Geographic Coordinates Research Articles

Database for the Fault Displacement Hazard Initiative Project

New predictive models for fault displacement and surface rupture hazard analysis developed through the Fault Displacement Hazard Initiative (FDHI) research program require a high-quality empirical database to apply advanced statistical methods and improve hazard estimates. This article discusses the development and contents of the FDHI Project database. We systematically collected, reviewed, and organized fault displacement measurements, surface rupture maps, and supporting information from the scientific literature. A framework was developed and implemented to classify principal and distributed faulting. Best-estimate net displacement amplitudes were calculated from slip component measurements and quality codes were assigned to all net displacement values. The database contains 75 historical, surface-rupturing crustal earthquakes ranging from M 4.9 to 8.0. Thirty-five earthquakes have a strike-slip faulting mechanism, while 25 and 15 events are reverse/reverse-oblique and normal/normal-oblique, respectively. Although most of the earthquakes are from Western North America, Japan, and other active tectonic regions, there are nine reverse faulting events from the stable continental region of Australia. The database contains over 40,000 individual fault displacement measurements for various slip components from roughly 28,000 observation sites. Geographic coordinates are included for all data, and event-specific coordinate systems are provided for each earthquake that transform data into an along-strike dimension. Our new database provides a standardized collection of surface rupture and fault displacement data and metadata that is the result of a comprehensive effort to create a reliable and stable product for the FDHI model development teams and the geoscience community.

Leveraging Copernicus for Ice Block Detection: A Step Towards Safer Arctic Navigation

Background The Arctic is becoming more accessible due to ice melting, posing challenges for navigation due to floating ice blocks. Detecting these ice blocks is crucial for ensuring the safety of vessels navigating these waters. Methods This study introduces a novel method for detecting ice blocks in the Arctic using Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) satellite images from the Sentinel-1 mission of the Copernicus program. Our approach combines state-of-the-art image segmentation and deep learning techniques. We utilize a Convolutional Neural Network (CNN) classifier to accurately locate ice blocks within the images and retrieve their geographic coordinates. The method's performance is validated using precision, recall, and F1-score metrics. Results Our CNN classifier demonstrates robust performance in detecting ice blocks, validated through precision, recall, and F1-score metrics. The practical application of our technology was showcased in the AI-ARC project’s Arctic demo, receiving positive feedback from coast guards across various European countries. The system provides near-real-time alerts about detected ice blocks, allowing for timely route adjustments and reducing collision risks. Conclusions The developed system significantly contributes to Arctic navigation safety by providing accurate and timely detection of ice blocks. This work underscores the transformative potential of AI in environmental monitoring and maritime safety. Future refinements will be based on user feedback and advancements in AI technology, enhancing the system's effectiveness and reliability.

Investigating the footprint of post-domestication dispersal on the diversity of modern European, African and Asian goats

BackgroundGoats were domesticated in the Fertile Crescent about 10,000 years before present (YBP) and subsequently spread across Eurasia and Africa. This dispersal is expected to generate a gradient of declining genetic diversity with increasing distance from the areas of early livestock management. Previous studies have reported the existence of such genetic cline in European goat populations, but they were based on a limited number of microsatellite markers. Here, we have analyzed data generated by the AdaptMap project and other studies. More specifically, we have used the geographic coordinates and estimates of the observed (Ho) and expected (He) heterozygosities of 1077 European, 1187 African and 617 Asian goats belonging to 38, 43 and 22 different breeds, respectively, to find out whether genetic diversity and distance to Ganj Dareh, a Neolithic settlement in western Iran for which evidence of an early management of domestic goats has been obtained, are significantly correlated.ResultsPrincipal component and ADMIXTURE analyses revealed an incomplete regional differentiation of European breeds, but two genetic clusters representing Northern Europe and the British-Irish Isles were remarkably differentiated from the remaining European populations. In African breeds, we observed five main clusters: (1) North Africa, (2) West Africa, (3) East Africa, (4) South Africa, and (5) Madagascar. Regarding Asian breeds, three well differentiated West Asian, South Asian and East Asian groups were observed. For European and Asian goats, no strong evidence of significant correlations between Ho and He and distance to Ganj Dareh was found. In contrast, in African breeds we detected a significant gradient of diversity, which decreased with distance to Ganj Dareh.ConclusionsThe detection of a genetic cline associated with distance to the Ganj Dareh in African but not in European or Asian goat breeds might reflect differences in the post-domestication dispersal process and subsequent migratory movements associated with the management of caprine populations from these three continents.

