Hot Spot Model Research Articles

Hotspots: On the Usefulness of a New Category

Abstract Does it make sense to introduce the notion of hotspot as a heuristically valuable category in the study of religion? In the first part of the article, I argue for the value of the notion by teasing the term apart into semiotic categories. Over and against the customary sacred–profane binary, which in my view does not represent a dichotomy but two opposite poles on a continuum, the hotspot model has the advantage of presenting itself in both temporal and spatial terms. Moreover, it includes the intermediary stages, signified by the distinction between lukewarm and lukecold locations and periods. Furthermore, I supplement this model with the Weberian typology of the different forms of authority needed in order to be able to differentiate between different types of hotspots. In the second part of the article, I try to demonstrate the value of the model by applying it to a variety of cases in the history of religions. Finally, I present reflections on the evolutionary origin of hotspots as a bridge between the two main parts holding that, to explain any phenomenon in the phenomenology of religion, it is urgent to find the evolutionary building blocks for it.

Parameter Estimation for Hot-spot Thermal Model of Power Transformers Using Unscented Kalman Filters

This paper presents a parameter estimation technique for the hot-spot thermal model of power transformers. The proposed technique is based on the unscented formulation of the Kalman filter, jointly considering the state variables and parameters of the dynamic thermal model. A two-stage estimation technique that takes advantage of different loading conditions is developed, in order to increase the number of parameters which can be identified. Simulation results are presented, which show that the observable parameters are estimated with an error of less than 3%. The parameter estimation procedure is mainly intended for factory testing, allowing the manufacturer to enhance the thermal model of power transformers and, therefore, its customers to increase the lifetime of these assets. The proposed technique could be additionally considered in field applications if the necessary temperature measurements are available.

Influencing mechanisms of a wax layer on the micro-friction behavior of the β-HMX crystal surface

This study investigated the influence of the wax layer on the micro-friction behavior of the energetic crystal surface by constructing the lubricating interface between wax and β-HMX crystals. The nano-scratch testing of β-HMX crystals with and without wax coating was conducted under the ramp-loading (0–3.5 ​mN) mode and the constant-loading (0.4 ​mN) mode. The testing results are as follows. Compared with the dry friction of β-HMX crystals without wax coating, wax can significantly improve the contact friction between the energetic crystals and the rigid micro-convex body. The influence of wax on the interface friction highly depends on the external load conditions. Under the ramp-loading mode, the wax layer can greatly reduce the average coefficient of friction (COF) from ∼0.7 (dry friction) to ∼0.2 (lubricating interface) and inhibit COF fluctuation. The inherent mechanism of wax reducing the interface friction works in the following way. Wax can effectively suppress the plowing effect of the rigid micro-convex body on the β-HMX crystal surface and further inhibit the occurrence of brittle fracture and crack defects. The wax layer can also suppress the friction anisotropy of the β-HMX crystal surface, significantly reducing the probability of high COF (>0.4). The distribution probabilities of COF in the range of 0–0.1 and 0–0.3 were 33% and 89%, respectively. The stick-slip effect of the friction behavior was observed under the constant-loading mode, with the COF varying periodically with the sliding distance. These study results can help understand the desensitization mechanisms of wax on the energetic crystal surface at a micro-scale quantitative level and provide a necessary basis for building the friction hot-spot model under dry friction and wax lubrication.

A Study of Land Ecological Environment Evaluation Based on an Ideal Point Model

The paper took Guang’an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang’an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated.

