Dynamic Source Research Articles

A Detailed Understanding of Slow Self‐Arresting Rupture

AbstractRecent numerical simulation studies suggest the existence of a seismic type that is distinct from regular earthquakes—the slow self‐arresting rupture (SSAR). Unlike regular earthquakes that propagate dynamically following the initiation, The SSARs automatically arrest within the nucleation zone without interference. Additionally, numerical simulations indicate that SSARs exhibit a significantly lower energy release compared to regular earthquakes, while also exhibiting a relatively long source duration. Given these distinctive properties, comprehending the source processes of SSARs assumes great strategic importance. However, our current understanding of SSARs, particularly regarding their response to different frictional conditions and their correlation with natural phenomena, remains limited in scope. To further explore the intricacies of SSARs, we employ a three‐dimensional fully dynamic source model to simulate SSARs under various slip‐weakening frictional conditions. The findings indicate that SSARs occur in frictional environments characterized by large normalized critical slip distances, with the seismic source process being primarily influenced by this parameter. Apart from displaying significantly smaller average slip and stress drop, which are two to three orders of magnitude lower than those of regular earthquakes of comparable magnitude, SSARs also showcase a decrease in duration, seismic moment, slip rate, and stress drop as the normalized critical slip distance increases. The moment‐duration scaling law of SSARs exhibits a linear pattern. Moreover, the observation of slow earthquakes offers further implications for the presence of SSARs, indicating their potential association with a wider range of intricate seismic phenomena.

Implementation of 802.11ax and cell-free massive MIMO scenario for 6G wireless network analysis extending OMNeT++ simulator

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

Extended Comparison and Performance Analysis for Mobile Ad-Hoc Networks Routing Protocols Based on Different Traffic Load Patterns and Performance Metrics

A mobile ad-hoc network (MANET) is a network of mobile nodes that dynamically form a transitory network lacking any existence of infrastructure and any form of centralized management. Nodes in ad hoc networks are powered by batteries with a limited lifespan and communicate in a restricted bandwidth. The unpredictable environment of a MANET may run into a major concern in the routing mechanism, therefore the need for a routing protocol with robust performance is still one of the key challenges in MANET deployment. In this work, a comparative comparison and extensive simulation analysis have been carried out for three major routing protocols: destination sequenced distance vector (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). Protocol evaluation has been extended by considering several simulation arrangements, different classes of traffic load patterns and diverse performance metrics. Based on packet rate change, node quantity and node speed, simulation scenarios were generated. Protocols were investigated against energy consumption, throughput, lost packets, routing load and packet delivery fraction for three types of traffic load patterns regular, irregular and joint traffic. DSR and AODV protocols proved to be more reliable when joint traffic was implemented when node speed and packets variations are considered. DSDV protocol verifies outstanding response over other protocols in terms of energy consumption when either regular or irregular traffic is applied. The simulation results for DSR protocol have verified the superiority over other protocols in 9 simulation scenarios when diverse metrics are considered. DSDV showed optimal performance in 7 cases, especially at low packet rates and in networks with minimum number of nodes. Similarly, AODV protocol showed outstanding performance in 6 scenarios, when higher packet rates and node mobility are considered.

Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors

To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations that transforms complex, high-dimensional inputs into lower-dimensional representations (i.e., manifolds) in support of multiple categorization behaviors. Here, we tested this hypothesis by analyzing these transformations reflected in dynamic MEG source activity while individual participants actively categorized the same stimuli according to different tasks: face expression, face gender, pedestrian gender, and vehicle type. Results reveal three transformation stages guided by the pre-frontal cortex. At stage 1 (high-dimensional, 50-120ms), occipital sources represent both task-relevant and task-irrelevant stimulus features; task-relevant features advance into higher ventral/dorsal regions, whereas task-irrelevant features halt at the occipital-temporal junction. At stage 2 (121-150ms), stimulus feature representations reduce to lower-dimensional manifolds, which then transform into the task-relevant features underlying categorization behavior over stage 3 (161-350ms). Our findings shed light on how the brain's network mechanisms transform high-dimensional inputs into specific feature manifolds that support multiple categorization behaviors.

Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems

This study investigates the development and enhancement of adaptive location-based routing protocols within dynamic wireless sensor networks (WSNs) in urban cyber-physical systems, recommending the implementation of the study’s innovative Urban Adaptive Location-based Routing Protocol (UALRP). This innovative protocol integrates real-time data analytics and adaptive machine learning models into its algorithmic framework to dynamically optimize routing decisions based on continuously changing urban conditions. Through the utilization of data-driven simulation models and machine learning techniques, the research sought to significantly improve the efficiency, reliability, and scalability of urban WSNs. Existing protocols such as Geographic Adaptive Fidelity (GAF), Greedy Perimeter Stateless Routing (GPSR), and Dynamic Source Routing (DSR) were critically assessed under urban settings using extensive datasets detailing New York City's traffic patterns and environmental variables. The analysis demonstrated that while GPSR showed superior performance in terms of latency, throughput, and energy efficiency among the traditional protocols, the introduction of UALRP, with its advanced predictive and adaptive capabilities, can further optimize these metrics. The study affirms the critical role of enhancing location accuracy and the ongoing advancement of machine learning models within urban routing protocols. These insights advocate for the broader implementation of adaptive strategies like UALRP to foster the development of more resilient and efficient urban cyber-physical systems.

Towards chemical source tracking and characterization using physics-informed neural networks

This paper presents a new algorithm using physics-informed neural networks (PINNs) to track and characterize spatio-temporally varying chemical sources based on time-resolved measurements of chemical concentration, fluid velocity, and pressure. Chemical source emission functions, chemical concentration, fluid velocity and fluid pressure fields are modeled as multi-layer perceptrons (MLPs). During the training process, sensors readings of chemical concentration, fluid velocity, and fluid pressure are matched to the output of the respective MLPs at the given spatio-temporal locations. Simultaneously, the physics of fluid flow and chemical dispersion at an arbitrary number of spatio-temporal locations in the domain of interest are enforced through regularization. Once trained, the constituent MLPs can be evaluated to generate the time resolved maps of source emissions, chemical concentration, fluid velocity and pressure. Extensive numerical simulation validate the method, including scenarios with both static and dynamic sources in complex flow fields. This innovative approach marks a significant advancement in environmental monitoring technologies.

Patterns and controls of topographic change within the deflation basins of a trough and bowl coastal blowout

AbstractIn North‐West Europe and throughout the world, dune systems are increasingly stabilised by vegetation, due to both human intervention and changes in climate. Blowouts are erosional hollows produced by wind erosion in vegetated or semi‐vegetated dune systems. Where active blowouts are present in vegetated dunes, they provide a source of dynamism and sediment in an otherwise fixed environment. The transition from a fixed vegetated dune to an active blowout is poorly understood, however, and anthropogenic attempts to reactivate areas of bare sand in dunes are often unsuccessful. In this study, we measured topographic change at a monthly resolution in the deflation basins of one bowl and one trough blowout over a 23‐month period in Ainsdale Sand Dunes National Nature Reserve, North‐West England. Our results show that monthly surface change in the blowouts did not correlate strongly with the transport capacity of winds measured at a meteorological station 10 km south of the site. Precipitation was found to have a moderate negative correlation with all indices of surface change, that is, the more it rained the less the surface changed. Inter‐annual (23 months) patterns of topographic change in the bowl and trough blowouts were distinctly different. In the deflation basin of the bowl blowout erosion predominantly took place on the erosional walls facing into the prevailing winds, while minimal change occurred on the erosional walls facing away from the prevailing wind direction. This produced a moderate negative correlation between surface change and slope, that is, the steeper the slope, the more erosion occurred. In the trough blowout, erosion took place in the centre of the deflation basin and minimal change was measured on the easternmost erosional wall, which faced the prevailing winds. Patterns of monthly topographic change were highly variable and demonstrated that changes in the direction of above‐threshold winds can cause blowout walls and floors to ‘flip’ from erosional to depositional surfaces. These findings highlight the variability and complexity of surface change in blowouts and demonstrate that patterns vary because of landform morphology and climatic conditions.