Status of the transfer state of 137Cs in zooplankton and surface water fish off Fukushima during 2018–2021

Radiocaesium introduced to coastal waters by the accident at the Fukushima Dai-ichi Nuclear Power Station (F1NPS) elevated the radioactivity level in biota. The radionuclide level in zooplankton, concentration of 137Cs radioactivity in surface water fish decreased rapidly, which was the primary food source for planktivorous fish, was recognized as not to be depurated at the same level prior to the accident. To evaluate the possible cause of this phenomenon, zooplankton and surface water fish were collected off Fukushima during 2018–2021, and the presence of radioactive particles was also examined. The concentrations of stable Cs and 137Cs radioactivity were analysed along with aluminium which was an indicator element of Cs that originated from suspended particles which were attached to or ingested by zooplankton. As a result, radioactive particles were often found in coastal zooplankton samples, and stable Cs and 137Cs of this inorganic fraction were identified. After removal of this excess radioactivity, the 137Cs radioactivity in whole-body tissue of zooplankton was derived. However, the level in the soft part of zooplankton during 2018–2020 was still greater than the levels which existed before 2010. Since habitat seawater was understood to not be a substantial source of 137Cs, then 137Cs transfer along the food chain, possibly from phytoplankton or detritus of enhanced radioactivity were suggested as important sources of 137Cs. In addition, enhanced 137Cs radioactivity in zooplankton was considered consequently elevate radioactivity levels in surface-dwelling water fish off Fukushima. Although the radioactivity level was not radiologically significant in relation to seafood safety limit, enhanced 137Cs radioactivity levels in biota was demonstrated in southern waters off Fukushima. In contrast, derived 137Cs/133Cs atom ratios of fish and seawater south of 37°E and west from 142°E indicated that radiocaesium transfer between fish and the environment was in an equilibrium state, showing the environment beyond these geographical coordinates had returned to the pre-accident state.

The distribution of Apis laboriosa revisited: range extensions, biogeographic affinities, and species distribution modelling

IntroductionApis laboriosa, the Himalayan giant honeybee, inhabits the foothills of Himalaya and neighboring mountainous regions. Here we revise its distribution in light of recent reports and discoveries, review the ecozones it inhabits, and reassess its likely distribution through species distribution modeling.MethodsWe revised the range map for A. laboriosa by mapping locality records from various sources: refereed research publications, museum specimens, records with identifiable images of bees in publicly available databases, personal observations of the authors, and photos/videos and their coordinates submitted to the authors by honey-hunters, beekeepers, and extension workers. We then used that map to determine the ecozones in which the species occurs. The geographical coordinates of the data localities were used to estimate the potential suitable areas for the bee with MaxEnt modeling.ResultsOur research filled in several previously identified gaps in the distribution of A. laboriosa: in western Nepal; mountainous regions of Myanmar, northwestern Thailand, and northern Laos; several river valleys in Xizang and Yunnan, China; and northeastern Pakistan. Over most of its range this bee species primarily occupies subtropical broadleaf forests with strong Himalayan affinities. However, in the western part of its range it extends into zones dominated by conifers. The sites where A. laboriosa has been recorded closely match the predicted range of the species. Two variables, mean temperature of the coldest quarter and temperature seasonality, contributed most (76%) to the species distribution model.DiscussionApis laboriosa has a more extensive distribution in the foothills of the Himalaya and neighboring mountainous regions than has been previously recognized. The range now extends from longitude 74.4°–105.9°E, a linear distance of 3300 km, and from latitude 19.2°N–34.8°N. We have documented nesting on tree branches in northern Vietnam. Future research is warranted on its elevational migrations along river valleys, population differentiation, and ecological role as a pollinator in the different ecological zones it inhabits.

