Open-source Framework Research Articles

Demystifying React Native Android Apps for Static Analysis

React Native, an open-source framework, simplifies cross-platform app development by allowing JavaScript-side code to interact with native-side code. Previous studies disregarded React Native, resulting in insufficient static analysis of React Native app code. This study initiates the investigation of challenges when statically analyzing React Native apps. We propose ReuNify to improve Soot-based static analysis coverage for JavaScript-side and native-side code. ReuNify converts Hermes bytecode to Soot’s intermediate representation. Hermes bytecode, compiled from JavaScript code and integrated into React Native apps, possesses a unique syntax that eludes current JavaScript analyzers. Additionally, we investigate opcode distribution and conduct in-depth analyses of the usage of opcode between popular apps and malware. We also propose a benchmark consisting of 97 control-flow-related cases to validate the control-flow recovery of the generated intermediate representation. Furthermore, we model the cross-language communication mechanisms of React Native to expand the static analysis coverage for native-side code. Our evaluation demonstrates that ReuNify enables an average increase of 84% in reached nodes within the call graph and further identifies an average of two additional privacy leaks in taint analysis. In summary, this paper demonstrates that ReuNify significantly improves the static analysis for the React Native Android apps

An Improved Direct Forcing Immersed Boundary Method With Integrated Mooring Algorithm for Floating Offshore Wind Turbines

Abstract An upgraded direct forcing immersed boundary method is implemented in the open-source hydrodynamic framework REEF3D::CFD for simulating the six-degrees-of-freedom (6DOF) motions of a floating offshore wind turbine (FOWT) based on the OC5 semi-submersible design. The direct forcing method is enhanced with a new density interpolation method across the fluid-structure interface that removes unphysical spurious phenomena and ensures stable and accurate wave load calculations on floating objects. A quasi-static algorithm is used for modeling the mooring system of the OC5 platform and restraining its motions in waves. The Navier-Stokes equations are solved on a staggered structured rectilinear grid for the hydrodynamic simulations. The level-set method is used to capture the free surface of the ocean waves. A ray-casting algorithm is employed to get inside-outside information near the fluid-solid interface while maintaining the underlying Cartesian grid in the hydrodynamic domain. The performance and accuracy of the mooring algorithm are compared to the widely-used mooring model MoorDyn, which is coupled to the hydrodynamic solver in REEF3D::CFD. The study demonstrates that the enhanced direct forcing method with the integrated quasi-static mooring algorithm in REEF3D::CFD provides a robust and accurate tool, suitable for the numerical analysis of the state-of-the-art FOWT in ocean waves.

A multidisciplinary framework from reactors to repositories for evaluating spent nuclear fuel from advanced reactors

This study presents a multidisciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF) disposal. The framework consists of (1) OpenMC for simulating neutronics, fuel depletion, and radioactive decays; (2) NWPY for computing the repository footprint given the thermal constraints; and (3) PFLOTRAN for simulating radionuclide transport in the geosphere to quantify the repository performance and environmental impact. We first perform the meta-analysis of past comparative analyses to identify the factors that led previously to their inconsistent conclusions. We then demonstrate the new framework by comparing five reactor types. Our analysis highlights the granularity and the specificities of each reactor and fuel type so that we should avoid making sweeping conclusions about advanced reactor SNF. Significant findings are that (1) the repository footprint is neither linearly related to SNF volume nor to decay heat, due to the repository’s thermal constraint (2), fast reactors have significantly higher I-129 inventory, which is often the primary dose contributor, and (3) the repository performance primarily depends on the waste forms. The TRISO-based reactors, in particular, have significantly higher SNF volumes compared to the others but result in smaller repository footprints and lower peak dose rates. The open-source framework ensures proper multidisciplinary connections between reactor simulations and environmental assessments, as well as the transparency/traceability required for such comparative analyses. It aims to support reactor designers, repository developers, and policymakers in evaluating the impact of different reactor designs, with the ultimate goal of improving the sustainability of nuclear energy systems.

