Hotspot Locations Research Articles

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

The Recombination Hotspot Paradox: Co-evolution between PRDM9 and its target sites

Recombination often concentrates in small regions called recombination hotspots where recombination is much higher than the genome’s average. In many vertebrates, including humans, gene PRDM9 specifies which DNA motifs will be the target for breaks that initiate recombination, ultimately determining the location of recombination hotspots. Because the sequence that breaks (allowing recombination) is converted into the sequence that does not break (preventing recombination), the latter sequence is over-transmitted to future generations and recombination hotspots are self-destructive. Given their self-destructive nature, recombination hotspots should eventually become extinct in genomes where they are found. While empirical evidence shows that individual hotspots do become inactive over time (die), hotspots are abundant in many vertebrates: a contradiction called the Recombination Hotspot Paradox. What saves recombination hotspots from their foretold extinction? Here we formulate a co-evolutionary model of the interaction among sequence-specific gene conversion, fertility selection, and recurrent mutation. We find that allelic frequencies oscillate leading to stable limit cycles. From a biological perspective this means that when fertility selection is weaker than gene conversion, it cannot stop individual hotspots from dying but can save them from extinction by driving their re-activation (resuscitation). In our model, mutation balances death and resuscitation of hotspots, thus maintaining their number over evolutionary time. Interestingly, we find that multiple alleles result in oscillations that are chaotic and multiple targets in oscillations that are asynchronous between targets thus helping to maintain the average genomic recombination probability constant. Furthermore, we find that the level of expression of PRDM9 should control for the fraction of targets that are hotspots and the overall temperature of the genome. Therefore, our co-evolutionary model improves our understanding of how hotspots may be replaced, thus contributing to solve the Recombination Hotspot Paradox. From a more applied perspective our work provides testable predictions regarding the relation between mutation probability and fertility selection with life expectancy of hotspots.

Road Accident Hotspots on Jordan’s Highway Based on Geometric Designs Using Structural Equation Modeling

One of the primary objectives of transportation engineering is to increase the safety of road infrastructure. This study seeks to determine the relationship between geometric design parameters in relation to road accident criteria based on accident hotspots on Jordan’s Desert Highway. The road accident data (from 2016 to 2019) were collected from the Jordan Traffic Department. The spatial pattern of hotspots was identified using a GIS tool named Getis-Ord Gi* based on the severity index of road accidents. A topographic survey was conducted to investigate the road alignment and intersections at hotspot locations. The study utilized the Structural Equation Modeling (SEM) technique via SmartPLS to highlight the correlation between geometric designs in relation to road accidents. The hotspot analysis (Gits-Ord Gi) discovered 80 road accident hotspots along the highway. The study found that horizontal alignment and road intersections significantly impact road accidents in hotspot locations. Furthermore, vertical alignment has no effect on road accidents in hotspot areas. The study enhanced the comprehension of the factors associated with road geometrics and intersections that affect the occurrence of road accidents.

Yield response and pathological characterization of promising genotypes of soybean against major diseases in Madhya Pradesh

The yield potential along with multiple disease resistance in 38 promising soybean genotypes including four checks i.e. JS 20-34, JS 335, JS 20-98 and NRC 86 was assessed under field condition of JNKVV, Jabalpur whichis one of the hot spot locations for major diseases of soybean like Charcoal rot, Aerial blight and Yellow mosaic. Significant differences were observed for days to 50 per cent flowering, days to maturity, yield (kg/h) and 100 seedweight. The highest yield was obtained from JS 21-72 (2650 kg/h) followed by Himso 1689 (2255 kg/h) and JS 21-71 (2222 kg/h). Maximum 100 seed weight of 14.37g was recorded in JS 21-72 followed by JS 21-71 (14.2g) and JS 20-34 (14.16g). Out of thirty eight, the yield ofseven genotypes i.e. Himso 1688, MAUS 734, DSb 33, NRC 139, SL 1171, VLS 97 and BAUS 100 could not be recorded due to complete mortality of the plants by charcoal rot. Eight genotypes viz., JS 21-71, MACS 1566, JS 21-72, DS 3110, MACS 1620, JS 20-34(C), RSC 11-15 and Himso 1689 against charcoal rot, seven genotypes against aerial blight, eighteen genotypes against YMV and thirty-three (33) against bacterial pustule disease showed high resistance. Overall, three genotypes viz., JS 21-71, JS 21-72 and Himso 1689 exhibited absolute tomoderate resistance against charcoal rot plus absolute/highresistanceto YMV, RAB and bacterial pustule.

