Potential Anomalies Research Articles

Generation of the Quasi‐Biweekly Oscillation in the Surface Potential Vorticity Over the Tibetan Plateau During Boreal Summer: A Case Study of 2014

AbstractThe atmospheric circulation around the Tibetan Plateau (TP) exhibits a substantial 10–20‐day quasi–biweekly oscillation (QBWO), profoundly impacting weather and climate locally and remotely. Understanding the factors influencing the generation of QBWO over the TP (QBWOTP) and its physical mechanism is crucial. This study has investigated the influence of multi–timescale and land–atmosphere interactions on the generation of the QBWOTP in surface potential vorticity (SPV), a valuable tool for characterizing the mechanical and thermal variabilities in mountain forcing, based on a 2014 case study. Results indicate that in the free atmosphere, the summer monsoon onset over the Bay of Bengal induces a northward shift in the westerly jet toward the TP, manifested as an increase in low‐frequency zonal winds. This shift facilitates the propagation of wave trains, leading to atmospheric quasi–biweekly potential temperature anomalies (θa) over the TP through a multi‐timescale interaction. Additionally, the TP's surface thermal forcing and arrival of wave trains trigger anomalous upward motion and increase cloud cover. The resultant decrease in net short‐wave radiations and increase in net long‐wave radiations contribute to variations in surface potential temperature (θs) over the TP. As θa and θs evolve, the difference between them enlarges, resulting in the generation of the SPV QBWOTP. Given the relationship between the QBWOTP and downstream rainfall, this study could provide novel insights into understanding and predicting downstream rainfall QBWO.

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Utilizing machine learning algorithm and Carrera unified formulation for vibration analysis of nanocomposite-enhanced bridge structures rested on an innovative elastic foundation: verification of the results via nondestructive testing

This paper presents an advanced vibration analysis of Al2O3 nanocomposite-reinforced concrete bridge structures resting on an innovative elastic foundation using the Carrera Unified Formulation (CUF). The primary objective is to investigate the dynamic response of these bridges under various loading conditions, accounting for the reinforcing effects of Al2O3 nanocomposites within the concrete matrix. The formulation incorporates a novel elastic foundation model designed to more accurately simulate realistic boundary conditions and soil-structure interaction. The accuracy and reliability of the CUF-based vibration analysis are further validated using nondestructive testing (NDT) techniques, which enable the detection of potential damage and anomalies in the bridge structures. Moreover, a machine learning (ML) algorithm is employed to predict the vibrational characteristics, facilitating an efficient comparison with the results obtained from CUF and NDT. The combination of theoretical modeling, experimental verification, and ML predictions highlights the robustness of the proposed method. The results demonstrate the effectiveness of using Al2O3 nanocomposites to enhance the mechanical properties of bridge structures, improving their vibrational performance, stability, and longevity. This study provides a comprehensive framework for future applications in bridge engineering, combining high-fidelity numerical methods with state-of-the-art testing and computational techniques.

Smart detection of subsurface anomalies: Concept, validation and applications

Naturally formed and engineering-induced subsurface anomalies (e.g., cavities and sinkholes) jeopardize infrastructure safety and hinder urban sustainability. Here we report a smart sensing method to detect subsurface anomalies based on the physical characteristics extracted from the effective signals scattered and reflected directly from these anomalies. Potential anomalies at submeter scales can be interpreted based on a sharp variation of anomaly score relative to the background anomaly score, showing the advantage of overcoming subjective uncertainty and biases involved in the traditional geophysical methods. We find that the use of scattered and reflected waves in an intermediate frequency range is well-suited for sensing deep subsurface infrastructure at high resolutions required for civil structures. We also demonstrate that a fast and reliable detection of subsurface anomalies relies solely on the physical characteristics of seismic data in two field cases, promoting geologic hazard forecast and decision-making effectiveness.