Optimizing laser-induced phase transformations in Sb2S3 thin films: Simulation framework and experiments

We present a novel simulation approach combined with pulsed laser experiments, spectroscopic ellipsometry, and Raman spectroscopy to comprehensively analyze phase transformation dynamics in thin films. The simulations apply to any thin film stack and incorporate critical factors, such as thin film interference, heat transfer, and temperature-dependent optical properties during heating and melting. As a case study, we investigate the picosecond laser-induced amorphization of antimony sulfide (Sb2S3) thin films, a promising alternative to traditional phase-change materials in photonic applications to validate the simulation model. The computational efficiency of our simulations enables not only the investigation of the laser-induced phase transformation but also the optimization of key process parameters and parameter fitting. The simulations identified optimal film thickness and laser fluence parameters that maximize energy efficiency, melting effectiveness, and quenching rate while ensuring high reflectivity contrast between the amorphous and crystalline states. By constructing a wide-ranging, high-resolution parameter map of the laser fluence and film thickness dependence of the melting process, we demonstrate how this model guides the understanding of phase transformation dynamics. Raman spectroscopy confirms the polycrystalline to amorphous transition of Sb2S3 and provides a semiquantitative estimate of the amorphous fraction as a function of laser fluence, which is qualitatively consistent with the simulation predictions of the model. The open-source simulation framework, experimentally validated, provides valuable insights into laser-induced amorphization dynamics in Sb2S3 and related phase-change material thin films, enabling rapid optimization of photonic devices.

ETHOS.ActivityAssure—an open-source validation framework for synthetic European activity profiles

The simulation of human behavior is an essential component in the domain of energy demand modeling. However, due to its diverse nature, it is often unclear whether a simulated behavior pattern is fitting to certain contexts. Combined with the poor availability of appropriate activity data, this makes proper validation of behavior models difficult. Existing validation approaches are limited and specialized to the respective use case, and are therefore not reusable or comparable. To address this issue, the new open-source framework ETHOS.ActivityAssure is presented for evaluation of generated activity profiles, supporting the validation of behavior models. For this purpose, an aggregated activity dataset is created from restricted European time use data and published to ensure reusability. Validation is conducted through a set of indicators and comparative plots, taking into account activity duration, frequency, and time. A categorization of person types and activities enables mapping to result categories commonly used in behavior models. The framework's capabilities are demonstrated on the residential demand model LoadProfileGenerator. The developed framework allows for consistent and reproducible validation of synthetic activity profiles targeting Europe, without requiring access to confidential data. This offers the opportunity to enhance both new and existing behavior models by identifying flaws, compare multiple modeling approaches, and thoroughly evaluate model quality for diverse target purposes.

Unlocking new export opportunities: An open-source framework for assessing green iron and steel supply chains

Globally traded commodities such as iron and steel are coming under international scrutiny owing to their significant embedded greenhouse emissions. Commodity export-oriented nations must explore opportunities for decarbonising these supply chains or risk losing market share as customers seek cleaner alternatives and as initiatives such as carbon border adjustment mechanisms (CBAM) come into effect. Here we present an open-source model developed to assist industry and other stakeholders in assessing the costs of potential CO2 abatement pathways in supplying green iron ore, green iron, or green steel to key global markets. Using a case study, we assess the development of a green steel supply chain from major iron ore producers of Australia, Brazil, and Sweden, to key markets in Europe, China, and India. Projected costs of renewable energy and other key technologies are used to estimate possible future costs of different decarbonisation pathways. Our modelling suggests that by 2050, export costs of green iron ore, iron and steel could range from 48 to 150, 370–600 and 540–810 USD/tonne respectively. Access to low-cost renewable energy is the key driver of cost reductions for value added products such as iron and steel, and given their substantial renewable energy potential, in 2050 Australia and Brazil could outcompete Sweden in providing these products, with both even outcompeting Sweden in exporting to the European market. The release of our model as open source allows any and all stakeholders to explore the implications of key technology and market uncertainties over least cost green iron and steel supply chains.

129Xe Image Processing Pipeline: An open-source, graphical user interface application for the analysis of hyperpolarized 129Xe MRI.

