density by 12.55 % compared to no cooling. When coolant flow velocity is below 0.1 m/s, increasing velocity promotes system heat dissipation, enhancing working efficiency and cell stability. However, when velocity exceeds 0.1 m/s, the change in the flow velocity has minimal effect on PEMFC performance. Furthermore, comparative analysis of different coolant temperatures indicates that at 333.15 K, maximum temperature remains below 348.2 K, maintaining membrane hydration and achieving a good balance of substances, temperatures, and performance.

A lightweight joint metric detection approach on YOLO for hot spots in photovoltaic modules

The hot spot effect is one of the primary causes of damage to photovoltaic (PV) modules and a significant factor contributing to the decline in their power generation capacity. Thermal imaging inspection of PV modules is an indispensable aspect of PV plant operation and maintenance. This article introduces a lightweight detection algorithm for hot spots in PV modules based on an enhanced version of YOLOv8. The algorithm incorporates a lightweight convolution operator, Volumetric Grid Spatial Cross Stage Partial, as a replacement for the original C2f operator, resulting in effective reduction of the model's parameter size and improved detection speed. Additionally, the Content-Aware ReAssembly of FEatures upsampling operator is utilized instead of the nearest-neighbor algorithm during the upsampling process, thereby minimizing the loss of hot spot feature information. Furthermore, a joint metric approach combining the Complete Intersection over Union metric and the normalized Wasserstein distance metric is proposed to calculate the localization loss of the target bounding boxes, thereby enhancing the regression accuracy of small hot spot prediction boxes. Experimental results demonstrate that the improved YOLOv8 network enables fast and accurate detection of hot spots. This method achieves an impressive average precision of 98.8% and a high detection speed of 218.3 fps.

A LiDAR-driven three-dimensional simulation model for far-red solar-induced chlorophyll fluorescence in forests

Solar-induced chlorophyll fluorescence (SIF) is a subtle but informative probe of plant photosynthesis. Quantifying the three-dimensional (3D) distribution of SIF benefits a better understanding of photosynthesis variations over heterogeneous canopies. Although radiative transfer models (RTMs) provide a solid theoretical basis for simulating the 3D SIF distribution, most RTMs use virtual scenes with complex reconstruction processes. This study aims to develop a 3D SIF simulation model (FluorLiDAR) directly driven by terrestrial light detection and ranging (LiDAR) data using leaf and canopy RTMs, including a 3D PAR (photosynthetically active radiation) simulation model, the Fluspect model, the atmosphere radiative transfer module in SCOPE, and the multiple scattering coefficients of sunlit and shaded leaves from the 4-scale model. The results show that (1) the simulated and measured SIF patterns were consistent, with R2 (RMSE) values of 0.73 (0.17 mW/nm/m2/sr) and 0.76 (0.12 mW/nm/m2/sr) for 1-min sampling and 10-min averages, respectively. Moreover, the R2 between FluorLiDAR and the DART simulation reached 0.94. The R2 of FluorLiDAR and DART were both higher than 1D mSCOPE using every 10-min sampling data. (2) Point density, denoted by average NPD (nearest point distance), influenced the performance of our model mainly when it was smaller than 0.1 m. Chlorophyll content had less influence on the model accuracy (R2 and rRMSE), and the bias between simulation and measurement decreased as chlorophyll content increased. (3) The simulated 3D SIF distribution pattern closely resembled PAR within the canopy. Besides, FluorLiDAR can simulate the hot spot effect like DART and mSCOPE though the effect was not as obvious as the other two models. This study highlights the potential of a LiDAR-driven SIF model for 3D SIF simulation over a heterogeneous canopy, which may benefit the understanding of the structural impacts on forest photosynthesis in real forest scenes.

Co-abatement of greenhouse gas and air pollutants in Shanghai, China: Spatial hotspots identification, effects assessment and policy implication

Driving co-abatement of Greenhouse Gas (GHGs) and Air Pollutants (APs) in the city level is crucial for fostering societal green and low-carbon transitions, yet comprehensive and refined researches at this level remain limited. To facilitate urban fine management of GHGs control and APs reduction, this study targeted nine categories of anthropogenic emission sources in Shanghai, a typical megacity of China, analyzing the co-benefits of three types of GHGs (CO2, CH4, N2O) and seven types of APs (SO2, NOx, CO, VOCs, NH3, PM2.5, PM10) via emissions flow, spatial distribution, hotspot regions identification, and scenario prediction. Results highlighted the source heterogeneity of different types and significant contributions of energy consumption. CO2 emissions showed a strong spatial correlation with SO2, NOx, and CO, followed by VOCs and PM. Hotspot regions for CO2-VOCs, CO2-NOx and CO2-SO2 co-abatement included power plants, petrochemical enterprises and chemical industrial parks in the southern coastal areas, iron and steel enterprises and power plants in the northern coastal areas, and airport areas in the central and eastern coastal areas, presenting great potential maximum reduction benefits. Achieving positive co-benefits in industrial sector would depend on the steady decline of CO2 emissions in power generation and steel industries. Introducing carbon capture devices and improving energy efficiency would be more beneficial to CO2 emission reduction, while increasing the share of clean energy and phasing out outdated vehicles, machinery, or production capacities are more effective in reducing APs. These mitigation measures could achieve 68.8 % and 47.6 % reduction for CO2 and APs by 2050, respectively, and the co-effect of CO2 and APs emission reduction would gradually increase with the continuous implementation of these measures.