Microplastic and heavy metal contamination in sediments of the high-altitude Nundkol Lake of northwestern Himalayas-Kashmir

This study investigates heavy metal and microplastic contamination in the sediment of Nundkol Lake, a high-altitude glacial lake in the northwestern Himalayas. Sediment analysis revealed heavy metal concentrations significantly exceeding background levels, with average concentrations of cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn), and chromium (Cr) recorded at 40.991 ppm, 54.434 ppm, 87,843.59 ppm, 1544.566 ppm, 83.288 ppm, 16.312 ppm, 104.571 ppm, and 106.514 ppm, respectively. Notably, the highest levels of Cu, Fe, Mn, and Pb were observed in sample GL2, indicating potential local geochemical anomalies. The trace elements were quantified using a Graphite Furnace - Atomic Absorption Spectrophotometer (Perkin Elmer-PinAAcle 900AA) for high precision.Microplastic analysis revealed an average abundance of 76±12.08 items/kg dry weight, predominantly fibers. The microplastics were mostly red (55.26 %), blue (36.84 %), orange (5.26 %), and green (2.63 %). Raman spectroscopy identified Polyamides (PA) as the predominant type (86.5 %), with the remainder being Polyethylene (PE) and Polypropylene (PP). The results suggest tourism, fishing, and trekking activities as major contributors to microplastic presence. This study uniquely combines the assessment of heavy metal and microplastic contamination in a high-altitude lake, providing a comprehensive view of pollutant dynamics. The findings highlight the need for tailored mitigation strategies and comprehensive monitoring programs to protect this fragile region. Applications include informing environmental policy, guiding pollution mitigation efforts, and promoting sustainable tourism practices. This study offers valuable insights into the environmental health of high-altitude lakes and underscores the need for efforts to mitigate anthropogenic impacts.

A spatiotemporal co-occurrence pattern mining algorithm based on ship trajectory data

Finding the potential spatiotemporal co-occurrence behavior patterns of large groups of ships while sailing is a challenging problem of great importance in many real-world applications. Through spatiotemporal data mining of ship trajectory data, route rules, navigation behavior, and potential anomalies can be mined, providing important support for maritime management, navigation safety, and emergency response. With the analysis and mining of ship trajectory data in some hotspot sea areas, this paper introduced a ship spatiotemporal co-occurrence pattern mining algorithm based on association rules. Based on the research of data model and the judgment criterion of spatio-temporal co-occurrence law, such concepts as candidate set, frequency set, and instance set are introduced together with the key procedure of algorithm, including pruning and pasting of candidate sets, screening of instance sets, definition of association reasoning, and association rule mining. Subsequently, the process of implementing the spatiotemporal co-occurrence pattern mining algorithm is devised. In the end, the algorithm is verified by taking the automatic identification system data of ships in hotspot sea areas as the source data. The proposed algorithm can find several ship combinations with spatiotemporal co-occurrence regularity in these hotspot sea areas, and the association rules on the co-occurrence of several ships. The performance of the proposed algorithms is illustrated on a real-world ship trajectory database and made a detailed comparative analysis. The results are very promising in terms of computational time. The experimental results show that our algorithm can effectively identify the motion patterns and behavior characteristics of ships, which provides an important reference and support for Marine traffic management, ship safety and Marine environment protection. The research results of this paper are of great significance for improving the efficiency and safety of maritime traffic, and also provide new ideas and methods for further research in related fields.

Determination of the Normative Value of Fetal Left Brachiocephalic Vein Diameter Using Ultrasound in a Tertiary Care Hospital in Eastern India.