Secure Routing and Hybrid SqueezenetFQuantumNN for Intrusion Detection in MANET

An efficient Intrusion Detection System (IDS) is essential to secure Mobile Ad-hoc Networks (MANETs) against malevolent attacks. The present IDS still faces challenges in enhancing detection accuracy and in decreasing the false alarm rate. To overcome the above issues, a secure routing protocol for intrusion detection in MANET has been proposed here. In the proposed model, initially, MANET is simulated, which is followed by routing based on Dynamic Source Routing (DSR) routing protocol. Then, intrusion detection is done at Base Station (BS). At the BS, the log files are acquired from the dataset at first and it is normalized using Quintile normalization to transform the data into a structured form. Then, the desired features are selected in the feature selection phase using Wave–Hedges metrics for removing redundant and irrelevant data. Finally, intrusion detection is performed using the proposed hybrid SqueezenetFQuantum Neural Network (SqueezenetFQuantumNN) which is devised by the fusion of Squeeze Net and Quantum Neural Network (QuantumNN). The effectiveness of the proposed SqueezenetFQuantumNN is measured depending on evaluation metrics and is found to attain accuracy of 95.72%, True Positive Rate (TPR) of 93.42%, and True Negative Rate (TNR) of 91.39%.

Bridging the gap: Integrating static and dynamic data for improved permeability modeling and super k zone detection in vuggy reservoirs

Permeability modeling in fractured and vuggy reservoirs presents significant challenges, especially in carbonate reservoirs like Brazil’s Barra Velha Formation. The interpretation of vug properties is often subject to misconceptions. It is common to mistakenly consider vugs as part of the matrix, attributing uniform porosity and permeability properties. Alternatively, they are sometimes regarded as fractures, attempting to replicate the influence on permeability enhancement. However, vugs exhibit distinct behavior compared to fractures. Their characteristics are contingent upon factors such as geometry, size, and their relationship with the matrix. Existing methods often overlook the substantial impact of vuggy porosity, leading to inadequate representation of permeability values obtained from core plug measurements or downhole static data, such as Nuclear Magnetic Resonance (NMR) logs. In contrast, dynamic data derived from drill-stem tests (DSTs) and inferred from the Productivity Index (J) provide a more holistic view of the reservoir’s permeability, yet typically yielding a single permeability value for each interpreted interval. This research introduces a novel methodology that addresses these challenges by integrating static and dynamic data, with a primary focus on vuggy porosity using a permeability indicator from a dynamic source. A key innovation lies in the development of a method for estimating total permeability in vuggy intervals, accounting for the effects of vugs and calibrating permeability with dynamic data such as J, DST and Production Logging Tests (PLT). We substantiated the impact of vugs on the augmentation of permeability and production through the application of a localized reservoir simulation model around a key well. Two sensitivity analyses were conducted, one employing a grid representing matrix permeability and another incorporating total permeability with adjustments accounting for the influence of vugs. Additionally, we present two approaches for estimating vuggy porosity: one based on acoustic WellBore Image (WBI) and the second through image segmentation, which can be applied universally across different wellbore image formats. Furthermore, this study explores the concept of superkzones, representing intervals with exceptionally high permeability. The partitioning coefficient (CP), quantifying the relationship between vuggy porosity and total porosity, is employed to generate a probability curve for the occurrence of superkzones in intervals lacking production data. This curve accounts for critical factors such as pressure drop and fluid viscosity, providing a more comprehensive understanding of reservoir behavior. Through a comprehensive case study, our research aims to shed light on the intricate permeability characteristics of the Barra Velha Formation, while identifying the presence of superkzones. The proposed methodologies offer a bridge between static and dynamic data, resulting in a continuous permeability curve that aligns more closely with production data. Ultimately, this work contributes valuable insights for reservoir management and the production strategies optimization in complex carbonate reservoirs within the Santos Basin.