The evolution of plant phenomics: global insights, trends, and collaborations (2000-2021).

Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications. The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET. Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges. The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.

The Impact of Polar Vortex Strength on the Longitudinal Structure of the Noontime Mid-Latitude Ionosphere and Thermosphere

Ground-based ionospheric, CHAMP/STAR, and GOCE satellite neutral density ρ observations under deep solar minimum conditions were used to find whether there is a dependence of longitudinal variations on polar vortex strength. Ionospheric stations at fixed-dipole geomagnetic Φ ≈ 38° and geographic φ ≈ 40°N latitudes located in ‘near-pole’ and ‘far-from-pole’ longitudinal sectors were used in the analysis. No significant longitudinal NmF2 (electron concentration in the F2-layer maximum) dependence on the polar vortex strength was revealed. Geomagnetic control was shown to be responsible for the observed longitudinal NmF2 variations. Satellite-observed longitudinal variations in neutral density did not show any visible reaction to the polar vortex strength. However, the impact of sudden stratospheric warming (SSW) on the upper atmosphere is strong enough to change the neutral density longitudinal distribution. The impact of SSW shows a global occurrence and ‘works’ within 3–5 days in geographic coordinates in the vicinity of the SSW peak. Atomic oxygen values retrieved under ‘strong’ and ‘weak’ polar vortex strengths confirm the results obtained on longitudinal variations in NmF2 and ρ. In conclusion, no visible effects related to ‘strong’ or ‘weak’ polar vortex strengths have been revealed in either NmF2 or satellite neutral density longitudinal variations. Alternatively, such effects may be very small and therefore cannot be confirmed experimentally.

Drifting long-lived. To the 50ʰᵗ anniversary of the opening of the “North Pole-22” station

The national drifting station “North Pole-22” (NP-22 or SP-22) had been opened on September 13, 1973 in the Arctic on the big ice island and operated for 3130 days. Now it is the longest operating observational station in the history of Arctic studies of the USSR and the Russian Federation. For eight and a half years, its total drift amounted 17,069 km. During that period a wide complex of scientific programs in oceanography, geophysics, meteorology, glaciology, hydrography, hydrobiology was carried out here. In addition, field tests and implementation of new types of scientific instruments and introduction of new expeditionary polar equipment into the observation practice were performed. For the first time in the practice of the drifting NP/SP stations, oblique sounding of the ionosphere, observations of fine thermohaline structure of waters of the active layer of the Arctic Ocean were carried out. Geographical coordinates of the station drift were determined using Soviet satellite navigation systems; a complex for recording satellite images of the ice cover was deployed; investigations of the arctic ecosystems were made. Specific experiments were conducted aimed at the natural decomposition of heavy hydrocarbons by the ultraviolet solar radiation. The ice airfield of this station received 1,511 flights of aircraft and helicopters, delivering 2,961 tons of expedition cargos. Landings on this airfield were provided by flight teams of the High-Latitude Air Expedition “Sever” of the Arctic and Antarctic Research Institute and the Hydrographic Service of the Northern Fleet. Foreign specialists repeatedly visited the station. For the nine rotations of polar explorers who worked at the NP/SP-22, its personnel amounted to 249 people, many of them several times worked at this drifting station in different years. The work of the NP/SP-22 team was appreciated by the government authorities of the USSR.

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

Assessing the dynamics and trigger factors of drought propagation in the China-Pakistan economic corridor: A three-dimensional perspective

Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region.