Fetal ultrasound plays a vital role in prenatal care by offering key insights into fetal growth and facilitating the early identification of congenital abnormalities. Among various anatomical structures, the fetal left brachiocephalic vein (FLBCV) plays a significant role in fetal circulation, yet normative data for FLBCV dimensions are limited, particularly in diverse populations. This study aims to establish normative values for FLBCV diameter in fetuses between 18 and 23 weeks of gestation in a tertiary care hospital in Eastern India. Such data are essential for accurate clinical assessment and early diagnosis of potential congenital anomalies. This descriptive study was conducted at the College of Medicine and Sagore Dutta Hospital, Kolkata, India, from January to December 2022. The study included 287 singleton pregnancies between 18 and 23 weeks of gestation, excluding cases with maternal or fetal conditions that could affect the measurements. LBCV diameter was measured using transabdominal ultrasound, and the normative values were established using percentile charts. Correlation analyses were performed to assess the relationship between FLBCV diameter and various fetal and maternal parameters. Linear regression analysis was done to identify significant predictors of FLBCV diameter. FLBCV diameter showed a consistent increase with gestational age, ranging from a median of 1.87 mm at 18 completed weeks to 2.27 mm at 22 completed weeks. The 95th percentile values exhibited more pronounced growth, particularly between 18 and 20 weeks. Significant positive correlations were observed between FLBCV diameter and fetal biometric parameters such as biparietal diameter, head circumference, abdominal circumference, and femur length. Head circumference emerged as the most significant predictor (p=0.007) of FLBCV diameter in the linear regression model. This study provides essential normative data on FLBCV diameter for the Indian population, offering a valuable reference for clinicians in prenatal diagnostics. The incorporation of FLBCV measurement in basic fetal echocardiography, which is a part of the second-trimester fetal anomaly scan, could enhance the detection of congenital anomalies, contributing to improved prenatal care.

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms

The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.

Active Security Surveillance and Object Detection

Active security surveillance and object detection have become crucial components in modern security systems, driven by advancements in computer vision and machine learning. These systems are designed to enhance real-time monitoring and threat detection across various environments, such as public spaces, transportation hubs, and critical infrastructure. Active security surveillance systems continuously scan and analyze video feeds or sensor data to identify potential security threats, unauthorized activities, or anomalies. They employ advanced algorithms for motion detection, facial recognition, and behavioural analysis, allowing for the rapid identification of suspicious activities or individuals. Object detection, a key element of these systems, involves the identification and classification of objects within a given scene. This is typically achieved through deep learning techniques, such as convolutional neural networks (CNNs), which can accurately detect and categorize objects like vehicles, weapons, or unattended bags. The integration of object detection with active surveillance enables more precise and automated responses to potential threats, reducing reliance on human operators and minimizing the risk of oversight. The adoption of active surveillance and object detection technologies has led to significant improvements in security management, offering enhanced situational awareness and faster response times. However, these technologies also raise concerns regarding privacy, data security, and ethical implications, particularly in their deployment in public and private spaces. Addressing these concerns requires careful consideration of data governance, transparency, and regulatory compliance to balance security needs with individual rights. As technology continues to evolve, ongoing research and development are essential to improving the accuracy, efficiency, and ethical deployment of active security surveillance and object detection systems.

Determination of borders of structure related to deep minerals based on potential field data in Ba Na area

This paper presents the research results of applying method two-dimensional energy filtering method in the «live window» to separate the high-frequency component of aeromagnetic data in order to eliminate residual flight line errors with amplitude ranges from -5÷5 nT and correct the data source before processing and analysis of data, identify potential field residual anomalies at depths h = 470 m, 1100 m, 1700 m, 2300 m. Application of the horizontal gradient of the vertical derivative of the potential field at various depths to determine the position of shallow fault systems, and determine the boundaries of local heterogeneous blocks related to the prospect of deep-hidden minerals at depths h = 470 m, 1100 m, 1700 m, 2300 m in Ba Na area. The results of the analysis of residual magnetic anomalies have built the distribution diagram of the shallow fault system buried by sediments and zoning the mineral prospect according to the potential anomaly in the study area. Fault system includes faults in sub-latitude, sub-longitude, and Northeast-Southwest directions, in which the sub-latitude fault system is dominant. The area of mineral prospect in Ba Na area to a depth h = 2300 m is determined by the maximum value of horizontal gradient vertical derivative of the closed loop residual gravity anomaly field with amplitude >0.007 mGal/m2, combined with residual gravity anomaly value, and location of mineral points have been revealed on the surface, prospective areas are mainly concentrated in the west, northwest, and southeast of the study area. The results of the analysis provided Geophysicists and Geologists with detailed research plans to evaluate specific types of minerals in the Ba Na area.