Source identification and spatial distribution of heavy metals in soil of central urban area of Chongqing, China

ABSTRACT To understand the characteristics of heavy metal pollution in soil and identify the source of heavy metals, 342 surface soil samples were collected in Chongqing, China. The contents of 11 heavy metals (i.e., arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni), mercury (Hg), antimony (Sb), manganese (Mn), and molybdenum (Mo)) were determined. The Principal Component analysis/absolute Principal component fraction (PCA/APCS) receptor model, the classified regression (CATREG) model, and the hot spot model were employed to analyze the data. The contents of As, Cd, Cr, Pb, Cu, Zn, Ni, Hg, Sb, Mn, and Mo in soil were 5.80, 0.13, 76.56, 25.55, 23.99, 75.58, 30.50, 0.05, 0.64, 573.32, and 0.56 mg·kg−1, respectively. The results of principal component analysis showed that the main heavy metal elements under the first principal component load were Cu, Ni, Zn, Mn, Cr, Pb, and Cd. The second principal component is mainly loaded with Mo, As, Hg, and Sb. The results of classified regression analysis showed that population density mainly affected Cu (0.54), soil mainly affected Ni (0.41), Sb (0.49), Zn (0.47), and Mn (0.21), and water quality mainly affected As (0.45) and Mo (0.37). Air quality mainly affected Cd (0.33) and Cr (0.37), traffic activity mainly affected Hg (0.31), and slope mainly affected Pb (0.31). The research results can be used to trace the environmental sources of soil heavy metals, fundamentally prevent and repair soil heavy metal pollution, and protect urban soil environmental quality.

Group behavior dissemination model of social hotspots based on data enhancement and data representation

The emergence and dissemination of hotspots in social networks mainly depend on the participation of group users. In this paper, considering the sparsity and complexity of effective data, we propose a group behavior dissemination model on the basis of data enhancement and data representation. First, given the inaccurate prediction results caused by the sparsity of valid data and the advantages of Generative Adversarial Networks (GAN) in learning data distribution and enhancing data, GAN is introduced to generate homomorphic data. We found that the accuracy improved by at least 6%. Second, with the diversity and complexity of the hotspot feature space and the ability of representation learning to mine hidden features of the hotspot, we designed a new method, called HP2vec, convert the feature space to a low rank and dense vector. Finally, considering the dynamic time limit of hotspot spreading, we create time slices to discretize the life of a hotspot and propose a dynamic dissemination method based on CNN. The experimental section shows that the accuracy of this method is as high as 86% in Weibo dataset and as high as 93% in twitter dataset. The model alleviates data sparseness and effectively predicts the group behavior in hotspots.

Spatial efficiency of protected mangrove areas in Madagascar

Protected Areas (PAs) are an important nature-based solution for mangrove conservation and rehabilitation. We evaluated spatial effectiveness of PAs for mangroves toward achieving Global Conservation Targets (GCTs). The hypothesis for this study was that PAs with different attributes have insignificant effects on mangrove conservation. We assessed the proportions of the most vulnerable mangroves inside PAs, and focused on a typical mangrove country (Madagascar). First, based on remote sensing technology and big data in Google Earth Engine (GEE), we identified the exposure location of mangroves, and determined the environmental factors significantly influencing mangrove distribution. Then, Vulnerability Assessment and Hot-Spot Analysis models were used to measure spatial vulnerability and hotspots of those values, respectively. Finally, we implemented the statistics for the most vulnerable mangroves inside PAs. It was found that: i. Mangroves were mainly abundant in west and east coasts with low latitudes, and the most typical environmental factor influencing mangrove distribution was elevation and; ii. PAs sheltered 486.18 km2 (22.16%) of the most vulnerable mangroves in Madagascar. Overall, PAs in Madagascar failed to match 30% of spatial requirements proposed by GCTs (A key proportion of spatial requirements used to reverse trends in biodiversity loss). This study provides a quantitative paradigm for verifying the spatial efficiency of PAs, and will inform local decision-makers on places where mangroves are facing adaption loss to optimize mangrove conservation in future.

