Variability Of Field Research Articles

Establishment of Local Diagnostic Reference Levels for Head CT Imaging in the Madina Region, Saudi Arabia

Background: Computed Tomography (CT) is crucial in medical diagnosis, particularly for head examinations. Diagnostic Reference Levels (DRLs) are pivotal in balancing diagnostic efficacy with radiation safety. International organizations such as the International Atomic Energy Agency (IAEA) and the International Commission on Radiological Protection (ICRP) provide guidelines for establishing DRLs, emphasizing their importance in optimizing radiation doses. Aim: This study aimed to establish and standardize DRLs for head CT imaging in institutional and regional settings, emphasizing the need for tailored reference levels specific to each region’s practices and standards. Method: Data collection included patient demographics, imaging parameters, and radiation dose indices, namely volume-weighted CT dose index (CTDIvol) and dose-length product (DLP). Statistical analyses were conducted to determine the median and 3rd quartile values for establishing DRLs. The results were compared with national and international benchmarks to assess variations in radiation doses across regions. Results: Demographic profiles detailed gender distributions and ages across hospitals. Imaging parameters exhibited variability in tube voltage, milliampere-seconds (mAs), pitch, scan length, and field of view (FOV). For CTDIvol, the 3rd quartile value was 65.8 mGy (range: 24.8–85.9 mGy), and for DLP, it was 1230.95 mGy·cm (range: 382.3–1189.0 mGy·cm). These values were slightly higher than the national DRLs for Saudi Arabia in 2021 and other international benchmarks, underscoring the need for further optimization and alignment of protocols. Conclusion: Optimizing and standardizing DRLs for head CT imaging is crucial for effectively managing radiation doses while ensuring diagnostic accuracy. Comparison with national and international benchmarks highlighted the importance of tailoring reference levels to regional practices and standards, ensuring patient safety without compromising diagnostic efficacy.

Variability of hydrophysical fields under the influence of tides in the river plume frontal zone of the Onega Bay of the White Sea according to a study conducted in June 2023

The results of a series of experiments conducted in June 2023 in the Onega Bay of the White Sea are presented. The study included quasi-temporal in situ and satellite observations in the vicinity of the river plume frontal area formed by the waters of the Onega River. The main objectives were to define the characteristics and dynamics of the area under the influence of tides, as well as to assess the causes of short-term spatial and temporal variability in the characteristics of thermohaline fields. The results show that tides have a significant effect on the structure of waters near the Lyamitsky shore. Here, a large contribution is made by fast-flowing water oscillations associated with short-term internal waves, which frequently occur near the frontal interface. The study demonstrates that the use of high-resolution optical and radar satellite imagery allows a more efficient analysis of frontal and wave dynamics, which is of great importance for understanding the peculiarities of local hydrodynamic processes in the water areas of tidal seas.

Paleomagnetic inclinations and relative paleointensity variations during the upper Aptian to middle Albian in Sergipe-Alagoas Basin, South Atlantic Ocean (NE Brazil)

Summary During the Jurassic and Early Cretaceous, Earth's magnetic field experienced a high frequency of polarity reversals. This hyperactivity period is followed by a ∼38 Myr prolonged period of stable normal polarity from the Aptian until the Santonian, known as the Cretaceous Normal Superchron (CNS; chron C34n, from 121.4 to 83.65 Ma). However, the CNS might have been characterized by more variability than previously thought, but the current databaseis uneven in its spatial and temporal distribution. In this study, we integrate paleomagnetic and biostratigraphic data from the SER-03 sedimentary core, drilled onshore in the Sergipe-Alagoas Basin, NE Brazilian margin, South Atlantic Ocean. The 183-meter cored interval spans 5.2 Myr (114.7–109.5 Ma), including the Aptian–Albian transition. It records the evolution from restricted marine settings to the complete opening of the South Atlantic Ocean. Magnetic remanence and rock magnetic parameters, such as magnetic susceptibility, anhysteretic remanent magnetization, and isothermal remanent magnetization, were measured. Rock magnetic analyses indicated that primary magnetizations were preserved throughout the interval, with detrital magnetite identified as the primary magnetic carrier, predominantly in the pseudo-single domain and low-coercivity states. Normalizations by χ, ARM15mT, and IRM15mT were used to determine relative paleointensity curves and were compared to the curve resulting from the pseudo-Thellier method. The SER-03 relative paleointensity record shows marked changes in both intensity and inclination. Inclination instability results are comparable with those found in volcanic rocks from surrounding the region. The high-resolution SER-03 magnetic record revealed that the field variability was higher than expected during the CNS, but remaining substantially lower than during other periods, such as thelate Jurassic. The new SER-03can serve as a dating and correlation tool for coeval records.