The Forbush decrease observed by the SEVAN particle detector network in the 25th solar activity cycle

The temporal variations of cosmic-ray intensity, measured by ground-based detectors at various latitudes, longitudes, and altitudes, are related to the geophysical and solar phenomena. The latter are interplanetary coronal mass ejections and fast solar wind from coronal holes, which cause interplanetary magnetic field (IMF) abrupt variations near Earth. Interacting with the magnetosphere, they cause worldwide sudden decreases (Forbush decreases, FDs) of intensity followed by gradual recovery. The amplitude of the flux depletion depends on the type and energy of the registered particle, which in turn depends on geographical coordinates and the detector's energy threshold and selective power. SEVAN particle detector network with nodes in Europe and Armenia selects three types of particles that demonstrate coherent depletion and recovery and correspond to different energy galactic protons interacting with disturbed magnetospheric plasmas.On November 3–4, 2021, an interplanetary coronal mass injection (ICME) hit the magnetosphere, sparking a strong G3-class geomagnetic storm and auroras as far south as California and New Mexico. All detectors of the SEVAN network have registered an (FD) of ≈5% depletion in a 1-min time series of count rates. Approaching the maximum solar activity cycle, large variations of the particle flux intensity were registered on February 27, March 23, 2023, and March 24, 2024.In this work, we present measurements of these FDs performed on mountain altitudes on Aragats (Armenia), Lomnicky Stit (Slovakia), Mileshovka (Czechia), and at sea level DESY (Hamburg, Germany). We compared FD measurements made by SEVAN detectors and neutron monitors located on Aragats and Lomnicky Stit and made a correlation analysis of FD registration at different locations.

Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height.

Thermal Earth model for the conterminous United States using an interpolative physics-informed graph neural network

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of 18km2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18 \\ km^2$$\\end{document} per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of 0-7km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0-7 \\ km$$\\end{document} at an interval of 1km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 \\ km$$\\end{document}. Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of 6.4∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.4^\\circ C$$\\end{document}, 6.9mW/m2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.9 \\ mW/m^2$$\\end{document} and 0.04W/m-K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.04 \\ W/m-K$$\\end{document}, respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.

A new record of vibrisseaceous fungus from Hakkari (Türkiye)

This study involved specimens of the genus Vibrissea Fr. growing on submerged wood, rubbish or other submerged organic matter in freshwater habitats collected in Şemdinli (Hakkâri) on 01.05.2015. The Vibrissea specimens, characterised by small and delicate fructification organs in the form of cups or bowls, were identified based on morphological data. In this text, Vibrissea decolorans (Saut.) A. Sánchez & Korf is described and illustrated to be a new record for the Ascomycota of Türkiye. The text provides a brief description of the newly recorded genus including collection date, geographical coordinates, macro- and micromorphological characteristics.

Conjugate aurora observations by the Gjøa and Discovery expeditions

Abstract. From 1901 to 1912 – known as the “heroic period” of Arctic and Antarctic exploration – great inroads were made (not only geographic but also scientific) to our knowledge of the continent. At Amundsen's Expedition through the Northwest Passage, measurements of the geomagnetic field and visual auroras were carried out for 19 months at Gjoa Haven (Gjøahavn in Norwegian; geographic coordinates 68°37′10′′ N, 95°53′25′′ W). Scott's Discovery Expedition – at Cape Armitage, McMurdo (coordinates 77.86° S, 166.69° E), Antarctica – carried out the same type of measurements. Their observations were carried out geomagnetically conjugate to Gjoa Haven, with both stations close to 78° magnetic latitude. In addition, measurements were overlapping in time during 1903–1904. However, these two stations are located at different longitudes, so there is a difference in local time between the stations of about 6.5 h. Gjoa Haven and Cape Armitage are conveniently located for separating disturbances in the polar cap regions caused by solar electromagnetic radiations or the solar wind. Auroras were observed during 7 months per year. This gave a unique possibility to compare conjugate characteristics of polar cap auroras. Comparing conjugate geophysical data introduces some difficulties. During the winter season at Gjoa Haven, they had a bright summer in Antarctica, and visa versa. Thus, simultaneous temporal and spatial ionospheric variations can be marked differently. Still, the average diurnal and seasonal variations were similar. The quantity of the auroral data from Cape Armitage was larger because there they had a continuous watch of the sky. The main findings regarding polar cap auroras are the following. Three different auroral forms dominate the polar cap. Low-intensity auroral bands – then called streamers – were the dominating auroral forms morning and afternoon. The number of auroral events in 1903 was nearly twice that in 1902 and 1904. A marked midwinter maximum was observed at both stations. Many displays were observed poleward of the oval. The large fraction was associated with weak magnetic disturbances. Some forms of polar cap aurora have special magnetic signatures and seem to be anti-correlated with Kp. They can be mapped even if they are not seen. According to recent satellite measurements (Newell et al., 2009), they are probably caused by polar rain and/or photoelectrons.