Hyperpolarized 129Xe MRI presents opportunities to assess regional pulmonary microstructure and function. Ongoing advancements in hardware, sequences, and image processing have helped it become increasingly adopted for both research and clinical use. As the number of applications and users increase, standardization becomes crucial. To that end, this study developed an executable, open-source 129Xe image processing pipeline (XIPline) to provide a user-friendly, graphical user interface-based analysis pipeline to analyze and visualize 129Xe MR data, including scanner calibration, ventilation, diffusion-weighted, and gas exchange images. The customizable XIPline is designed in MATLAB to analyze data from all three major scanner platforms. Calibration data is processed to calculate optimal flip angle and determine129Xe frequency offset. Data processing includes loading, reconstructing, registering, segmenting, and post-processing images. Ventilation analysis incorporates three common algorithms to calculate ventilation defect percentage and novel techniques to assess defect distribution and ventilation texture. Diffusion analysis features ADC mapping, modified linear binning to account for ADC age-dependence, and common diffusion morphometry methods. Gas exchange processing uses a generalized linear binning for data acquired using 1-point Dixon imaging. The XIPline workflow is demonstrated using analysis from representative calibration, ventilation, diffusion, and gas exchange data. The application will reduce redundant effort when implementing new techniques across research sites by providing an open-source framework for developers. In its current form, it offers a robust and adaptable platform for 129Xe MRI analysis to ensure methodological consistency, transparency, and support for collaborative research across multiple sites and MRI manufacturers.

Exploring the Efficiency of Hybrid Recommender Systems Implemented with TensorFlow Framework

In recent years, the field of recommendation systems has seen significant advancement with the introduction of hybrid approaches. These systems combine the strengths of multiple recommendation techniques to provide more accurate and diverse recommendations to users. In this research, we propose and evaluate the effectiveness of a hybrid recommender system that utilizes TensorFlow, an open-source machine learning framework, to implement the system. The proposed system combines both collaborative and content-based methods to remove the cold start problem and make personalized recommendations that can recommend similar movies to the users based on features extracted by the model. The results of our experiments demonstrate that the proposed hybrid system outperforms traditional singular methods and can be effectively implemented using TensorFlow. This research provides insights into the potential of TensorFlow for building efficient hybrid recommendation systems and the benefits of combining multiple recommendation techniques.

Making Tax Smart: Feasibility of Distributed Ledger Technology for Building Tax Compliance Functionality to Central Bank Digital Currency

The latest advancements in distributed ledger technology (DLT) and payment architectures such as the UK’s New Payments Architecture present opportunities for leveraging the hidden informational value and intelligence within payments. In this paper, we present Smart Money, an infrastructure capability for a central bank digital currency (CBDC) which enables real-time value-added tax split payments, oversight, controlled access, and smart policy implementation. This capability is implemented as a prototype called Making Tax Smart (MTS), which utilises the open-source R3 Corda DLT framework. The results presented herein confirm that it is feasible to build an MTS capability which is scalable and co-exists with the current payment systems. Smart Money CBDC has the potential to mobilise payments data, transforming the role of money from a blunt instrument to a government policy sensor and actuator without disrupting the existing money system. DLT, smart contracts, and programmable money have a crucial role to play with benefits for government departments, the economy, and society as a whole.

RadicalPy: A Tool for Spin Dynamics Simulations.

Radical pairs (electron-hole pairs, polaron pairs) are transient reaction intermediates that are found and exploited in all areas of science, from the hard realm of physics in the form of organic semiconductors, spintronics, quantum computing, and solar cells to the soft domain of chemistry and biology under the guise of chemical reactions in solution, biomimetic systems, and quantum biology. Quantitative analysis of radical pair phenomena has historically been successful by a few select groups. With this in mind, we present an intuitive open-source framework in the Python programming language that provides classical, semiclassical, and quantum simulation methodologies. A radical pair kinetic rate equation solver, Monte Carlo-based spin dephasing rate estimations, and molecule database functionalities are implemented. We introduce the kine-quantum method, a new approach that amalgamates classical rate equations, semiclassical, and quantum techniques. This method resolves the prohibitively large memory requirement issues of quantum approaches while achieving higher accuracy, and it also offers wavelength-resolved simulations, producing time- and wavelength-resolved magnetic field effect simulations. Model examples illustrate the versatility and ease of use of the software, including the new approach applied to the magnetosensitive absorption and fluorescence of flavin adenine dinucleotide photochemistry, spin-spin interaction estimation from molecular dynamics simulations on radical pairs inside reverse micelles, radical pair anisotropy inside proteins, and triplet exciton pairs in anthracene crystals. The intuitive interface also allows this software to be used as a teaching or learning aid for those interested in the field of spin chemistry. Furthermore, the software aims to be modular and extensible, with the aim to standardize how spin dynamics simulations are performed.