Management zone delineation: Utilizing multiple data sources to minimize soil spatial variability in commercial potato fields under Prince Edward Island pedoclimatic conditions

Management zone delineation: Utilizing multiple data sources to minimize soil spatial variability in commercial potato fields under Prince Edward Island pedoclimatic conditions

Assessing the Effectiveness of Satellite and UAV-Based Remote Sensing for Delineating Alfalfa Management Zones Under Heterogeneous Rootzone Soil Salinity

Site-specific application of agricultural inputs is crucial for optimizing resource utilization in alfalfa (Medicago sativa L.) production and addressing challenges such as soil salinity. The main objective of this study was to assess the effectiveness of PlanetScope and UAV-based NDVI imagery for delineating alfalfa management zones under heterogeneous rootzone soil salinity. The research was conducted in the alfalfa field located in Imperial Valley, CA. The extent of rootzone soil salinity was assessed using Electromagnetic induction (EMI) technology and deep soil sampling. Reference management zones were then defined using the soil salinity (ECe) map derived from apparent electrical conductivity (ECa) data. Additionally, a time series of NDVI images from PlanetScope imagery and an NDVI image captured using an unmanned aerial vehicle were used to delineate remote sensing-based management zones. Laboratory analysis of disturbed soil samples collected at various depths provided soil physicochemical property data. Soil salinity of the samples ranged from 2.2 to 13.4 dS m−1 with a moderate level of variability (CV = 37.7%). ECe-based management zones accounted for approximately 83% of the field's variability and exhibited substantial differentiation among delineated zones concerning diverse soil properties, including ECa, ECe, gravimetric water content, Mg2+, boron, Ca2+, Na+, and Cl−. Notably, NDVI images effectively captured field variability on par with ECe-based zoning. Moreover, NDVI images recommended the same optimal number of zones (i.e., three) to address the field's variability, aligning with the ECe-based zoning approach. Our findings highlight that heterogeneity of soil salinity in the root zone primarily impacts the variability of alfalfa NDVI early in the growing season. Consequently, this early stage emerges as the most opportune timeframe for NDVI-based zoning for rapid assessment of rootzone soil salinity concerns.

Spatial and Temporal Bayesian Hierarchical Model Over Large Domains With Application to Holocene Sea Surface Temperature Reconstruction in the Equatorial Pacific

AbstractWe present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation.

Multiple scale method integrated physics-informed neural networks for reconstructing transient natural convection

This study employs physics-informed neural networks (PINNs) to reconstruct multiple flow fields in a transient natural convection system solely based on instantaneous temperature data at an arbitrary moment. Transient convection problems present reconstruction challenges due to the temporal variability of fields across different flow phases. In general, large reconstruction errors are observed during the incipient phase, while the quasi-steady phase exhibits relatively smaller errors, reduced by a factor of 2–4. We hypothesize that reconstruction errors vary across different flow phases due to the changing solution space of a PINN, inferred from the temporal gradients of the fields. Furthermore, we find that reconstruction errors tend to accumulate in regions where the spatial gradients are smaller than the order of 10−6, likely due to the vanishing gradient phenomenon. In convection phenomena, field variations often manifest across multiple scales in space. However, PINN-based reconstruction tends to preserve larger-scale variations, while smaller-scale variations become less pronounced due to the vanishing gradient problem. To mitigate the errors associated with vanishing gradients, we introduce a multi-scale approach that determines scaling constants for the PINN inputs and reformulates inputs across multiple scales. This approach improves the maximum and mean errors by 72.2% and 6.4%, respectively. Our research provides insight into the behavior of PINNs when applied to transient convection problems with large solution space and field variations across multiple scales.

Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature

Abstract. Emulators of Earth system models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows us to emulate gridded monthly mean precipitation fields using gridded monthly mean temperature fields as forcing. The emulator is designed as an extension of the Modular Earth System Model Emulator (MESMER) framework, and its core relies on the concepts of generalised linear models (GLMs). Precipitation at each (land) grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a gamma distribution with a logarithmic link function. The second factor is the residual variability in the precipitation field, which is assumed to be independent of temperature but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent principal components and subsequently approximated and sampled using a kernel density estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single-ensemble member across scenarios and evaluate the emulator performance using simulations with historical and Shared Socioeconomic Pathways (SSP5-8.5) forcing. We show that the framework captures grid-point-specific precipitation characteristics, such as variability, trend, and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with those of ESMs. The framework is also able to capture compound hot–dry and cold–wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit coherent monthly temperature and precipitation time series is a major step towards a computationally efficient representation of impact-relevant variables of the climate system.

The importance of boundary evolution for solar-wind modelling

The solar wind is a continual outflow of plasma and magnetic field from the Sun’s upper atmosphere—the corona—that expands to fills the solar system. Variability in the near-Earth solar-wind conditions can produce adverse space weather that impacts ground- and space-based technologies. Consequently, numerical fluid models of the solar wind are used to forecast conditions a few days ahead. The solar-wind inner-boundary conditions are supplied by models of the corona that are, in turn, constrained by observations of the photospheric magnetic field. While solar eruptions—coronal mass ejections (CMEs)—are treated as time-dependent structures, a single coronal “snapshot” is typically used to determine the ambient solar-wind for a complete model run. Thus, all available time-history information from previous coronal-model solutions is discarded and the solar wind is treated as a steady-state flow, unchanging in the rotating frame of the Sun. In this study, we use 1 year of daily-updated coronal-model solutions to comprehensively compare steady-state solar-wind modelling with a time-dependent method. We demonstrate, for the first time, how the SS approach can fundamentally misrepresent the accuracy of coronal models. We also attribute three key problems with current space-weather forecasting directly to the steady-state approach: (1) the seemingly paradoxical result that forecasts based on observations from 3-days previous are more accurate than forecasts based on the most recent observations; (2) high inconsistency, with forecasts for a given day jumping significantly as new observations become available, changing CME propagation times by up to 17 h; and (3) insufficient variability in the heliospheric magnetic field, which controls solar energetic particle propagation to Earth. The time-dependent approach is shown to alleviate all three issues. It provides a consistent, physical solution which more accurately represents the information present in the coronal models. By incorporating the time history in the solar wind along the Sun-Earth line, the time-dependent approach will provide improvements to forecasting CME propagation to Earth.

The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform)

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics.

Enhancing Irrigation Management: Unsupervised Machine Learning coupled with Geophysical and Multispectral Data for Informed Decision-Making in Rice Production

Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI-PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p<0.01) with multispectral data (-0.25 to -0.45) for fields 1a, 1b and 4a. On the other hand, there was a direct correlation (p<0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a. For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions.

A Systematic Analysis of Star Cluster Disruption by Tidal Shocks. II. Predicting Star Cluster Dissolution Rates from a Time-series Analysis of Their Tidal Histories

Most of the dynamical mass loss from star clusters is thought to be caused by the time variability of the tidal field (“tidal shocks”). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalization problem. Here we address these issues by investigating how star cluster evolution depends on the statistical properties of its tidal history. We run a large suite of direct N-body simulations of clusters with tidal histories generated from power spectra of a given slope and with different normalizations, which determine the timescales and amplitudes of the shocks, respectively. At fixed normalization (i.e., the same median tidal field strength), the dissolution timescale is nearly independent of the power spectrum slope. However, the dispersion in dissolution timescales, obtained by repeating simulations for different realizations of statistically identical tidal histories, increases with the power spectrum slope. This result means that clusters experiencing high-frequency shocks have more similar mass-loss histories than clusters experiencing low-frequency shocks. The density–mass relationship of the simulated clusters follows a power law with slope between 1.08 and 1.45, except for the lowest normalizations (for which clusters effectively evolve in a static tidal field). Our findings suggest that star cluster evolution can be described statistically from a time-series analysis of its tidal history, which is an important simplification for describing the evolution of the star cluster population during galaxy formation and evolution.