(Re-)Connecting Inflation and the Labor Market: A Tale of Two Curves

We propose an empirical framework in which shocks to worker reallocation, aggregate activity, and labor supply drive the joint dynamics of labor market outcomes and inflation, and where reallocation shocks take two forms depending on whether they result from quits or from job loss. In order to link our approach with previous theoretical and empirical work, we extend the procedure for estimating a Bayesian sign-restricted VAR so that priors can be directly imposed on the VAR's impact matrix. We find that structural shocks that shift the Beveridge curve have different effects on inflation. Our model allows us to fully decompose movements of or along the empirical Beveridge curve in terms of the contribution of each shock and also allows us to estimate the Phillips correlation associated with each shock; our results imply that observed Beveridge and Phillips correlations can change over time depending on what types of structural shocks predominate in a given period. Applying our model to the pandemic-related recession and recovery, we find that reallocation shocks were a key source of labor market dynamics during this period and explain how a post-pandemic ``soft landing,’’ in which inflation declined without a significant rise in unemployment, was possible.

Research on Voltage Coordinated and Stability Control of Condensers Based on Reactive Power Replacement

Abstract In this research, we investigated condenser voltage coordination and stability control systems by analyzing voltage coordinated control mode among excitation, voltage coordination and stability as well as automatic-voltage reactive power control systems. By using dynamic excitation regulation and steady-state regional voltage source control replacement, dynamic reactive power quickly output under fault condition as well as optimized regional reactive power under steady state were realized. The correctness and practicability of the proposed strategy was verified through engineering application. Adopting the reactive power displacement of voltage coordination control system to solve local voltage instability problem after UHV DC blocking. The proposed method realized multi-time scale emergency, recovery and steady-state control of the system under steady and dynamic states.

Chaos in Order: Applying ML, NLP, and Chaos Theory in Open Source Intelligence for Counter-Terrorism

The present research aims to investigate whether Chaos Theory can be combined with Machine Learning and Natural Language Processing to apply these techniques to Open Source Intelligence (OSINT) analysis. Describing the role of OSINT in different domains and highlighting chaos as a valuable resource for information gathering, the study highlights that the substantial volume, swift velocity, and extensive variety of open-source data pose significant challenges. To address these challenges it is proposed to apply elements of Chaos Theory and advanced computational methods to open-source data. Key concepts from Chaos Theory that will be explored are the ‘Butterfly Effect’, and ‘Strange Attractors’, attempting to demonstrate that chaotic aspects of data can be exploited and transformed into dynamic and powerful sources of information. To support the above, the research includes a case study that exploits and analyses data from Reddit posts and concludes that recognizing and exploiting the dynamic interaction between order and chaos places Chaos Theory not only complementary but as a foundational stone of the overall OSINT toolkit, in the hands of intelligence analysts.

Dust model sensitivity to dust source mask, sandblasting efficiency, air density, and land use: Implications for model improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared with a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 12 times more dust than does a physics-based expression. Simulations using model-predicted versus constant air density differ by only 8%. However, this difference (often overlooked) could range between 12% and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The difference between simulations within each of the four pairs is generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Multichannel Dynamic Sound Rendering and Echo Suppression in a Room Using Wave Field Synthesis

With multichannel sound rendering systems, an immersive sound can be experienced by a broader audience. Rendering of a stationary source is widely studied in the literature. However, a dynamic sound, where the source’s position is continuously changing, provides improved source localization and a listener experience more immersed in the rendered sound. This paper investigates the multichannel sound rendering of a dynamic sound source using a wave field synthesis method in a reverberant shoe-box room model. Finite difference time domain is employed to visualize the reproduced sound field in the time domain. To address the challenge of room echo-induced degradation in sound quality, a method is presented in which the reflected sound is compensated by rendering the primary source’s images from the auxiliary actuators, enhancing SNR by approximately 2–3.5 dB.

Outdoor Radon Dose Rate in Canada's Arctic amid Climate Change.