SU2-COOL: Open-source framework for non-ideal compressible fluid dynamics

We present a fully open-source framework for the numerical simulation of Non-Ideal Compressible Fluid Dynamics (NICFD). The open-source Computational Fluid Dynamics suite SU2 is coupled to the open-source thermophysical library CoolProp, which includes state-of-the-art thermodynamic models of numerous pure fluids and mixtures relevant to applications. Accurate thermodynamic models are needed due to non-ideal operating conditions in which the fluid thermodynamics cannot be described by the simple ideal-gas law (Pv=RT). The coupling interface implements new C++ classes, which allow the automatic exchange of information between SU2 and CoolProp, and it is made directly available as an additional module integrated into the open-source SU2 suite. To assess the performance of the NICFD simulation framework, we present three test cases: a nozzle flow exhibiting non-ideal thermodynamics effects, a nozzle flow with non-monotone Mach number variation, a representative non-ideal gasdynamics effect, and a non-classical rarefaction oblique shock over a wedge. Results are verified against available experiment data and solutions obtained with different implementations of non-ideal thermodynamics in SU2. Performance of the new framework is assessed on user-friendliness, scalability, solution accuracy, and computational efficiency.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

An evaluation of the hybrid Fokker–Planck-DSMC approach for high-speed rarefied gas flows

The Direct Simulation Monte Carlo (DSMC) method has been widely used for simulations of rarefied gas flows. It has been proven that the numerical solution of the DSMC method converges to the Boltzmann equation, providing a solid physical foundation for high Knudsen numbers. However, many aerospace engineering problems include both low and high-density regions in a single domain, making the DSMC method computationally expensive. Over the past decade, the particle Fokker–Planck (FP) method has been studied as a means to reduce computational costs near the continuum regime. While enhancing efficiency, the FP method loses physical accuracy at high Knudsen numbers. Aiming for universal accuracy and efficiency across the whole range of Knudsen numbers, the hybrid FP-DSMC method has been studied. Nevertheless, consistent comparisons among the DSMC, FP, and hybrid FP-DSMC methods have received limited attention so far. This paper presents a consistent comparative study of the DSMC, FP, and hybrid FP-DSMC methods to assess the accuracy and efficiency of the hybrid FP-DSMC method. The hybrid FP-DSMC solver is developed based on the open-source SPARTA framework. The benchmark problems include supersonic flow in a planar nozzle, hypersonic flow around a cylinder, and hypersonic flow around a THAAD-like missile. The results demonstrate that the hybrid FP-DSMC method can be more efficient than the DSMC method while being more accurate than the FP method. The speed-up achieved by the FP-DSMC method ranged from 1.5 to 14 times compared to the converged DSMC simulation.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring

Recent research highlights the potential of consumption-based feedback for water conservation, emphasizing the need for Non Intrusive Water Monitoring (NIWM). However, existing NIWM studies often rely on small datasets, a pre-selected class of models, and inaccessible software. Here, we introduce PyNIWM, a machine learning-based open-source Python framework for NIWM. PyNIWM enables water end-use classification via (i) data characterization and feature engineering, (ii) water end-use event classification with four machine learning classifiers, and (iii) performance assessment. We demonstrate PyNIWM on a real-world dataset containing around 800,000 labeled end-use events from 762 homes across the USA and Canada. The four PyNIWM classifiers achieve F1 scores above 0.85, indicating high suitability for water end-use classification. However, a tradeoff between accuracy and computational cost exists. Finally, data balancing through oversampling enhances classification of low-represented end-use classes, but does not improve overall classification. We release PyNIWM as an open-source software, aiming for collaborative and reproducible research.