Correlated metals and unconventional superconductivity in rhombohedral trilayer graphene: A renormalization group analysis

Motivated by recent experimental observations of correlated metallic phases and superconductivity in rhombohedral trilayer graphene (RTG), we perform an unbiased study of electronic ordering instabilities in hole-doped RTG. Specifically, we focus on electronic states energetically proximate to Van Hove singularities (VHSs), where a large density of states promotes different interaction-induced symmetry-breaking electronic orders. To resolve the Fermi surface near VHSs, we construct a fermionic hot-spot model and demonstrate that a perpendicular electric field can tune different nesting structures of the Fermi surface. Subsequently, we apply a renormalization group analysis to describe the low-energy phase diagrams of our model under both short-range repulsive interactions as well as realistic (long-range) Coulomb interactions. Our analysis shows instabilities towards either intervalley coherent metallic phases or superconducting phases. The dominant pairing channel depends crucially on the nature of Fermi surface nesting -- repulsive Coulomb interaction favors spin-singlet $d$-wave pairing for relatively small displacement field and spin-singlet $i$-wave pairing for larger displacement field. We argue that the phase diagram of RTG can be well-understood by modeling the realistic Coulomb interaction as the sum of repulsive density-density interaction and ferromagnetic spin-triplet intervalley coherence (IVC) Hund's coupling, while phonon-mediated electronic interactions have a negligible effect on this system, in sharp contrast to twisted graphene multilayers.

Fate identification and management strategies of non-recyclable plastic waste through the integration of material flow analysis and leakage hotspot modeling

Low priority on waste management has impacted the complex environmental issue of plastic waste pollution, as evident by results of this study where it was found that 24.3% of waste generation in Jakarta and Bandung is emitted into the waterway due to the high intensity of human activity in the urban area. In this study, we investigated the viable integration between material flow analysis and leakage hotspot modeling to improve management strategies for plastic pollution in water systems and open environments. Using a multi-criteria assessment of plastic leakage from current waste management, a material flow analysis was developed on a city-wide scale defining the fate of plastic waste. Geospatial analysis was assigned to develop a calculation for identification and hydrological analysis while identifying the potential amount of plastic leakage to the river system. The results show that 2603 tons of plastic accumulated along the mainstream of the Ciliwung River on an annual basis, and a high-density population like that in Bandung discarded 1547 tons in a one-year period to the Cikapundung River. The methods and results of this study are applicable towards improving the control mechanisms of river rejuvenation from plastic leakage by addressing proper management in concentrated locations.

Common Structural Features of Hydrophobic α-Helical Hot Spots: Insights for the Design of Novel α-Helix Mimetics.

The binding conformations of α-helical hydrophobic hot spots are convergent into two spatial areas in protein-protein complex structures. The physical basis for convergence was disclosed, which allows the development of pharmacophore models for i/i + 4/i + 7 or i/i + 3/i + 4 α-helical hot spots. Further investigation revealed that this convergence of binding conformations is common among all hydrophobic hot spots regardless of their α-helical positions. This observation led to a streamlined generation of pharmacophore models for hydrophobic hot spots at any positions along the α-helix. These successfully evaluated pharmacophore models may be useful for designing novel α-helical hot spot mimetics.

Analysis of core asymmetries in inertial confinement fusion implosions using three-dimensional hot-spot reconstruction

Three-dimensional effects play a crucial role during the hot-spot formation in inertial confinement fusion (ICF) implosions. A data analysis technique for 3D hot-spot reconstruction from experimental observables has been developed to characterize the effects of low modes on 3D hot-spot formations. In nuclear measurements, the effective flow direction, governed by the maximum eigenvalue in the velocity variance of apparent ion temperatures, has been found to agree with the measured hot-spot flows for implosions dominated by mode ℓ=1. Asymmetries in areal-density (ρR) measurements were found to be characterized by a unique cosine variation along the hot-spot flow axis. In x-ray images, a 3D hot-spot x-ray emission tomography method was developed to reconstruct the 3D hot-spot plasma emissivity using a generalized spherical-harmonic Gaussian function. The gradient-descent algorithm was used to optimize the mapping between the projections from the 3D hot-spot emission model and the measured x-ray images along multiple views. This work establishes a platform to analyze 3D low-mode core asymmetries in ICF.