Diversity and Ecological Distribution of Plecoptera of Minas Gerais, Brazil.

The order Plecoptera constitutes a relatively small group of aquatic insects, encompassing 17 extant families and comprising over 4400 valid species. In Brazil, the number of valid extant species is 207, located in two families: Perlidae (149) and Gripopterygidae (58). Despite extensive research on the southeastern region of Brazil, there is a notable scarcity of comprehensive studies consolidating geographical records and species richness of Plecoptera in the state of Minas Gerais. This study seeks to increase and refine our understanding of Plecoptera within Minas Gerais, focusing on its diversity and distribution. The initial phase involved a thorough review of articles documenting Plecoptera species in the state. Subsequently, biological material from the Museum of Entomology at the Federal University of Viçosa collection was meticulously identified, and its geographical records were incorporated. Utilizing this dataset, we compiled an updated list of Plecoptera species documented in Minas Gerais. Geographical coordinates of collection points were then mapped and graphically represented to elucidate the geographic and altitudinal distribution of these species. A total of 42 Plecoptera species were identified within the state of Minas Gerais, adding many occurrence records and documenting the first record of Gripopteryx pinima for the state. Despite these advancements, knowledge gaps persist, particularly in the mesoregions of Triângulo/Alto Paranaíba, Oeste de Minas, Vale do Mucuri, and Campo das Vertentes. This endeavor serves as an initial foundation to stimulate further collections and investments in undersampled areas, fostering future monitoring and conservation initiatives for aquatic environments.

A high-precision oasis dataset for China from remote sensing images

High-resolution oasis maps are imperative for understanding ecological and socio-economic development of arid regions. However, due to the late establishment and relatively niche nature of the oasis discipline, there are no high-precision datasets related to oases in the world to date. To fill this gap, detailed visual interpretation of remote sensing images on Google Earth Professional or Sentinel-2 was conducted in summer 2020, and for the first time, a high-precision dataset of China’s oases (abbreviation HDCO) with a resolution of 1 meter was constructed. HDCO comprises 1,466 oases with a total area of 277,375.56 km2. The kappa coefficient for this dataset validated by the field survey was 0.8686 and the AUC value for the ROC curve was 0.935. In addition, information on the geographic coordinates, climatic conditions, major landforms, and hydrological features of each oasis was added to the attribute table of the dataset. This dataset enables researchers to quantitatively monitor location and area of oases, fosters exploration of the relationship between oases and human under climate change and urbanization.

Hit and run: Forensic vehicle event reconstruction through driver-based cloud data from Progressive's snapshot application

Driving Insurance Applications (DIAs) have emerged as a valuable resource in the ever-evolving digital landscape. Automobile owners are storing extensive data on driving behaviors and patterns. This study pioneers the forensic analysis of Progressive's Snapshot application, focusing on the extraction and potential forensic use of data that remains inaccessible through the mobile application's interface. In our approach we focused on four research questions: How accurate is location and speed data collected by Progressive Snapshot?, What forensically relevant data can we extract from the Progressive Cloud that is unavailable to the user from the mobile application interface?, Can we employ anti-forensics techniques, specifically fake location data, to create false trip details?, Can we reconstruct a hit-and-run scenario from trip event details? To answer these questions, we developed PyShot, a Python-based open-source tool, to extract data from the Progressive cloud. Our tests confirmed Snapshot's accuracy in recording speed and location. Despite efforts to fake the Global Positioning System (GPS) location, the cloud still maintained accurate records. PyShot revealed more detailed driving data, like dangerous maneuvers and distracted driving, compared to the mobile application. This study also explores the forensic reconstruction of hit-and-run incidents, using a mannequin and focusing on Progressive's server data. Analyzing event categories, geographical coordinates, and timestamps provides insights into the capabilities and constraints of this application in forensic investigations. The findings offer valuable insights into the forensic capability of data retained by DIAs, contributing to their potential use in forensic investigations.