The molecular mechanisms that underlie IGHMBP2-related diseases.

Immunoglobulin Mu-binding protein 2 (IGHMBP2) pathogenic variants result in the fatal, neurodegenerative disease spinal muscular atrophy with respiratory distress type 1 (SMARD1) and the milder, Charcot-Marie-Tooth (CMT) type 2S (CMT2S) neuropathy. More than 20 years after the link between IGHMBP2 and SMARD1 was revealed, and 10 years after the discovery of the association between IGHMBP2 and CMT2S, the pathogenic mechanism of these diseases is still not well defined. The discovery that IGHMBP2 functions as an RNA/DNA helicase was an important step, but it did not reveal the pathogenic mechanism. Helicases are enzymes that use ATP hydrolysis to catalyse the separation of nucleic acid strands. They are involved in numerous cellular processes, including DNA repair and transcription; RNA splicing, transport, editing and degradation; ribosome biogenesis; translation; telomere maintenance; and homologous recombination. IGHMBP2 appears to be a multifunctional factor involved in several cellular processes that regulate gene expression. It is difficult to determine which processes, when dysregulated, lead to pathology. Here, we summarise our current knowledge of the clinical presentation of IGHMBP2-related diseases. We also overview the available models, including yeast, mice and cells, which are used to study the function of IGHMBP2 and the pathogenesis of the related diseases. Further, we discuss the structure of the IGHMBP2 protein and its postulated roles in cellular functioning. Finally, we present potential anomalies that may result in the neurodegeneration observed in IGHMBP2-related disease and highlight the most prominent ones.

Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing.

Accurate assessment of fragment abundance within a genome is crucial in clinical genomics applications such as the analysis of copy number variation (CNV). However, this task is often hindered by biased coverage in regions with varying guanine-cytosine (GC) content. These biases are particularly exacerbated in hybridization capture sequencing due to GC effects on probe hybridization and polymerase chain reaction (PCR) amplification efficiency. Such GC content-associated variations can exert a negative impact on the fidelity of CNV calling within hybridization capture panels. In this report, we present panelGC, a novel metric, to quantify and monitor GC biases in hybridization capture sequencing data. We establish the efficacy of panelGC, demonstrating its proficiency in identifying and flagging potential procedural anomalies, even in situations where instrument and experimental monitoring data may not be readily accessible. Validation using real-world datasets demonstrates that panelGC enhances the quality control and reliability of hybridization capture panel sequencing.

The Impact of Chromosomal Mosaicisms on Prenatal Diagnosis and Genetic Counseling-A Narrative Review.

Genetic disorders represent a high-impact diagnosis for both patients and their families. Prenatal screening methods and, when recommended, genetic testing allow parents to make informed decisions about the course a pregnancy is going to take. Although offering certainty about the potential evolution and prognosis of the pregnancy, and then the newborn, is usually not possible, genetic counseling can offer valuable insights into genetic disorders. Chromosomal mosaicisms are genetic anomalies that affect only some cell lines in either the fetus or the placenta or both. They can affect autosomal or heterosomal chromosomes, and they can be either numerical or structural. The prognosis seems to be more severe if the genetic alterations are accompanied by malformations visible in ultrasounds. Several genetic techniques can be used to diagnose certain mosaicisms, depending on their nature. A novel approach in prenatal care is non-invasive prenatal screening (NIPS), also known as non-invasive prenatal testing (NIPT), which, although it does not always have diagnostic value, can provide valuable information about potential genetic anomalies, especially numerical, with high sensitivity (Se).