ANALYSIS OF SINGLE-VEHICLE AND MULTIPLE-VEHICLE CRASHES ALONG THE HAWAN KIBO CRASH CORRIDOR, PLATEAU STATE, NIGERIA

In-depth analysis of the characteristics of road traffic crashes at blackspots or hotspot locations is, generally, insufficient in Nigeria. This is despite the fact that blackspots represent recognized locations with road safety deficiencies and mitigation of crashes at such locations produce multiple benefits. This paper examines characteristics of road traffic crashes along Hawan Kibo route, one of the most recognized crash corridors in Nigeria, with particular emphasis on single-vehicle (SV) and multiple-vehicle (MV) crashes. The data shows that between 2015 and 2019, 355 crashes were recorded (SV: 219; MV: 136), with 1288 persons sustaining injuries (SV: 652; MV: 636) and 121 fatalities (SV: 46; MV: 75). The most important causes of crashes and casualties for SV crashes were brake failure, speed violation, and fatigue; while wrongful overtaking, brake failure and speed violations were the most prominent for MV crashes. Time of day for crashes was not significantly different between SV and MV crashes but number of persons injured per crash was significantly different between them. Though MV crashes were less in number, they appeared to be more severe. There was no statistically significant difference between the fatality rates per crash for SV and MV crashes even though SV fatality rates were significantly less than those for MV crashes. In the light of the fact that the most prominent causes of crashes and casualties are associated with poor human judgment and attitude, the study suggests that more creative and concerted efforts should be made to educate drivers and passengers on road...

Automatic bike sharing system planning from urban environment features

ABSTRACT The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots.

Anticipating where are unknown aquatic insects in Europe to improve biodiversity conservation

AbstractAimUnderstanding biodiversity patterns is crucial for prioritizing future conservation efforts and reducing the current rates of biodiversity loss. However, a large proportion of species remain undescribed (i.e. unknown biodiversity), hindering our ability to conduct this task. This phenomenon, known as the ‘Linnean shortfall’, is especially relevant in highly diverse, yet endangered, taxonomic groups, such as insects. Here we explore the distributions of recently described freshwater insect species in Europe to (1) infer the potential location of unknown biodiversity hotspots and (2) determine the variables that can anticipate the distribution of unknown biodiversity.LocationThe European continent, including western Russia, Cyprus and Turkey.MethodsGeoreferenced information of all sites where new aquatic insect species were described across Europe from 2000 to 2020 was compiled. In order to understand the observed spatial patterns in richness of recently described species, spatial units were defined (level 6 of HydroBASINS) and associated with a combination of a set of socioeconomic, environmental and sampling effort descriptors. A zero‐inflated Poisson regression approach was used to model the richness of newly described species within each spatial unit.ResultsNine hundred and sixty‐six recently described species were found: 398 Diptera, 362 Trichoptera, 105 Coleoptera, 66 Plecoptera, 28 Ephemeroptera, 3 Neuroptera, 2 Lepidoptera and 2 Odonata. The Mediterranean Basin was the region with the highest number of recently described species (74%). The richness of recently described species per spatial unit across Europe was highest at mid‐elevation areas (between 400 and 1000 m), latitudes between 40 and 50° and in areas with yearly average precipitation levels of 500–1000 mm, a medium intensity of sampling effort and low population density. The percentage of protected areas in each study unit was not significantly related to the richness of recently described species. In fact, 70% of the species were found outside protected areas.Main conclusionsThe results highlight the urgent need to concentrate conservation efforts in freshwater ecosystems located at mid‐altitude areas and out of protected areas across the Mediterranean Basin. The highest number of newly described species in those areas indicates that further monitoring efforts are required to ensure the aquatic biodiversity is adequately known and managed within a context of growing human impacts in freshwater ecosystems.