Decades of radiation monitoring data were analyzed to estimate outdoor Radon Dose Rates (RnDRs) and evaluate climate change impacts in Canada's Arctic Regions (Resolute and Yellowknife). This study shows that the RnDR involves dynamic sources and complex environmental factors and processes. Its seasonality and long-term trends are significantly impacted by temperatures and soil-and-above water contents. From 2005 to 2022, Yellowknife's RnDR increased by +0.35 ± 0.06 nGy/h per decade, with the fastest increases occurring in cold months (October to March). The rise is largely attributable to water condition changes over time in these months, which also caused enhanced soil gas emissions and likely higher indoor radon concentrations. In Resolute, the RnDR increased between 2013 and 2022 at +0.62 ± 0.19 nGy/h (or 16% relatively) per decade in summer months, with a positive temperature relationship of +0.12 nGy/h per °C. This work also demonstrates the relevance of local climate and terrain features (e.g., typical active layer depth, precipitation amount/pattern, and ground vegetation cover) in researching climate change implications. Such research can also benefit from using supporting monitoring data, which prove effective and scientifically significant. From the perspective of external exposure to outdoor radon, the observed climate change effects pose a low health risk.

Removal efficiency of restroom ventilation revisited for short-term evaluation

Ventilation efficiency or contaminant removal efficiency is often evaluated using the ratio between the concentrations in the exhaust air and the room air. This ratio does not truly represent the expectation of ventilation in restrooms, where dynamic airflow fields and sources are more typical. This study focuses on a short-term (10 min) pollutant removal percentage in a residential restroom featuring a dynamic airflow field, particularly with the onset of window-induced stack ventilation during toilet uses. Thirteen ventilation scenarios of a residential restroom were studied using the numerical method that was validated by a mock-up experiment. The scenarios differed in the operation of the exhaust fan and window. Results show that the 10-min pollutant removal percentage of a typical exhaust ventilation system at 10 h-1 air change rate (ACH) is only 68.5%. Under exhaust ventilation, opening the window can introduce both adverse short circuit and favorable stack ventilation depending on the difference between the indoor and outdoor temperatures. As the temperature difference increases from 0 to 12.5 °C, the removal percentage increases from below 50%, a drop due to short circuit, to above 98% thanks to a tripled ventilation rate. The human thermal plume has notable effect on the removal percentage, but its effect can be neglected with the presence of stack ventilation. The hybrid ventilation strategy has impact on perceived air quality and thermal comfort. When the outdoor air is colder, opening the window under exhaust ventilation may increase the current sitting user’s exposure to the self-produced pollutants but can reduce the exposure of the next immediate standing user. In addition, opening the window in cold days will make the toilet user thermally uncomfortable with reduced local temperatures and increased airflow velocities. The study highlights the importance of using the short-term removal percentage to evaluate the performance of restroom ventilation.

Rapid Primary Sulfate Aerosol Generation Observed With OP‐FTIR in the Eruptive Plume of the Fagradalsfjall Basaltic Eruption, Iceland, 2021

AbstractOpen‐Path Fourier‐Transform Infrared (OP‐FTIR) absorption spectroscopy is a powerful method for remote characterization of volcanic plume composition from safe distances. Many studies have used it to examine the composition of volcanic gas emitted at the surface, which is influenced by initial volatile contents and magma ascent/storage processes, and help to reveal the dynamics controlling surface activity. However, to evaluate the health hazard threats associated with volcanic emissions and their potential impact on wider atmospheric conditions, near‐source particle measurements are also key. Here we present a forward model and fitting algorithm which allows quantification of particle size and abundance. This was successfully applied to radiometrically uncalibrated OP‐FTIR spectra collected with a highly dynamic radiation source during the Fagradalsfjall eruption, Iceland, on 11 August 2021. Quantification of plume temperatures ranging from 350 to 650 K was essential to characterize the emission‐absorption behavior of SO2, enabling retrievals of particulate matter in the thermal infrared spectral window (750–1250 cm−1) in each spectrum. For the first time, we observe the rapid formation of primary aerosols in young plumes (only a few seconds old) with OP‐FTIR. Temperature‐dependent SO2/SO42− molar ratios range from 100 to 250, consistent with a primary formation mechanism controlled by cooling and entrainment of atmospheric gases. This novel aerosol spectrum retrieval opens new frontiers in field‐based measurements of sulfur partitioning and volcanic plume evolution, with the potential to improve volcano monitoring and quantification of air quality hazard assessments.