User Behavior Prediction of Social Hotspots Using Interaction with Multiple Messages and Neural Networks

Abstract: The variety of communications under social hot topics has a significant impact on user engagement behaviour in network public opinion study. This article suggests a prediction model of user participation behaviour during repeated messaging of trending social issues, taking into account interactions between numerous messages and complicated user behaviours. A multimessage interaction influence-driving method was first presented to better precisely forecast user involvement behaviour by taking into account the impact of multimessage interaction on user participation behaviour. Second, this study proposes a user participant behaviour prediction model of social hotspots based on a multimessage interaction-driving mechanism and the BP neural network. This is done in light of the behavioural complexity of users participating in multimessage hotspots and the simple structure of backpropagation (BP) neural networks (which can map complex nonlinear relationships).

HOTSPOT PREDICTIVE MODELING USING REGRESSION DECISION TREE ALGORITHM

Forest fires had always become an international issue influencing many life sectors, including environmental, social, and economic. The forest fire in 2013 was regarded as one of the worst forest fire tragedies in history, not only in Indonesia but also in the world. Detection of hotspots on the earth's surface by the satellite can be an indication of land and forest fire occurrence. This research aims to build a predictive model of monthly hotspots in Rokan Hilir Regency using the regression tree algorithm. Several variables related to weather information are included, such as rainfall, sea surface temperature, and southern oscillation index. This research used 245 training data and 43 testing data, resulting a predictive model with a correlation of 0.875 and an error rate of 0.166. Based on the values, we can conclude that the performance of the model is considerably good.

Role of self-generated magnetic fields in the inertial fusion ignition threshold

Magnetic fields spontaneously grow at unstable interfaces around hot-spot asymmetries during inertial confinement fusion implosions. Although difficult to measure, theoretical considerations and numerical simulations predict field strengths exceeding 5 kT in current National Ignition Facility experiments. Magnetic confinement of electrons then reduces the rate of hot-spot heat loss by >5%. We demonstrate this via magnetic post-processing of two-dimensional xRAGE hydrodynamic simulation data at bang time. We then derive a model for the self-magnetization, finding that it varies with the square of the hot-spot temperature and inversely with the areal density. The self-magnetized Lawson analysis then gives a slightly reduced ignition threshold. Time-dependent hot-spot energy balance models corroborate this finding, with the magnetic field quadrupling the fusion yield for near-threshold parameters. The inclusion of magnetized multi-dimensional fluid instabilities could further alter the ignition threshold and will be the subject of future work.

Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India.

Modern human civilization has suffered from the disastrous impact of COVID-19, but it teaches us the lesson that the environment can restore its stability without human activity. The Government of India (GOI) has launched many strategies to prevent the situation of COVID-19, including a lockdown that has a great impact on the environment. The present study focuses on the analysis of Particulate Matter 2.5 (PM2.5) concentration levels in pre-locking, lockdown, and unlocking phases across ten major cities of Maharashtra (MH) that were the COVID hotspot of India during the COVID-19 outbreak; phase-wise and year-wise (2018–2020) hotspot analysis, box diagram and line graph methods were used to assess spatial variation in PM2.5 across MH cities. Our study showed that the PM2.5 concentration level was severe at pre-lockdown stage (January–March) and it decreased dramatically at the lockdown stage, later it also increased in its previous position at the unlocking stages, i.e., PM2.5 decreased dramatically (59%) during the lockdown period compared to the pre-lockdown period due to the shutdown of outdoor activities. It returns to its previous position due to the unlocking situation and increases (70%) compared to the lockdown period which illustrated the ups and downs of PM2.5 and ensures the position of different cities in the Air Quality Index (AQI) categories at different times. In the pre-lockdown phase, maximum PM2.5 concentration was in Navi Mumbai (NAV) (358) and Mumbai (MUM) (338), and Pune (PUN) (335) and Nashik NAS (325) subsequently, whereas at the last of the lockdown phase, it becomes Chandrapur (CHN) (82), Nagpur (NAG) (76), and Solapur (SOL) (45) subsequently. Hence, the restoration of the environment during the lockdown phase was temporary rather than permanent. Therefore, our findings propose that several effective policies of government such as relocation of polluting industries, short-term lockdown, odd–even vehicle number, installation of air purifier, and government strict initiatives are needed in making a sustainable environment.