An Open Annotated Dataset and Baseline Machine Learning Model for Segmentation of Vertebrae with Metastatic Bone Lesions from CT.

Automatic analysis of pathologic vertebrae from computed tomography (CT) scans could significantly improve the diagnostic management of patients with metastatic spine disease. We provide the first publicly available annotated imaging dataset of cancerous CT spines to help develop artificial intelligence frameworks for automatic vertebrae segmentation and classification. This collection contains a dataset of 55 CT scans collected on patients with various types of primary cancers at two different institutions. In addition to raw images, data include manual segmentations and contours, vertebral level labeling, vertebral lesion-type classifications, and patient demographic details. Our automated segmentation model uses nnU-Net, a freely available open-source framework for deep learning in healthcare imaging, and is made publicly available. This data will facilitate the development and validation of models for predicting the mechanical response to loading and the resulting risk of fractures and spinal deformity.

KnowBug: Enhancing Large language models with bug report knowledge for deep learning framework bug prediction

Understanding and predicting the bug type is crucial for developers striving to enhance testing efficiency and reduce software release problems. Bug reports, although semi-structured, contain valuable semantic information, making their comprehension critical for accurate bug prediction. Recent advances in large language models (LLMs), especially generative LLMs, have demonstrated their power in natural language processing. Many studies have utilized these models to understand various forms of textual data. However, the capability of LLMs to fully understand bug reports remains uncertain. To tackle this challenge, we propose KnowBug, a framework designed to augment LLMs with knowledge from bug reports to improve their ability to predict bug types. In this framework, we utilize bug reports from open-source deep learning frameworks, design specialized prompts, and fine-tune LLMs to assess KnowBug’s proficiency in understanding bug reports and predicting different bug types.

A hardware architecture for single and multiple ellipse detection using genetic algorithms and high-level synthesis tools

Ellipse detection techniques are often developed and validated in software environments, neglecting the critical consideration of computational efficiency and resource constraints prevalent in embedded systems. Furthermore, programmable logic devices, notably Field Programmable Gate Arrays (FPGAs), have emerged as indispensable assets for enhancing performance and expediting various processing applications. In the realm of computational efficiency, hardware implementations have the flexibility to tailor the required arithmetic for various applications using fixed-point representation. This approach enables faster computations while upholding adequate accuracy, resulting in reduced resource and energy consumption compared to software applications that rely on higher clock speeds, which often lead to increased resource and energy consumption. Additionally, hardware solutions provide portability and are suitable for resource-constrained and battery-powered applications. This study introduces a novel hardware architecture in the form of an intellectual property core that harnesses the capabilities of a genetic algorithm to detect single and multi ellipses in digital images. In general, genetic algorithms have been demonstrated to be an alternative that shows better results than those based on traditional methods such as the Hough Transform and Random Sample Consensus, particularly in terms of accuracy, flexibility, and robustness. Our genetic algorithm randomly takes five edge points as parameters from the image tested, creating an individual treated as a potential candidate ellipse. The fitness evaluation function determines whether the candidate ellipse truly exists in the image space. The core is designed using Vitis High-Level Synthesis (HLS), a powerful tool that converts C or C＋＋functions into Register-Transfer Level (RTL) code, including VHDL and Verilog. The implementation and testing of the ellipse detection system were carried out on the PYNQ-Z1, a cost-effective development board housing the Xilinx Zynq-7000 System-on-Chip (SoC). PYNQ, an open-source framework, seamlessly integrates programmable logic with a dual-core ARM Cortex-A9 processor, offering the flexibility of Python programming for the onboard SoC processor. The experimental results, based on synthetic and real images, some of them with the presence of noise processed by the developed ellipse detection system, highlight the intellectual property core’s exceptional suitability for resource-constrained embedded systems. Notably, it achieves remarkable performance and accuracy rates, consistently exceeding 99% in most cases. This research aims to contribute to the advancement of hardware-accelerated ellipse detection, catering to the demanding requirements of real-time applications while minimizing resource consumption.