Uncovering spatiotemporal micromobility patterns through the lens of space–time cubes and GIS tools

In the past ten years, cities have experienced a burst of micromobility services as they offer a flexible transport option that allows users to cover short trips or the first/last mile of longer trips. Despite their potential impacts on mobility and the fact that they offer a cleaner, more environmentally friendly alternative to private cars, few efforts have been devoted to studying patterns of use. In this paper we introduce new ways of visualizing and understanding spatiotemporal patterns of micromobility in Madrid based on the conceptual framework of Time-Geography. Hägerstrand’s perspectives are taken and adapted to analyze data regarding use of micromobility, considering each trip departure location (origins) obtained from GPS records. The datasets are collected by three of the most important micromobility operators in the city. Trip origins (points) are processed and visualized using space–time cubes and then spatially analyzed in a GIS environment. The results of this analysis help to identify the landscape of micromobility in the city, detecting hotspot areas and location clusters that share similar behavior throughout space and time in terms of micromobility departures. The methods presented can have application in other cities and could offer insights for transport planners and micromobility operators to better inform urban planning and transportation policy. Additionally, the information could help operators to optimize vehicle redistribution and maintenance/recharging tasks, reducing congestion and increasing efficiency.

Using geographically weighted regression analysis to assess predictors of home birth hot spots in Ethiopia.

Annually, 30 million women in Africa become pregnant, with the majority of deliveries taking place at home without the assistance of skilled healthcare personnel. In Ethiopia the proportion of home birth is high with regional disparity. Also limited evidence on spatial regression and deriving predictors. Therefore, this study aimed to assess the predictors of home birth hot spots using geographically weighted regression in Ethiopia. This study used secondary data from the 2019 Ethiopian Mini Demographic and Health Survey. First, Moran's I and Getis-OrdGi* statistics were used to examine the geographic variation in home births. Further, spatial regression was analyzed using ordinary least squares regression and geographically weighted regression to predict hotspot area of home delivery. According to this result, Somalia, Afar, and the SNNPR region were shown to be high risk locations for home births. Women from rural residence, women having no-education, poorest wealth index, Muslim religion follower, and women with no-ANC visit were predictors of home delivery hotspot locations. The spatial regression revealed women from rural resident, women having no-education, women being in the household with a poorest wealth index, women with Muslim religion follower, and women having no-ANC visit were predictors of home delivery hotspot regions. Therefore, governmental and other stakeholders should remain the effort to decrease home childbirth through access to healthcare services especially for rural resident, strengthen the women for antenatal care visits.

Joint Confidence Region Approach to Ranking Hotspot Locations Considering Uncertainty in Expected Risk Estimates

Network screening or crash hotspot identification is an essential task of all road safety improvement programs. The most common approach to network screening is to use statistical models to predict the expected risk at the locations of interest and then rank them accordingly. The predicted risk used for ranking is mostly in the form of point estimates, without any consideration of the inherent uncertainty with the estimates, which could lead to identifying a wrong list of crash hotspots. This study aims to fill this research gap by employing a frequentist approach to finding a joint confidence region of risk for ranking locations and identification of hotspots. A case study on three-legged minor approach stop-controlled intersections in Kitchener, Ontario, is conducted to illustrate the proposed approach. Crash risk is modeled using a combination of a hierarchical full Bayesian negative binomial model and a multinomial logit model, which are then used to estimate the 95% confidence interval of the expected risk. For each location, the confidence region of rankings is obtained on the basis of the expected risk estimates. The results show that considering uncertainty in the crash hotspot identification process can lead to varied ranking positions for each location. In fact, considering uncertainty, the true value of the estimated crash risk is unknown. By quantifying uncertainty, it can be concluded that the true value of the estimated risk follows a distribution with different probabilities. As a result, consideration of uncertainty in the road safety analysis may help to identify hotspots more accurately.