Energy dependence of the proton geometry in exclusive vector meson production

The gluon radius of the proton is expected to increase at small gluon momentum fractions $x$, an effect which has hitherto not been considered in the dipole model framework. We investigate the energy dependence of exclusive $J/\ensuremath{\psi}$, $\ensuremath{\phi}$, and $\ensuremath{\rho}$ production by introducing three models for $x$ dependence of the gluon thickness function. We allow the transverse width of the proton to increase as $x$ decreases, using novel parametrizations in the spherical proton and the hotspot model. We compare these with a model where the number of hotspots increases as $x$ decreases and confront the models with HERA data. The models exhibit clear differences in the slope of the $t$-spectra and in the cross section ratios between coherent and incoherent events. Comparisons to $t$-slopes and ${W}_{\ensuremath{\gamma}p}$ measurements show a preference for models where the proton's size increases as $x$ decreases.

Validation of the hot-spot model with terrestrial laser scanning

Validation of the hot-spot theoretical model was carried out with extensive terrestrial laser scanning (TLS) measurements in the Järvselja RAMI pine stand. A 120 m × 120 m × 12 m point cloud of laser hits of 1 cm resolution was built to describe the spatial structure of the crown layer. From this point cloud, foliage density was found at 1 dm resolution, and a Fourier analysis of the spatial structure of the crown layer was performed. The autocorrelation function of the transmittance of 1 dm slices of the crown layer, which controls the hot-spot profile of directional reflectance, was estimated directly from the point cloud. The determined value of the hot-spot parameter was found to agree well with the value estimated indirectly from the measurements of reflectance profile in the principal plane.

Superconducting hot-electron bolometer mixers and their applications

Superconducting hot electron bolometer (HEB) mixers are the most sensitive heterodyne detectors at frequencies beyond 1 THz, and are used for many astronomical observations. HEB, consisting of a narrow and short superconducting thin film patch connected to an antenna via contact pads, is brought into a highly sensitive operating regime by applying a DC current and a certain amount of local-oscillator power. Potentially, superconducting HEB mixers are capable of exhibiting high sensitivity for frequencies up to several tens of THz, as their output noise and conversion gain are nearly frequency independent according to the mixing mechanism. Moreover, the understanding of mixing mechanism has evolved over time from lumped-element model to distributed hot-spot model. In this review, we provide an overview of theoretical and experimental studies on HEBs and their applications, especially in astronomical observations on ground, airborne and space telescopes.

Significant reduction of carbon stocks and changes of ecosystem service valuation of Indian Sundarban

The Sundarban mangrove or tidal influenced natural ecosystem is extremely productive and providing multiple goods and services to millions of people. In the last few decades, the quality and quantity of mangrove ecosystem are being deteriorated. The main objectives of this current research are (i) to investigate the ecosystem service values (ESVs) using a time series satellite data (1975, 2000 and 2020) and different unit values (ii) to analyze the dynamic pattern of carbon sequestration through InVEST model and (iii) determination of ESVs change hotspots by Getis-Ord Gi* method. Here, mangrove forest has the highest ecosystem service value and highest carbon sinker. The total loss of ESVs was estimated 3310.79 million USD during last 45 years in Sundarban Biosphere Reserve (SBR) due to high natural and anthropogenic adversities. InVEST model also revealed that the total static carbon storage over the study area was 48.87, 46.65 and 43.33 Tg for the year 1975, 2000 and 2020 respectively. Total 6313944 mg/6.31Tg loss of carbon has been observed in the case of mangrove forest during the overall study period (1975–2020). So, illegal human encroachment should be strictly (through law and regulations) restricted within Sundarban mangrove ecosystem for the benefits of people.

How does warfare affect firms' productivity?

Warfare afflicts millions and creates unparalleled operational challenges in the developing world. With data from war‐torn Colombia, I examine the effects of civil war on firms' total factor productivity (TFP) using propensity score and conflict hotspot models. I find that warfare increases TFP in service firms, such as public administration and defense, by up to 12.68%, whereas it decreases TFP in manufacturing and other nonservice companies by up to 3.64% a year after a violent episode occurs. This effect stems from intellectual capital growth and service firms' ability to create value from customer input.