Shifts from surface density compensation to projected warming, freshening and stronger stratification in the subpolar North Atlantic

The hydrography and stratification of the subpolar North Atlantic is highly variable, with convection activating and deactivating across parts of the Labrador and Irminger seas. Likely consequential for the Atlantic Meridional Overturning Circulation (AMOC), this variability is examined in an eddy-rich ocean model hindcast spanning 1958–2021 and in 1950–2050 simulations with four climate models, spanning differences in ocean resolution (eddy-rich or eddy-permitting), code and implementation. Stratification of the Labrador and Irminger seas is quantified with the Potential Energy Anomaly (PEA) in the upper 1000 m of the water column. Monthly PEA anomalies are evaluated alongside corresponding anomalies of sea surface temperature, salinity, and density. For 30-year windows, moving correlations between PEA and surface properties are obtained over the 100-year simulations to characterize the evolving relationships. As climate change progresses, stratification in three of the four models is increasingly associated with variable surface salinity, in both regions. Lagrangian analyses of surface flow pathways in the decades preceding 1990 and 2040 are undertaken for one of the models in which surface salinity grows in influence. The subpolar presence of low-salinity Arctic waters and high-salinity subtropical Atlantic waters are found to increase and decrease respectively by 2040. Furthermore, in three of the four models, surface density compensation associated with correlation of surface temperature and salinity anomalies is progressively replaced by combined surface warming and freshening, lowering surface density, and strengthening stratification. The extent of these model-dependent changes and processes are of consequence for the projected fate of the AMOC by the mid twenty-first century.

3D reconstruction of semantic-rich digital twins for ACMV monitoring and anomaly detection via scan-to-BIM and time-series data integration

Current research in air-conditioning and mechanical ventilation (ACMV) operation focuses on isolated sub-processes and analytical models. Digital twins, as digital replicas of assets, processes, or systems in the built environment, enable facilities manager (FM) to gain insights into the physical features of space, equipment performance, and energy efficiency. This study presents the 3D reconstruction of semantic-rich digital twins, which encompasses conditional and machine learning-enabled monitoring with 3D geometric models, for ACMV modeling and operation. The proposed framework involves a hybrid rule-based and data-driven approach to forecast the performance of indoor environment and identify potential anomalies throughout ACMV operation. Following this, a scan-to-BIM process is undertaken, with the aid of Simultaneous Localization and Mapping algorithms, to semi-automatically generate the as-built geometric models. Lastly, semantic enrichment of BIM is performed by incorporating time-series data from the rule-based and data-driven approach with 3D geometric models. The proposed approach supports the reconstruction of content-aware and semantic-rich digital twins, which utilize sensor-derived time-series data and 3D geometric models, to conduct advanced analysis for intelligent ACMV operation towards energy efficiency and occupant comfort.

Remote Detection of Leakages from a Compromised Buried Water Supply Pipe through Geophysical Measurements

The continuous drastic loss of stored water at a private household presumed to be caused by leakage(s) was subjected to geophysical investigations with a view to delineating the source(s) of the leakage(s). The Spontaneous Potential (SP) and Electrical Resistivity (ER) methods were deployed for the study. Measurements were taken along four (4) traverses at two intervals – firstly when the water supply valve from the overhead storage tank was closed and secondly when it was opened. The Total Field array was adopted for SP measurements with constant station separation of 0.5 m. ER measurements were taken via the Wenner array with electrode spacing (a) varied with an interval of 0.5 m from 0.5 m to 1.5 m. SP data were presented as profiles and maps while ER data were presented as 3-D resistivity depth stacks. SP values varied from -100 mV to 350 mV during the two measurement periods. ER values also varied from 100 ohm.m to 1200 ohm.m during the closed valve period and 100 ohm.m to 900 ohm.m during the opened valve period. During the closed valve period, pipeline routes were interpreted as manifesting oval-shaped high potential anomalies with central linear trend on the SP map; and as linearly-trending high resistivity intrusion within low resistivities on resistivity maps. The disparities between the results of closed valve and opened valve measurements were pointers to leakage-induced inhomogeneity in the subsurface. The diminished high potential SP anomaly and the anomalously low resistivity specks within linearly-trending high resistivity values were regarded as the source of leakage.