Delayed neutron library analysis using dynamic control rod calibration method based on results from the VR-1 reactor experiment and Serpent transient calculation

This study is focused on a comparison of experimental neutron flux data from the VR-1 reactor with results from Serpent calculation. Serpent data were calculated by the dynamic part of the code and the steady-state criticality calculation. The Serpent calculation was performed with the JEFF 3.3 and ENDF/B-VIII.0 nuclear data library. This paper expands previous work and focuses on the integral control rod worth. Comparison of the ENDF/B-VIII.0 and JEFF 3.3 nuclear data libraries with experimental data is especially valuable. The comparison shows better performance of JEFF 3.3 nuclear data instead of ENDF/B-VIII.0 for the transient calculation of the cases presented in this article. In addition, the comparison of static and dynamic reactivity provided an interesting difference, especially for small rod insertion. Moreover, the study confirms that the Serpent dynamic external source simulation mode provides an accurate description of reactor behavior, and that the shape of the integral control rod worth is close to one experimentally measured.

Prototype Biodiversity Digital Twin: Phylogenetic Diversity

Phylogenetic diversity (PD) represents a fundamental measure of biodiversity, encapsulating the extent of evolutionary history within species groups. This measure, pivotal for understanding biodiversity's full dimension, has gained recognition by major environmental and scientific organisations, including the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Unlike traditional taxonomic richness, PD offers a comprehensive, evolutionary perspective on biodiversity, essential for conservation planning and biodiversity management. This manuscript describes the development of a BioDT (Biodiversity Digital Twin) prototype, aimed at facilitating the calculation and visualisation of biodiversity metrics from global, dynamic data sources. By utilising the PhyloNext pipeline and integrating with global phylogenetic and species occurrence databases like the Open Tree of Life (OToL) and the Global Biodiversity Information Facility (GBIF), the prototype aims to significantly reduce computation time and enhance user interaction. This enables dynamic visualisation and potentially hypothesis testing, making it a valuable tool for researchers, monitoring initiatives, policy-makers and the public. The prototype's development focuses on improving the PhyloNext pipeline's scalability and creating a more intuitive user interface, expanding its utility for conservation efforts and biodiversity exploration. Our work illustrates the potential impact of the BioDT prototype in supporting diverse user groups in visualising and exploring PD, thus contributing to more informed decision-making in conservation and biodiversity management.

Genesis and Related Reservoir Development Model of Ordovician Dolomite in Shuntogol Area, Tarim Basin

The Ordovician thick dolostone in Shuntogol area of the Tarim Basin has the potential to form a large-scale reservoir, but its genesis and reservoir development model are still unclear. Starting from a sedimentary sequence, this study takes a batch of dolostone samples obtained from new drilling cores in recent years as the research object. On the basis of core observation and thin section identification, trace elements, cathodoluminescence, carbon and oxygen isotopes, rare earth elements, and X-ray diffraction order degree tests were carried out to discuss the origin of the dolomite and summarize the development model of the dolostone reservoir. The analysis results show that the Ordovician dolomite in the study area had a good crystalline shape, large thickness, high Fe and Mn values, and mostly showed bright red light or bright orange–red light under cathode rays. The ratio of δ18O values to seawater values at the same time showed a negative bias; the δCe values were negative anomalies, the δEu values were positive anomalies, and the order degree was high. This indicates that the dolomitization process occurred in a relatively closed diagenetic environment. The Ordovician carbonate rocks in the study area were low-lying during the sedimentary period, and with the rise of sea level, the open platform facies continued to develop. When the Middle and Lower Ordovician series entered the burial stage, the main hydrocarbon source rocks of the lower Cambrian Series entered the oil generation peak, and the resulting formation overpressure provided the dynamic source for the upward migration of the lower magnesium-rich fluid, and the dolomitization fluid entered the karst pore system in the target layer to produce all the dolomitization. This set of dolostone reservoirs is large in scale and can be used as a favorable substitute area for deep carbonate exploration for continuous study.