Feasibility study on fiber-optic inorganic scintillator array sensor system for multi-dimensional scanning of radioactive waste

We developed a miniaturized multi-dimensional radiation sensor system consisting of an inorganic scintillator array and plastic optical fibers. This system can be applied to remotely obtain the radioactivity distribution and identify the radionuclides in radioactive waste by utilizing a scanning method. Variation in scintillation light was measured in two-dimensional regions of interest and then converted into radioactivity distribution images. Outliers present in the images were removed by using a digital filter to make the hot spot location more accurate and cubic interpolation was applied to make the images smoother and clearer. Next, gamma-ray spectroscopy was performed to identify the radionuclides, and three-dimensional volume scanning was also performed to effectively find the hot spot using the proposed array sensor.

Exploring the diversity of virulence genes in the Magnaporthe population infecting millets and rice in India.

Blast pathogen, Magnaporthe spp., that infects ancient millet crops such pearl millet, finger millet, foxtail millet, barnyard millet, and rice was isolated from different locations of blast hotspots in India using single spore isolation technique and 136 pure isolates were established. Numerous growth characteristics were captured via morphogenesis analysis. Among the 10 investigated virulent genes, we could amplify MPS1 (TTK Protein Kinase) and Mlc (Myosin Regulatory Light Chain edc4) in majority of tested isolates, regardless of the crop and region where they were collected, indicating that these may be crucial for their virulence. Additionally, among the four avirulence (Avr) genes studied, Avr-Pizt had the highest frequency of occurrence, followed by Avr-Pia. It is noteworthy to mention that Avr-Pik was present in the least number of isolates (9) and was completely absent from the blast isolates from finger millet, foxtail millet, and barnyard millet. A comparison at the molecular level between virulent and avirulent isolates indicated observably large variation both across (44%) and within (56%) them. The 136 Magnaporthe spp isolates were divided into four groups using molecular markers. Regardless of their geographic distribution, host plants, or tissues affected, the data indicate that the prevalence of numerous pathotypes and virulence factors at the field level, which may lead to a high degree of pathogenic variation. This research could be used for the strategic deployment of resistant genes to develop blast disease-resistant cultivars in rice, pearl millet, finger millet, foxtail millet, and barnyard millet.

Status of different anthropogenic organic pollutants accumulated in sediments from Olt River Basin, Romania: From distribution and sources to risk assessment

Some organic pollutants including organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) have been banned from production worldwide, but due to their toxicity and persistence are still of concern. Also, unintentional by-products of combustion and industrial processes such as polycyclic aromatic hydrocarbons (PAHs), represent a permanent threat to the safety of the environment and the population. In this study, surface sediment samples from the middle and lower Olt River Basin (ORB), Romania, including dams, the main tributaries and the confluence with Danube River were collected during seasonal sampling campaigns in 2019 and analyzed for 13 OCPs, 12 PCBs and 15 PAHs in order to evaluate the impact of the main anthropogenic activities in the area (industrial activities and agriculture) and the ecological status of the ORB. The registered levels of OCPs, PCBs and PAHs in surface sediments varied from low to significantly polluted environments, indicating a clear spatial distribution between sites based on concentrations and congener profiles correlated with the influence of anthropogenic activities in the surrounding area. Based on some molecular diagnostic ratio and multivariate statistical analysis, both non-point sources and point sources deposition by surface runoff or atmospheric deposition were identified. Overall, the contamination profile of the study area reveals significant amounts of organochlorine compounds, resulting from the industrial production of chlorinated products, including lindane, but also from the long-term agricultural use of both HCHs and DDTs, more than half of the sites having levels that pose a potential risk for benthic organisms. Therefore, levels of POPs in the hot-spots sampling locations raise numerous concerns about the safety of the environment and the population in the region, requiring immediate actions.