An Energy-Efficient Clustering Approach for Wireless Sensor Networks to Reduce Hot-Spot Effect and Idle Listening Energy Consumption

An Energy-Efficient Clustering Approach for Wireless Sensor Networks to Reduce Hot-Spot Effect and Idle Listening Energy Consumption

Ultrafast recrystallization and grain refinement of 2A97 Al-Cu-Li alloy by electropulsing assisted annealing at lower temperature

In the current work, a pulsed current was applied on recrystallization annealing to promote the recrystallization of cold-rolled 2A97 Al-Cu-Li alloys. Besides, the effect of stored deformation energy on electropulsing assisted recrystallization was also investigated. The results indicated that, with the assistance of electropulsing, the complete recrystallization temperature of the 55 % cold-rolled alloys was decreased from 480 °C to 440 °C, and the holding time was significantly decreased from 30 min to 15 s. Moreover, the recrystallization promotion effect of electropulsing was enhanced with the increment of stored deformation energy. As the rolled reduction increased from 23 % to 55 %, the drop in complete recrystallization temperature induced by electropulsing rose from 20 °C to 40 °C. Additionally, the average grain size decreased from 46.8 μm to 22.6 μm, leading to an enhancement in the mechanical properties of the 2A97 alloy. The microstructure observation showed that the electropulsing assisted recrystallization was a process of nucleation and nuclei growth dominated by dislocation rearrangement, and the ultrafast recrystallization at lower temperature induced by electropulsing may be attributed to the reduction of the nucleation energy barrier and the promotion of dislocation rearrangement caused by the ‘local hot spot effect’ and ‘electromigration effect’.

Fiber Optic LSPR Sensing AFM1 in Milk with Enhanced Sensitivity by the Hot Spot Effect Based on Nanogap Construction.

The detection of the amount of aflatoxin M1 (AFM1) in milk is crucial for food safety. Here, we utilize a fiber optic (FO) localized surface plasmon resonance (LSPR) biosensor by constructing gold nanoparticle (AuNP) multimers, in which the nanogaps amplified the LSPR signal by the hot spot effect, and achieved a highly sensitive detection of f AFM1. Through the optimization of parameter conditions for the fabrication of the sensor and detection system, a high performance result from the FO LSPR biosensor was obtained, and the method for AFM1 detection was established, with a wide detection range of 0.05-100 ng/mL and a low limit of detection (LOD) of 0.04 ng/mL, and it has been successfully validated with the actual sample milk. Therefore, it is a good strategy to fabricate highly sensitive FO LSPR sensors for detecting AFM1 by constructing AuNP multimers, and this approach is suitable for developing other biosensors.

Evaluating the Performance of the Enhanced Ross-Li Models in Characterizing BRDF/Albedo/NBAR Characteristics for Various Land Cover Types in the POLDER Database

The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R2 > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products.

Thiol-functionalized mesoporous silica-embedded AuNPs with highly sensitive substrates for surface-enhanced Raman scattering detection

Surface-enhanced Raman spectroscopy (SERS) provides a fast, simple, and label-free approach that can directly detect various analytes attached to SERS substrates and provide qualitative and quantitative information based on the SERS spectra of the analyte. The signal enhancement provided by noble metal particles allows us to achieve high sensitivity and good stability even at low concentrations in water quality testing. Over the past decade, SERS has become increasingly popular in the detection field, highlighting its significant application potential. However, the signal enhancement of single AuNPs is inferior to that of AgNPs, but we can achieve an overall enhancement by uniformly arranging multiple AuNPs. The hot-spot effect, which is crucial for Raman signal enhancement, occurs only when NMNPs are within a specific distance. However, when noble metal particles come into contact with each other, the enhancement effect of the hot spot disappears. Therefore, in this study, AuNPs@MPS-SH substrates with surface-enhanced Raman scattering (SERS) activity were successfully synthesized by immobilizing AuNPs on thiol-functionalized mesoporous silica (MPS-SH). MPS nanospheres with tailored pore size and surface-bound -SH groups were synthesized for the controlled embedding of Au nanoparticles (AuNPs). By adjusting the ratio of reactants, uniform AuNP dispersion and strong MPS-AuNP interactions were achieved. This optimized structure creates localized surface plasmon resonance (LSPR) hotspots, significantly enhancing the surface-enhanced Raman scattering (SERS) effect for ultrasensitive analyte detection. Transmission electron microscopy (TEM) revealed that the average diameter of MPS nanoparticles is 260 nm, with a pore size of 8 nm. Optimal Raman enhancement was achieved at an AuNPs concentration of 2.6 × 1018 particles/mL. The lowest detection limit concentration for Rhodamine 6G was below 10−6 M, while for uremic toxin (urea) it was below 10−3 M. The ability to directly detect low concentrations by mixing with the analyte demonstrates the ultra-high sensitivity and label-free detection potential of this study in the fields of water quality and disease detection.