Analysis of Enceladus’s Time-variable Space Environment to Magnetically Sound its Interior

We provide a comprehensive study of Enceladus’s time-variable magnetic field environment and identify in measurements of the Cassini spacecraft signatures that appear to be consistent with induced fields from the moon’s interior. Therefore, we first analyze the background field Enceladus is exposed to within 21 flybys and 50 crossings of the moon’s orbit by the Cassini spacecraft. Considering magnetic field variability due to Enceladus’s eccentric orbit, Saturn’s planetary period oscillations, and local time effects within the magnetospheric current sheet, we construct predictive, time-variable background fields near Enceladus with a correlation coefficient of 0.75 and larger compared to the measured background fields. Subsequently, we build a geophysically based electrical conductivity model of Enceladus’s ocean from the equation of state for saline water and mixing laws for a porous core permeated by water. Using this conductivity model and the derived time-variable fields, we calculate expected induced fields. For close flybys, we identify within mostly plume-dominated magnetic field perturbations of 10–30 nT much smaller perturbations of 1–3 nT, which could be consistent with induction. The flybys over Enceladus’s north pole are best suited for induction studies, and the associated measurements suggest that a conductivity of the ocean with 1–3 S m–1 is not sufficient to produce an adequate induction response, but they support a highly conductive, porous core of 20–30 S m–1 and/or a more conductive ocean. Our study also provides strategies for future magnetic sounding of Enceladus.

Space Weather Forecasts of Ground Level Space Weather in the UK: Evaluating Performance and Limitations

AbstractGeomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.

Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery

Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99–100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.

Variability in strawberry tunnels impacts fruit quality and limits melatonin effects.

Fluctuations in environmental conditions within fields and crop plant performance can greatly affect production and quality standards. These factors are particularly relevant for producers, who require sustained optimal production to profit from small margins. Fluctuations might be exacerbated at the end of the crop season, where neither of the aforementioned factors are optimal. In the present integrated study, we assess strawberries' nutritional quality and the impact of harvest timing, tunnel conditions and inter-individual variability in a Mediterranean production tunnel divided into blocks, where two harvests were performed 3 weeks apart. In addition, the effects of sprayed melatonin at the end of productive season were also evaluated. End-season harvesting negatively impacted fruit hydration, antioxidant capacity and ripening-related hormones in strawberry fruits. Additionally, tunnel distribution influenced fruit nutritional quality, with light radiation being the main variable factor disturbing antioxidant contents. Nutrients exhibited high inter-individual plant variability, accounting for 20% variation, and were strongly correlated with fruit hydration and ripening-related phytohormones. Finally, melatonin applications affected neither fruit production, nor nutritional parameters, for which the effects were masked by the intrinsic strawberry variability. Overall, the results underline the limitations of this type of application for field implementation. Fruit quality variation in strawberry fields is explained by environmental and inter-individual variability. Likewise, the implementation of regulatory molecules such as melatonin in field applications relies on crop homogeneity and might have limited applicability in heterogeneous productive systems. Consequently, identifying and reducing microclimate variability in productive fields is paramount for advancing agricultural practices to uphold unwavering standards on fruit quality. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder

Previous studies indicate that transcranial direct current stimulation (tDCS) is a promising emerging treatment option for autism spectrum disorder (ASD) and its efficacy could be augmented using concurrent training. However, no intrastimulation social cognition training for ASD has been developed so far. The objective of this two-armed, double-blind, randomized, sham-controlled clinical trial is to investigate the effects of tDCS combined with a newly developed intrastimulation social cognition training on adolescents with ASD. Twenty-two male adolescents with ASD were randomly assigned to receive 10 sessions of either anodal or sham tDCS at F3/right supraorbital region together with online intrastimulation training comprising basic and complex emotion recognition tasks. Using baseline magnetic resonance imaging data, individual electric field distributions were simulated, and brain activation patterns of the training tasks were analyzed. Additionally, questionnaires were administered at baseline and following the intervention. Compared to sham tDCS, anodal tDCS significantly improved dynamic emotion recognition over the course of the sessions. This task also showed the highest activations in face processing regions. Moreover, the improvement was associated with electric field density at the medial prefrontal cortex and social awareness in exploratory analyses. Both groups showed high tolerability and acceptability of tDCS, and significant improvement in overall ASD symptoms. Taken together, multisession tDCS improved dynamic emotion recognition in adolescents with ASD using a task that activates brain regions associated with the social brain network. The variability in the electric field might diminish tDCS effects and future studies should investigate individualized approaches.