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Safety experts and transportation departments are focused on reducing road accidents and their societal and economic effects. The most crucial step in establishing a successful road safety practice is identifying dangerous highway zones through the study of crashes and looking at how the location of accidents relates to surrounding geography and other factors. Using the latest cutting-edge GIS analytical methods, this study aims to map the locations of accident hot spots and evaluate the severity and spatial extent of crash occurrences in Ohio. Road traffic crash (RTC) data has been analyzed using sophisticated GIS-based hot spot analysis for decades by safety researchers. Using four years' worth of crash data from the state of Ohio and spatial autocorrelation analysis, this study aims to show how a GIS technique can be used to find places where accidents are likely to happen (2017–2020). The study analyzed and ranked crash hotspot areas using the matching severity levels of RTCs. Cluster zones of high and low crash severity were discovered using the spatial autocorrelation tool and the Getis Ord Gi* statistics tool to evaluate the distribution of RTCs. The analysis used Getis Ord Gi*, the crash severity index, and Moran's I spatial autocorrelation of accident events. The findings indicated that these techniques were useful for identifying and rating crash hotspot locations. Since the sites of the identified accident hotspots are located in significant cities in the state of Ohio, such as Cleveland, Cincinnati, Toledo, and Columbus, the organizations in charge of traffic management should make it their top priority to minimize the negative socioeconomic impact that RTCs have and should also conduct a thorough investigation. This study's contribution is the incorporation of crash severity into hot spot analysis using GIS, which could lead to better-informed decision-making in the realm of highway safety.

K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia

Forest fires are the most common cause of deforestation in Indonesia. This condition has a negative impact on the survival of living things. Of course, this has received special attention from various parties. One effort that can be made for prevention is to group these points into areas with the potential for fire using the clustering method. In this research, a comparative study of the clustering algorithm between K-Means and K-Medoids was conducted on hotspot location data obtained from Global Forest Watch (GFW). Besides that, important variables that affect the clustering process are also analyzed in terms of feature importance. There are nine important variables used in the clustering process, of which the Acq_time variable is the most important. The cluster quality of both algorithms is evaluated using the silhouette coefficient (SC). Both algorithms are capable of producing strong clusters. The best number of clusters is six clusters. The K-medoids algorithm is better at grouping data than K-means.

Perspectives of opioid prescribers in overdose hotspots and coldspots, Massachusetts, 2019

Background Prescription opioids (POs) have had a devastating effect on people and public health systems in the U.S. Due to the urgency and complexity of the opioid crisis, there is a need to expand qualitative research on the medical community’s perspectives on opioid prescribing practices and the role that prescription drug monitoring programs (PDMPs) have played in mitigating this crisis. Methods We conducted qualitative interviews with clinicians (n = 23) across specialties and a range of overdose hotspot and coldspot locations in Massachusetts during 2019. We aimed to capture their perspectives on the opioid crisis, changes in clinical practice, and experiences with opioid prescribing and PDMPs. Results Respondents consistently recognized the role clinicians played in the opioid crisis and reported reductions in their opioid prescribing, which were motivated by the crisis itself. The limitations of opioids in pain management were frequently discussed. While clinicians appreciated having greater awareness of their opioid prescribing and increased access to patient prescription histories, they also expressed concerns about surveillance of their prescribing and other unintended consequences. We observed that clinicians in opioid prescribing hotspots had more detailed and specific reflections on their experiences with the Massachusetts PDMP, MassPAT. Conclusion Clinician perceptions of the severity of the opioid crisis in Massachusetts and thoughts on their role as prescribers were consistent across specialty, prescribing level, and practice location. Many clinicians in our sample cited use of the PDMP as an influence on their prescribing. Those practicing in opioid overdose hotspots had the most nuanced reflections about the system.

