Hotspot Locations Research Articles

Improved cryogenic stability by thermal insulation between forced-flow gas-cooled REBCO conductors in fusion magnets

A key design feature is presented to improve the cryogenic stability of forced-flow gas-cooled HTS conductors for fusion magnets. In order to realize the compact and economic fusion system, a variety of conductors are under development with stacked REBCO tapes so that the high-field magnets can operate at 20–30 K with the internal flow of helium gas. Thermal-hydraulic analysis is typically based on 1-D models under a prescribed thermal load along the conductors. In this study, the effect of thermal interaction between adjacent conductors in a winding pack is rigorously considered for the analysis. In a double pancake with single cooling loop, it is evidently verified that the conductor temperature may rise to a peak at inner layers, seriously affecting current sharing and thermal stability. The analytical results are useful in predicting the temperature and location of hot spot in terms of a dimensionless parameter, NTU. In theory, the gas-cooled magnet can be thermally stable by either infinitely increasing or infinitesimally decreasing the thermal interaction between conductors. In practice, however, the thermal insulation to minimize the contact between inbound and outbound flows in a double pancake is proposed as an effective way to improve the cryogenic stability.

Seasonal Distribution of Gravity Waves Near the Stratopause in 2019–2022

AbstractThe cloud imaging and particle size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere satellite provides images of gravity waves (GWs) near the stratopause and lowermost mesosphere (altitudes of 50–55 km). GW identification is based on Rayleigh Albedo Anomaly (RAA) variances, which are derived from GW‐induced fluctuations in Rayleigh scattering at 265 nm. Based on 3 years of CIPS RAA variance data from 2019 to 2022, we report for the first time the seasonal distribution of GWs entering the mesosphere with high (7.5 km) horizontal resolution on a near‐global scale. Seasonally averaged GW variances clearly show spatial and temporal patterns of GW activity, mainly due to the seasonal variation of primary GW sources such as convection, the polar vortices and flow over mountains. Measurements of stratospheric GWs derived from Atmospheric InfraRed Sounder (AIRS) observations of 4.3 μm brightness temperature perturbations within the same 3‐year time range are compared to the CIPS results. The comparisons show that locations of GW hotspots are similar in the CIPS and AIRS observations. Variability in GW variances and the monthly changes in background zonal wind suggest a strong GW‐wind correlation. This study demonstrates the utility of the CIPS GW variance data set for statistical investigations of GWs in the lowermost mesosphere, as well as provides a reference for location/time selection for GW case studies.

Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.

Predicting the hotspot location and motor threshold prior to transcranial magnetic stimulation using electric field modelling

Objective. To investigate whether the motor threshold (MT) and the location of the motor hotspot in transcranial magnetic stimulation (TMS) can be predicted with computational models of the induced electric field. Approach. Individualized computational models were constructed from structural magnetic resonance images of ten healthy participants, and the induced electric fields were determined with the finite element method. The models were used to optimize the location and direction of the TMS coil on the scalp to produce the largest electric field at a predetermined cortical target location. The models were also used to predict how the MT changes as the magnetic coil is moved to various locations over the scalp. To validate the model predictions, the motor evoked potentials were measured from the first dorsal interosseous (FDI) muscle with TMS in the ten participants. Both computational and experimental methods were preregistered prior to the experiments. Main results. Computationally optimized hotspot locations were nearly as accurate as those obtained using manual hotspot search procedures. The mean Euclidean distance between the predicted and the measured hotspot locations was approximately 1.3 cm with a 0.8 cm bias towards the anterior direction. Exploratory analyses showed that the bias could be removed by changing the cortical target location that was used for the prediction. The results also indicated a statistically significant relationship (p < 0.001) between the calculated electric field and the MT measured at several locations on the scalp. Significance. The results show that the individual TMS hotspot can be located using computational analysis without stimulating the subject or patient even once. Adapting computational modelling would save time and effort in research and clinical use of TMS.

Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques

Urban traffic accidents pose significant challenges to the sustainability of transportation infrastructure not only in Vietnam but also all over the world. To decrease the frequency of accidents, it is crucial to analyze accident data to determine the relationship between accidents and causes, especially for serious accidents. This study suggests an integrated approach using Geographic Information System (GIS) and Data Mining methods to investigate the features of urban traffic accidents in Hanoi, Vietnam aiming to solve these challenges and enhance the safety and efficiency of urban transportation. Firstly, the dataset was segmented into homogenous clusters using the two-step cluster method. Secondly, the correlation between causes and traffic accidents was examined on the overall dataset as well as on each cluster using the association rule mining (ARM) technique. Finally, the location of accident groups and high-frequency sites of accidents (hotspots) were determined by using GIS techniques. As a result, a five-cluster model was created, which corresponded to five common accident groupings in Hanoi. Moreover, the results of the study also identified the types of accidents, the main causes, the time, and the surrounding areas corresponding to each accident group. In detail, cluster 5 depicted accidents on streets, provincial, and national roads caused by motorbikes making up the highest percentage within the groups, accounting for 29.2%. Speeding and driving in the wrong lane in the afternoon and at night were the main causes in this cluster (Cf ≥ 0.9 and Lt ≥ 1.22). Next, cluster 2 had the second-highest proportion. Cluster 2 presented accidents between a truck/car and a motorbike on national and provincial roads, accounting for 27.8%. Cluster 1 presented accidents between a truck/car and a motorbike on local streets, accounting for 22%. Cluster 3 illustrated accidents between two motorbikes on the country lanes, accounting for 12.3%. Finally, cluster 4 depicted single-vehicle motorbike crashes, with the lowest rate of 8.8%. More importantly, this study also recommended using repeatability criteria for the same type of accidents or causes to determine the location of hotspots. Also, suggestions for improving traffic infrastructure sustainability were proposed. To our knowledge, this is the first time in which these three methods are applied simultaneously for analyzing traffic accidents.

Evaluation of elite soybean genotypes for yield and rust resistance in Southern Zone of India

The genotype x environment interaction (GEI) is an important aspect of plant breeding program as it decides the general and specific adaptation of a variety to the varied environments. Higher magnitude of GEI were noted in soybean with significant influence on yield. In the present study fourteen genotypes were evaluated at six locations in Southern Zone of India (Adilabad, Bangalore, Bidar, Dharwad, Kasbe Digraj and Pune) during Kharif 2017, 2018 and 2019. Across locations, DSb 34 recorded 21 % and 18 % increased yield over zonal check (DSb 21) and rust susceptible check (JS 335), respectively. DSb 34 also exhibited highly resistant reaction to rust disease at hotspot location and can be used as donor in soybean disease resistance improvement.

TOWARDS LARGE-SCALE BUILDING ATTRIBUTE MAPPING USING CROWDSOURCED IMAGES: SCENE TEXT RECOGNITION ON FLICKR AND PROBLEMS TO BE SOLVED

Abstract. Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.

Analisis Daerah Rawan Kecelakaan Lalu Lintas pada Jalan Tjilik Riwut Kota Kasongan Kabupaten Katingan Provisi Kalimantan Tengah

Katingan Regency is one of the areas known to be prone to traffic accidents in Central Kalimantan Province. As a result of these accidents, there are many casualties. Therefore, it is necessary to conduct research to determine the level of accidents that occur on the Tjilik Riwut Kasongan road section. The purpose of this study is to analyze accident data on the Tjilik Riwut Kasongan road, analyze the location of traffic accident hotspots on the road, analyze the main factors causing accidents and provide recommendations to relevant agencies to reduce the accident rate on the road. The results of the accident analysis on the Tjilik Riwut Kasongan road carried out for 5 years (2018-2022) accidents amounted to 51 events. Based on the Accident Equivalent Number (AEK) method and the Upper Control Limit (UCL) method, it shows that segment 1 (Km 0+000 - Km 5+000) is a traffic accident prone area. The main cause of accidents is due to human factors and the influence of road conditions. The alternative countermeasures are patching potholes, cleaning drainage, leveling asphalt patching, adding road medians, repainting faded signs, installing traffic signs, and socializing traffic safety and legal discipline in traffic.

Improved thermal hydraulic network modelling and error analysis in disc‐type transformer windings

AbstractTemperature distribution in transformer windings is a crucial factor that influences the safe operation, and lifespan of an insulating system in an oil‐immersed power transformer. Thermal hydraulic network modelling (THNM) has been the subject of numerous studies because of its lower computation cost as compared to a computational fluid dynamics (CFD) simulation. This study proposes improvements to THNM, with primary focus on non‐uniformity of temperatures in the entry and exit vertical oil ducts, and the feasibility of using a piecewise fitting formula for Nusselt number (Nu) of convection heat transfer coefficient in transformer windings. The deviation of hot spot temperature was 0.3°C, and the location of hot spot for CFD and THNM was 34th disc and 35th disc, respectively. The calculation time of CFD and THNM was about 34 h and 35 s under the same computer hardware situations. This provides an acceptable THNM method for the rapid prediction of transformer hot spot temperature and location at low costs for digital twin.