Paleomagnetic Field Variability and Revised Chronostratigraphy of Bering Sea (IODP Expedition 323) Deep‐Sea Sediments During MIS 6–7 (130–144 ka)

AbstractWe have developed replicate paleomagnetic secular variation (PSV) records for MIS 6–7 (130–244 ka) from IODP Ex. 3 23 Sites 1,343, 1,344, and 1,345 (Bering Sea). We can correlate the PSV at all three sites and identify 90 inclination features and 64 declination features. We have developed relative paleointensity records for all three sites by normalizing the demagnetized natural remanence to magnetic susceptibility. Paleointensity highs/lows can be tied to the PISO‐1500 global oxygen‐isotope‐dated paleointensity record. This provides a significant increase in chronostratigraphic resolution for these sites. Only one excursion is recorded in the MIS 6–7 sediments of the Bering Sea—the Iceland Basin Excursion (∼196 ± 3 ka). Replicate records of the inclinations and declinations both flip quickly to reversed polarity directions, stay there for several hundred years and then flip quickly back to normal polarity directions. However, the inclination flips occur without significant declinations changes and visa versa. A statistical PSV study was carried out by averaging the data in 3 ky and 9 ky windows. There is a distinctive bimodal pattern to the angular dispersions with most time spent with low angular dispersion values (∼10°–15°), but there is an interval of more than 30 ky (185–220 ka) with angular dispersions averaging two to three times higher amplitudes (∼25°–35°). This interval also has low paleointensities and the Iceland Basin Excursion. This same, coupled pattern of high angular dispersion, low paleointensity, and excursions is noted synchronously in the central North Atlantic Ocean and may indicate a global pattern to the geomagnetic field.

Utilization of Open Polygon Method for Land Mapping in Mojoroto Kediri with Geospatial Approach

Land mapping is an important research activity to determine the location of points on the earth's surface, describing the physical condition of parts of the surface that resemble the actual condition. The objective of this process is to gain a comprehensive understanding of the land to be built and used through land area measurements, topographic maps, and land volume analysis at various altitudes. In this mapping, the open polygon method is used for calculations which are conventional methods using data point values as data centers to represent the area of influence. During this mapping, there are three main points used with the open polygon method. A total of 26 ground contours were recorded, and 4 road contours recorded. During the mapping process, corrections are required on the open polygon calculations to ensure the accuracy of the results. This correction involves adjusting the data to take into account field variability and inaccuracies of measuring instruments, so that the resulting data can be reliable for further analysis and correct decision-making. The land that has been painted can be used for various fields such as construction, and mining. Land mapping allows partial surface physical state imaging, classification of land, and can be used to plan, build, and maintain infrastructure such as highways and bridges. The result of this practice obtained data of the maximum height of 90,0945 masl and minimum 88,76 masl with the average ground height 90,05059 masl, while the average highway height is at 88,86175 masl.

Near‐surface wind variability in Prydz Bay and Amery Ice Shelf region, East Antarctica: A four‐decade SOM analysis

AbstractNear‐surface wind fields in the Prydz Bay and Amery Ice Shelf region of East Antarctica play a crucial role in the formation and variability of Antarctic Bottom Water, a cold, dense water mass that sinks and spreads across the deep ocean basins influencing ocean circulation and modulating earth's climate system. This study investigates the primary modes of variability of these wind fields using the self‐organizing map (SOM) method and data from the latest version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalyses (ERA5), spanning four decades from 1979 to 2020. While the wind field climatology, characterized by small seasonal variation, is dominated by katabatic and large‐scale forcing, the spatial patterns of the primary variability modes are mainly influenced by synoptic system activities. The overall trend in annual wind speed anomalies is positive across the study region, with the exception of the southwestern part and central Prydz Bay. However, significant trends are observed in only two out of nine SOM nodes (nodes 4 and 9), which collectively explain less than 30% of the averaged trends over the region. The interannual variability in the seasonal occurrences of certain nodes is linked to several well‐known climate modes, including the El Niño–Southern Oscillation, the Southern Annular Mode and Zonal Wavenumber 3. Our results provide a reference for forecasting the occurrence frequency of specific patterns, which could help mitigate the impact of extreme wind events through improved forecasting.