Shorebird Monitoring Using Spatially Explicit Occupancy and Abundance

Loss of habitat and human disturbance are major factors in the worldwide decline of shorebird populations, including that of the threatened migratory piping plover (Charadrius melodus). From 2013 to 2018, we conducted land-based surveys of the shorebird community every other week during the peak piping plover season (September to March). We assessed the ability of a thin plate spline occupancy model to identify hotspot locations on Whiskey Island, Louisiana, for the piping plover and four additional shorebird species (Wilson’s plover (Charadrius wilsonia), snowy plover (Charadrius nivosus), American oystercatcher (Haematopus palliatus), and red knot (Calidris canutus)). By fitting single-species occupancy models with geographic thin plate spline parameters, hotspot priority regions for conserving piping plovers and the multispecies shorebird assemblage were identified on the island. The occupancy environmental covariate, distance to the coastline, was weakly fitting, where the spatially explicit models were heavily dependent on the spatial spline parameter for distribution estimation. Additionally, the detectability parameters for Julian date and tide stage affected model estimations, resulting in seemingly inflated estimates compared to assuming perfect detection. The models predicted species distributions, biodiversity, high-use habitats for conservation, and multispecies conservation areas using a thin-plate spline for spatially explicit estimation without significant landscape variables, demonstrating the applicability of this modeling approach for defining areas on a landscape that are more heavily used by a species or multiple species.

Dicentric chromosome breakage in Drosophila melanogaster is influenced by pericentric heterochromatin and occurs in nonconserved hotspots.

Chromosome breakage plays an important role in the evolution of karyotypes and can produce deleterious effects within a single individual, such as aneuploidy or cancer. Forces that influence how and where chromosomes break are not fully understood. In humans, breakage tends to occur in conserved hotspots called common fragile sites (CFS), especially during replication stress. By following the fate of dicentric chromosomes in Drosophila melanogaster, we find that breakage under tension also tends to occur in specific hotspots. Our experimental approach was to induce sister chromatid exchange in a ring chromosome to generate a dicentric chromosome with a double chromatid bridge. In the following cell division, the dicentric bridges may break. We analyzed the breakage patterns of 3 different ring-X chromosomes. These chromosomes differ by the amount and quality of heterochromatin they carry as well as their genealogical history. For all 3 chromosomes, breakage occurs preferentially in several hotspots. Surprisingly, we found that the hotspot locations are not conserved between the 3 chromosomes: each displays a unique array of breakage hotspots. The lack of hotspot conservation, along with a lack of response to aphidicolin, suggests that these breakage sites are not entirely analogous to CFS and may reveal new mechanisms of chromosome fragility. Additionally, the frequency of dicentric breakage and the durability of each chromosome's spindle attachment vary significantly between the 3 chromosomes and are correlated with the origin of the centromere and the amount of pericentric heterochromatin. We suggest that different centromere strengths could account for this.

Thermal Distribution Characteristics Related to the Transformer Fine Structure Predicted by CFD-Based Spline Interpolation

Insulation overheating is a major cause of transformer failures, which makes it important to quickly and accurately predict the thermal distribution characteristics related to the transformer’s fine structure. In this work, a hybrid model of computational fluid dynamics (CFD)-based spline interpolation (CFD-SI model) is proposed for the real-time prediction of transformer thermal characteristics. Taking a 750-kV oil-immersed power transformer as an example, a CFD model is established from a complete conjugate heat transfer model and validated simultaneously by 24-h experiments and the empirical formulation. A group of 30 CFD simulations is employed to establish a hybrid model. Validation results of eight randomly selected cases demonstrate that this hybrid model can accurately describe heat flow distributions and predict hot-spot locations with a computational speed of 5570 times faster than the CFD model. Furthermore, a series of 42 predictions of hot spot temperatures under various scenarios are performed using this hybrid model to characterize insulation aging due to overheating, which enabled continuous management over its lifetime.

COMPUTER VISION AIDED HOTSPOT CREATION IN VIRTUAL ENVIRONMENTS

Hotspot creation is one of the most important modules within virtual environments which helps show the navigators of these environments some information about semantic elements within it and facilitate the navigation between the virtual spaces. In this paper, a system for automatic hotspot proposals and creation in virtual environments is proposed. The system uses computer vision modules to automatically propose hotspot locations in addition to identifying and creating these hotspots with candidate labels. Two main modules used in the system are object detection and scene segmentation. The scene segmentation helps give candidate hotspot areas and provides an overall understanding of the semantics of the virtual environment. The object detection module also uses pretrained deep networks for automatic hotspot creation over these objects. The system helps speed up the hotspot creation process and offers a tool for virtual environment users and creators.