Short-term effect of liquid organic fertilisation and application methods on N2, N2O and CO2 fluxes from a silt loam arable soil

AbstractThe absence of N2 flux measurements in liquid manure-amended soils has resulted in a poor understanding of the effect of manure application on gaseous N losses. The aim of this study was to quantify N2, N2O, CO2, N2O reduction to N2, depth distribution of moisture, water-extractable organic C, NO3−, NH4+, pH, and diffusivity in a laboratory incubation experiment with an arable silt-loam soil. To quantify N processes and gaseous fluxes, 15N tracing was applied. An artificial livestock slurry-mixture was added to the soil in various treatments (control, surface or injected application; slurry-application rate: 42.9 kg N ha− 1; soil water content of either 40% or 60% water-filled pore space (WFPS)). The soil was incubated for 10 days. The depth distribution of the control parameters was measured twice during the experiment on days 5 and 10. The average increase in N2 and N2O fluxes from denitrification was about 900% in slurry-amended soils. The highest N2 and N2O fluxes from denitrification were measured in the slurry injection, 60% WFPS treatment (7.83 ± 3.50 and 11.22 ± 7.60 mg N m− 2 d− 1, respectively). The hypothesis that injected slurry at a higher water content enhances denitrification was confirmed. This study provides important insights into the formation, spatial and temporal variation of the manure-soil hotspot and its impact on the denitrification process. The results will form part of a dataset to develop, improve and test manure application submodules of biogeochemical models and will help to understand in detail the effect of hotspots on N-cycling in manure-treated soils.

Large-scale SU-8 Micro-Pole-Arrays decorated with Hierarchical Metal-Dielectric-Metal nanostructures as sensitive 3D SERS substrates

Dielectric layer-mediated surface-enhanced Raman scattering (SERS) substrates are currently explored for ultrasensitive detection and expected to provide uniform SERS response by the virtue of three-dimensional micro-nanostructures, dielectric layer, and plasmonic metal nanostructures. For this purpose, the present work reports on the development of the metal-dielectric-metal (MDM) three-dimensional (3D) micro-pole-arrays structure based on SU-8 colloid lithography. By utilizing the large-scale periodic arrangement characteristics of SU-8 colloidal lithography, along with the hot spot effect created by the nano-gap between MDM, we successfully prepared an array of SERS substrates that exhibit both periodicity and multi-dimensional plasmonic coupling effect. Compared to a single metal nanoparticle substrate, the SERS activity of the substrate is significantly enhanced, and the substrate has trace detection ability (LOD of detection of 10−12 mol/L for R6G). The enhancement factor (EF) is 5.72 × 107. Periodicity ensures that the prepared substrate has good reproducibility (RSD < 8 %) and stability. Furthermore, the substrate modified with HS-β-CD demonstrated potential for rapid trace SERS detection of polychlorinated biphenyls (PCB-77) (LOD of 10−8 mol/L for PCB-77). The results indicate that the proposed substrate has the potential for application in the preparation of SERS sensors with high sensitivity and uniformity.

Adjacent object distorts the organ-of-interest in filtered back-projection tomographic image reconstruction: 'ramp filter' or 'reconstruction' artifact revisited.

To simulate the artifact caused by an adjacent object on organ-of-interest during filtered back-projection (FBP) tomographic reconstruction (the so-called "ramp filter" artifact) and to demonstrate the extent to which an organ-of-interest is influenced by such adjacent hot spot or attenuating object. Two simulations are conducted with two simplified phantoms: "hot spot" and "cardiac." First one is used to visualize effect of hot spot on its periphery. Second one is used to evaluate effect of nearby object (liver) on left ventricle (LV) as organ-of-interest. To generate sinograms, forward projection process is done with and without modeling radiation attenuation. FBP using windowed ramp filter is done. All slices are analyzed by plotting intensity profiles. In tomographic slices, there is a hypo-intense halo around presumed edge of object when compared to background intensity, more noticeable in phantoms with less blurring. Even with ramp filter applied, no halo is visible in FBP without attenuation for cardiac phantom. In contrast, in slices with considering attenuation, zones with different levels of count suppression on both sides of object are visualized instead. The most prominent one is between liver and LV in simulation with higher-attenuating object and higher activity. A single hot spot with sufficient amount of blurring does not distort its surroundings. Hot spots and attenuating objects near organ-of-interest, however, distort myocardial perfusion imaging. Artifactual defects are thus only created when attenuation is modeled during FBP, producing zones of count suppression between organ-of-interest and nearby object or hot spot.

THE EFFECTS OF TRANSACTION HOTSPOTS AND FLIPPING HOTSPOTS ON HOUSING PRICES

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.