Numerical computation of heat transfer, moisture transport and thermal comfort through walls of buildings made of concrete material in the city of Douala, Cameroon: An ab initio investigation

The buildings of the city of Douala in Cameroon have been experiencing degradation for several decades due to the climate characterized by high humidity and oppressive heat. As a result, large grayish or black stains can be observed on these buildings. We sometimes witness the subsidence of the slab of the balconies, the cracking of the walls and the collapse of the buildings worn by the humidity. These damages are generally caused by infiltration and capillary rise. In addition, it has been demonstrated that people living in damp buildings are at risk of illnesses such as asthma and lung infections. Therefore, the novelty of this work is threefold: (i) it proposes for the very first time a numerical study of the transport of humidity and heat through the porous walls of buildings constructed with concrete material, the main construction material in the city of Douala; (ii) It was determined what level of indoor thermal comfort was appropriate for sleeping inside a real three-dimensional G+1 complex residential building constructed with concrete blocks; and (iii) Using the geographical coordinates, and time data, the sun radiation's direction of incidence was assessed throughout the simulation. The computation was performed using Comsol Multiphysics 6.0 software. The distributions of temperature, relative humidity as well as moisture level were presented at various periods. It appears from the results that face to this high humidity, the concrete material retains a large quantity of water for a considerable periods of time, which weakens the steel reinforcement of concrete which is corroded by rust. The computation of thermal comfort in the 3D building showed that the various rooms of the building were not comfortable during the night since temperature inside the building increased progressively due to diffusion of heat. In addition, the numerical solutions indicated that the energy stored within the walls diffused from the external walls to the internal walls during the night. It was also demonstrated that the walls of the building were warmer than the windows, doors and the roof at the computational times, which simply revealed a greater storage capacity of heat in the concrete blocks material. The findings highlighted that the temperature decreased rapidly in a thickness of 0.06 m of the concrete block during the nine days and this decrease was attenuated in the second part of the thickness of the concrete block (0.14 m).

A dataset of the distribution and characteristics of debris flows along the National Highway 318 in the Sino-Nepalese transport corridor (2022)

Due to the unique geographical and geological conditions in the Sino-Nepal transportation corridor, there is a lack of reliable and comprehensive data on debris flow distribution data in the region. This dataset is compiled based on early identification of debris flows and field surveys, integrating Digital Elevation Models (DEM) and geological maps. It provides a catalog and detailed feature information of debris flows within the corridor along the National Highway 318 between China and Nepal from 1990 to 2022. Precipitation and temperature parameters are derived from 30 years (1980–2009) of interpolated data from national meteorological stations. Fault distribution information is sourced from the 1:50,000 digital geological maps published by the China Geological Survey. Terrain information is obtained from NASA's Shuttle Radar Topography Mission (SRTM) data, which provides a global DEM at a resolution of 12.5 m. Within the geographical coordinates of 28.0°–29.4°N and 85.1°–88.9°E, a total of 255 debris flow records have been extracted, covering an area of 1660.73 km². We primarily employed hydrological analysis methods in ArcGIS for the extraction of debris flow basins. Considering the limitations in DEM accuracy, we made necessary manual corrections with reference to Google Earth imagery. The dataset comprises three files: a vector file depicting the main distribution range of debris flows along the National Highway 318 in the Sino-Nepalese transport corridor; a vector file containing the catalog of debris flows; and a document detailing the parameters and characteristics of the debris flows.The dataset has undergone on-site field surveys and confirmation to ensure its reliability. Therefore, it can serve as a foundational dataset applicable to debris flow studies in the region.