Evaluation of indigenous and exotic soybean accessions for yield, resistance to frog-eye leaf spot and yellow mosaic virus diseases

AbstractSoybean is a major source of vegetable oil and protein worldwide. Globally, India is among the top five producers where soybean is a major oilseed grown under diverse agro-climatic conditions by small and marginal farmers. The present study aims to identify soybean varieties with higher yield levels, resistance to pestdiseases and adaptability to climatic fluctuations. One hundred and twenty-five (125) indigenous and exotic soybean germplasm accessions and five checks were evaluated and characterized for eight agro-morphological traits at five testing locations and also screened for frog-eye leaf spot (FLS) and yellow mosaic virus (YMV) diseases under hot-spot locations during the rainy season. A wide range of variability was observed among accessions for days to 50% flowering (39–59), plant height (41–111 cm), number of nodes/plant (10–30), pod clusters/plant (14–39), number of pods/plant (40–102), days to maturity (96–115), grain yield/plant (4.89–16.54 g) and 100-seed weight (6.02–13.72 g). Among various traits, 100-seed weight (0.45), number of pods/plant (0.60) and number of pod clusters/plant (0.38) were found to be major yield-contributing traits as they exhibited highly significant correlation with grain yield/plant. Principal components PCI and PCII with eigen value &gt;1 accounted for 42.66 and 27.08% of the total variation, respectively. Accessions G24 (EC 393222) from Taiwan and G40 (IMP-1) from the USA belonging to cluster IV were found promising for multiple yield traits and JS 20–38 from cluster III for earliness as per cluster analysis. GGE biplot average environment coordination (AEC) view revealed that the accessions viz., G11 (EC 333872), G2 (EC 251506) and G47 (TNAU-S-55) were the best performing stable genotypes in terms of grain yield/plant across locations. Twelve accessions had a high level of resistance against both FLS and YMV diseases under natural hot-spot conditions which can be utilized as promising donors in the soybean breeding programme.

Thermal Imaging Efficacy in Predicting Trim Loss from Cull Sow Carcasses

Abattoir-level digital image measurement and assessments recorded during the harvest process could contribute to better estimates of the economic value of each animal and could be used as animal health and well-being assessment tools to provide information/feedback for commercial pork producers. This study evaluated the association between live animal infrared images and nonedible trim loss from cull sows. Eighty sow carcasses selected from cull sows normally presented at a Midwest cull sow abattoir were evaluated. A thermal image and corresponding trim losses were collected from each sow.Regions of interest, referred to as hotspots, were isolated from the thermal images of individual sows. The relationships between the thermal hotspots, trim loss weight, and trim loss percentage from each carcass were evaluated using Pearson correlation coefficients, stepwise regression, and prediction equations. The most predictive model included covariates for the number of hotspots, live identification category, hotspot location, and explanatory continuous covariates for pixels with elevated temperature. Live animal defect classification and hotspot location categories significantly contributed to variation in percent trim. This suggests that visually identifying defects like abscesses, poor condition, mammary infection, or a combination on live cull sows is associated with trim losses from their carcass.

Spatial, temporal, and demographic variability in patagonian toothfish (Dissostichus eleginoides) spawning from twenty-five years of fishery data at South Georgia

Patagonian toothfish (Dissostichus eleginoides) are a commercially important species that support a longline fishery at the subantarctic island of South Georgia (CCAMLR Subarea 48.3). Understanding the life history of Patagonian toothfish is key to the successful management and sustainability of this fishery. Using catch data from the past 25-years, 1997 to 2021, we provide an updated assessment of the spatial, temporal, and demographic variability of Patagonian toothfish spawning at South Georgia. Our findings confirm that spawning occurs in the vicinity of the shelf-break of South Georgia, with significant spawning hotspots detected at Shag Rocks, midway along both the northern and southern shelf breaks, and at the eastern end of the island. The location of these hotspots were consistent over the 25-years examined. Based on data between 1997 and 2007, when fishing occurred routinely all around the island and at Shag Rocks, 40% of detected hotspot locations overlapped with regions where Benthic Closed Areas (BCAs) were established in 2008. With this, we can estimate that approximately 40% of spawning hotspots are located within, and already protected by, the existing network of BCAs. There was evidence that the timing of toothfish spawning exhibited bimodality at South Georgia with a peak in April being observed in the first two years of the time series. This peak fell outside the seasonally restricted fishing season for many subsequent years. These findings are discussed in the context of both historic, current, and possible future regulatory changes to this longline fishery.

Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands

AbstractDrained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gases fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood, but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N2O) and carbon dioxide (CO2) fluxes were more evenly distributed across space than methane (CH4). In the corn system, hot moments of CH4 flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N2O fluxes in the annual corn agroecosystem but were less informative for perennial alfalfa N2O fluxes or CH4 fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO2, CH4, and N2O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.

Multi-environment screening of Linum germplasm collection for dissecting the potential of bud fly (Dasyneura lini Barnes) resistance and assembling a reference set for efficient utilization in genetic improvement

Linseed or flaxseed (Linum usitatissimum L.) is one of the most ancient industrial crops domesticated for oil and natural fiber. Bud fly (Dasyneura lini Barnes) susceptibility is serious biotic stress affecting its production both quantitatively and qualitatively. It has economic significance particularly in Asian countries, causing substantial crop damage leading to significant yield penalty and deterioration of seed quality. In the present study, a large collection of 2598 linseed germplasm accessions from the Indian National Genebank were screened for bud fly infestation at two natural hot-spot locations in India. The maximum bud fly incidence across all four environments ranged from 47.93% to 90.47%. Based on average bud fly infestation over the environments, a reference set comprising 195 accessions including 46 resistant, 94 moderately resistant and 55 moderately susceptible/susceptible germplasm was developed. Further, a selected subset of 26 accessions having a common reaction against linseed bud fly at both locations was validated for resistance over multi-location-year environments (four environments over two years) through GGE biplot analysis. The GGE biplot revealed that two accessions, G22 (EC0993391) and G23 (IC0633096) were highly resistant (<10% bud fly damage) and most stable over the tested environments against bud fly infestation. Additionally, two accessions namely G15 (IC0498795) and G24 (EC0099001) were considered resistant as they showed consistent performance and low bud fly infestation in comparison to the resistant check (Neela). Moreover, these accessions had a great buffering capacity to perceive environmental signals for developing adaptive capacities in a wide range of environments. The identified germplasm accessions may serve as potential donors for accelerating the resistance breeding programme to impart a long-lasting, broad-spectrum resistance against linseed bud fly.

Mapping of Important HIV/AIDS Hotspots in Palu City Following Natural Disaster

This study aims to obtain a map of the distribution of HIV and AIDS Key Population Hotspots based on the criteria for MSM, Waria and FSW after the natural disaster in 2018. The remapping method uses a Geographic Information System (GIS) application. The data processing technique uses the waterfall model scheme, namely, completing the application in a structured or sequential manner, where a stage must be completed first before going to the next stage. No Ethical consideration is needed. The shift in key population hotspot data from 2017and to 2022 data and then Natural Disasters (Earthquake, Liquefaction and Tsunami) in 2018 had an impact on geographical location shifts with conditions in 2017, 4 Districts became key population hotspot locations with 29 hotspots while in 2022 14 key population hotspots were found consisting of 5 sub-districts, so there was an additional 1 sub-district which became a geographically shifting hotspot. Features of activity hubs centered on major population centers, such as hotels, boarding houses, salons, cafés, clubs, city parks, and malls in 2017; in 2022, other amenities like laundry will be included. Male Fellow (MSM), Female Sex Workers (FSW), and Transgender are the features of the major population hotspots since 2017, however in 2022 Natural Disasters (Earthquake, Liquefaction, and Tsunami) in 2018 did not suffer a shift.

Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)

AbstractThe spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions.