Prediction of long-term changes of weak diurnal stratification in shallow lakes using artificial neural networks

ABSTRACT Thermal stratification plays a key role in lakes' ecosystems. In contrast to deep lakes, the thermal structure of shallow polymictic lakes is characterized by a weak stratification with an apparent diurnal cycle. Long-term changes in stratification are governed by climate change and anthropogenic effects such as water level regulation. We developed a simple and robust model system consisting of an energy balance model to estimate depth-averaged water temperatures and an artificial neural network (ANN) model to predict stratification with high temporal resolution. One novelty of our approach is that instead of directly estimating water temperatures at different depths, we simulated the potential energy anomaly index, the indicator of stratification's strength. The ANN-based model's performance was assessed against a physical-based one-dimensional model (General Ocean Turbulence Model) by modeling a 40-year-long period from 1981 to 2020. The new model accurately predicts a shallow lake's weak stratification and its diurnal cycle. Besides, the model proved reliable on longer time scales, capturing the effect of climate change, anthropogenic water level regulation, and their synergistic interaction on the change of stratification's intensity and duration.

3D Gravity and magnetic inversion modelling for geothermal assessment and temperature modelling in the central eastern desert and Red Sea, Egypt

The Central Eastern Desert and Red Sea region have emerged as a significant area of interest for geothermal energy exploration, owing to their unique geological characteristics and active tectonic activity. This research aims to enhance our understanding of the region's geothermal potential through a comprehensive analysis of gravity and magnetic data. By utilizing a 3D gravity inversion model, a detailed examination of subsurface structures and density variations was conducted. Similarly, a 3D magnetic inversion model was employed to investigate subsurface magnetic properties. Integration result from the Pygimli library ensured robustness and accuracy in the inversion results. Furthermore, a temperature model was developed using the WINTERC-G model and inversion techniques, shedding light on the thermal structure and potential anomalies in the study area. The analysis of the Bouguer gravity map, 3D gravity inversion model, and magnetic data inversion yielded significant findings. The Red Sea exhibited higher gravity values compared to the onshore Eastern Desert, attributed to the presence of a thinner and denser oceanic crust as opposed to the less dense continental crust in the Eastern Desert. The 3D gravity inversion model revealed distinct variations in density, particularly high-density zones near the surface of the Red Sea, indicating underlying geological structures and processes. Conversely, density gradually decreased with depth along the onshore line, potentially influenced by a higher concentration of crustal fractures. The magnetic data inversion technique provided additional insights, highlighting areas with demagnetized materials, indicative of elevated temperatures. These findings were consistent with the correlation between high-density areas and low magnetic susceptibility values, reinforcing the proposition of increased heat transfer from the Red Sea. Comparative analysis of temperature profiles further confirmed the presence of elevated temperatures in promising zones, emphasizing the geothermal potential associated with heat transfer from the Red Sea.This research contributes to the understanding of the geothermal resources in the Central Eastern Desert and Red Sea region. The results from gravity and magnetic data inversions, combined with temperature profiles, provide valuable information for future geothermal exploration and utilization efforts. The findings underscore the importance of geothermal energy in achieving sustainability and contribute to the global discourse on renewable energy sources.

HCN: Hybrid Capsule Network for Fetal Plane Classification in Ultrasound Images

ABSTRACTClassifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture‐based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network‐based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings.

Fitting z-curves to estimate the size of the UESD file drawer and the replicability of published findings

The unexpected event during survey design (UESD) established itself as a viable causal inference design across multiple social science disciplines in the past few years. The distribution of UESD test statistics has not yet been scrutinized for potential anomalies to the same degree as those from other causal inference methods, such as DiD, RDD, or IV. In this article, I leverage recent advances in meta-analytical methodology to estimate the replicability of statistically significant UESD findings and quantify the size of the file drawer of non-significant findings. Precisely, I aggregate 1095 ITT coefficients and standard errors from UESD studies published between 2019 and 2023 to fit their z-curve and estimate their observed discovery rate, expected discovery rate, and expected replication rate. While most statistically significant UESD findings are predicted to be replicable, the distribution of z-values also indicates publication bias toward marginally significant findings and a large file drawer of non-significant findings. The innovative z-curve methodology, which has not seen much (or any) use in political science yet, provides promising new insights beyond established tools for the assessment of publication bias, such as funnel plots or caliper tests, and can readily be applied to entire subfields of quantitative political science.