Crime mapping and hot spot analysis using geospatial data in Jalingo metropolis of Taraba State, Nigeria

Crime mapping and hot spot analysis are topical and significant research across the fields of criminology, data digging, city planning and law enforcement for crime control. This study seeks to identify high crime areas which require an officer presence and predicting the possible response demand to increase the efficiency of response officer patrols. Crime type and patterns exist on a spatial level; these patterns and type can be grouped geographically by physical location, and analyzed contextually based on the region in which crime occurs. The study aimed at analyzing crime hotspots using geospatial data in Jalingo metropolis by identifying the different kinds of crime in the study area, determine the spatio-temporal variation of crime occurrences in the study area and determine the high crime density area (hotspot).This study proposed a method to identifying level crime, localize crime hotspots, identify relationship between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. Several types of crime were analyzed in this dataset, including burglary, bribery, forgery ,murder ,rape ,theft drug abuse, assault, and robbery analysis, several interesting findings were drawn about crime in Jalingo metropolis, including: Crime mapping and hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Jalingo metropolis as a whole, individual months in which certain hotspots behaved anomalously, and a strong relationship between crime hotspot locations. This type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates in Jalingo metropolis. Policing approaches like patrolling and response to crime incidents can be more effective if proper crime mapping and crime hot spot analysis data are available for policing is used to make decisions on crime control in the study area. Predictive policing in crime management use information such as historical crime data to predict crime patterns and response demand. Historical crime data is used to identify high crime areas through kernel density estimation. It is also used to anticipate the levels of response demand. Both of these factors are used to determine how to direct police patrols. This study revealed how kernel density estimation is used for crime mapping and hot spot.

Location: root architecture structures rhizosphere microbial associations.

Root architectural phenotypes are promising targets for crop breeding, but root architectural effects on microbial associations in agricultural fields are not well understood. Architecture determines the location of microbial associations within root systems, which, when integrated with soil vertical gradients, determines the functions and the metabolic capability of rhizosphere microbial communities. We argue that variation in root architecture in crops has important implications for root exudation, microbial recruitment and function, and the decomposition and fate of root tissues and exudates. Recent research has shown that the root microbiome changes along root axes and among root classes, that root tips have a unique microbiome, and that root exudates change within the root system depending on soil physicochemical conditions. Although fresh exudates are produced in larger amounts in root tips, the rhizosphere of mature root segments also plays a role in influencing soil vertical gradients. We argue that more research is needed to understand specific root phenotypes that structure microbial associations and discuss candidate root phenotypes that may determine the location of microbial hotspots within root systems with relevance to agricultural systems.

A novel approach to building digital twin transformers by combining virtual-real sensing: An example of degree of polymerization distribution

Digital twin (DT) in the power industry faces challenges due to complex structures and limited distributed sensors for power equipment. In this work, a novel approach combining virtual-real sensing (VRS) was used to build DT for transformers, taking the degree of polymerization (DP) distribution as an example. Temperature and moisture distributions were calculated, and a dynamic deduction model for DP distribution was established. The visualization of DP distribution was achieved through surface-rebuild. Results showed a 3.5% error in temperature distribution compared to monitoring data. Moisture distribution was concentrated in the cardboard and bottom solid insulation, reaching 3.2% in the 48th year. DP distribution pattern aligned with Cigre’s report, with the lowest value at the hot spot location of the LV windings, deduced to be 252.15 in the 48th year. This study provides a data base for realizing transformers DT and is a key step towards building a vital DT transformer.

Down the Penrose stairs, or how selection for fewer recombination hotspots maintains their existence.

In many species, meiotic recombination events tend to occur in narrow intervals of the genome, known as hotspots. In humans and mice, double strand break (DSB) hotspot locations are determined by the DNA-binding specificity of the zinc finger array of the PRDM9 protein, which is rapidly evolving at residues in contact with DNA. Previous models explained this rapid evolution in terms of the need to restore PRDM9 binding sites lost to gene conversion over time, under the assumption that more PRDM9 binding always leads to more DSBs. This assumption, however, does not align with current evidence. Recent experimental work indicates that PRDM9 binding on both homologs facilitates DSB repair, and that the absence of sufficient symmetric binding disrupts meiosis. We therefore consider an alternative hypothesis: that rapid PRDM9 evolution is driven by the need to restore symmetric binding because of its role in coupling DSB formation and efficient repair. To this end, we model the evolution of PRDM9 from first principles: from its binding dynamics to the population genetic processes that govern the evolution of the zinc finger array and its binding sites. We show that the loss of a small number of strong binding sites leads to the use of a greater number of weaker ones, resulting in a sharp reduction in symmetric binding and favoring new PRDM9 alleles that restore the use of a smaller set of strong binding sites. This decrease, in turn, drives rapid PRDM9 evolutionary turnover. Our results therefore suggest that the advantage of new PRDM9 alleles is in limiting the number of binding sites used effectively, rather than in increasing net PRDM9 binding. By extension, our model suggests that the evolutionary advantage of hotspots may have been to increase the efficiency of DSB repair and/or homolog pairing.