Fundamental Metrics Research Articles

Floating active carpets drive transport and aggregation in aquatic ecosystems

Communities of swimming microorganisms often thrive near liquid–air interfaces. We study how such ‘active carpets’ shape their aquatic environment by driving biogenic transport in the water column beneath them. The hydrodynamic stirring that active carpets generate leads to diffusive upward fluxes of nutrients from deeper water layers, and downward fluxes of oxygen and carbon. Combining analytical theory and simulations, we examine the biogenic transport by studying fundamental metrics, including the single and pair diffusivity, the first passage time for particle pair encounters and the rate of particle aggregation. Our findings reveal that the hydrodynamic fluctuations driven by active carpets have a region of influence that reaches orders of magnitude further in distance than the size of the organisms. These non-equilibrium fluctuations lead to a strongly enhanced diffusion of particles, which is anisotropic and space dependent. Fluctuations also facilitate encounters of particle pairs, which we quantify by analysing their velocity pair correlation functions as a function of distance between the particles. We found that the size of the particles plays a crucial role in their encounter rates, with larger particles situated near the active carpet being more favourable for aggregation. Overall, this research broadens our comprehension of aquatic systems out of equilibrium and how biologically driven fluctuations contribute to the transport of fundamental elements in biogeochemical cycles.

Secondary production and biomass in mussel assemblages relate to species richness and stream size but not life history

AbstractIncreases in species richness with habitat area (species–area relationship, or SAR) and increases in ecosystem function with species richness (biodiversity–ecosystem functioning, or BEF) are widely studied ecological patterns. Incorporating functional trait analysis into assemblage datasets may help clarify interpretations of SAR and BEF relationships in natural ecological systems. For example, life history theory can be used to make predictions about what species are most important in generating ecosystem function given a certain set of environmental conditions. We used quantitative assemblage data for freshwater mussels at nine sites in western Alabama, USA, to test for SAR and BEF relationships. At each site, we calculated species richness, mussel assemblage density, and two fundamental metrics of ecosystem function: biomass and secondary production. We also tested whether the proportional biomass and production contributions from species belonging to each of three life history strategies—opportunistic strategists adapted to unstable or frequently disturbed habitats, periodic strategists adapted to habitats subject to predictable large‐scale disturbances, and equilibrium strategists adapted to stable habitats—varied longitudinally with stream drainage area, a proxy for habitat area. Species richness increased with stream size (SAR), and both biomass and production increased with species richness (BEF) and mussel density. There were few longitudinal changes in the proportional contributions of the different life history strategy classifications that we used, but the invasive clam Corbicula fluminea contributed proportionally more biomass and production at sites that had smaller drainage areas. This study provides further evidence for a clear longitudinal SAR in stream‐dwelling taxa. It also suggests BEF relationships for biomass and secondary production in natural assemblages but underscores the importance of assemblage density in BEF studies that use observational field data. Variation in proportional biomass and production contributions by different life history strategies was likely limited by the size of the stream size gradient in our study, as contributions were uniformly high for species with life history traits better adapted to stable and productive habitats such as mid‐sized rivers with low or predictable hydrologic disturbance frequencies. This highlights the need to understand how organisms' functional traits govern their relationships to the environment at different scales.

Metamorphosis of EduX: a comparative study for education in the metaverse

This research explores critical aspects of the ongoing educational transition from traditional two-dimensional digital platforms toward immersive realities in the metaverse. In this work, a networked, collaborative, ubiquitous, and immersive platform, “EduX” is developed to host different educational sessions. Several metrics are tracked and compared with parallel sessions using traditional digital platforms used by educational institutions, particularly during and after the Covid-19 pandemic, such as Zoom, MS Teams, and Black Board Ultra as the Learning Management System. Four groups of students from different domains of knowledge (n = 396) participated in the study, whereby each group was split into two subgroups, taking their session via EduX and BBUltra, followed by post-assessments and discussions. Using a mixed methods approach, quantitative and qualitative data were collected and analyzed to examine the influence of the platform on engagement, performance, and overall student achievements. The discussed results demonstrate superior usability and user experience levels of the proposed platform, with higher levels of engagement, leading to significantly improved attainments. Other than demonstrating the efficacy and feasibility of the platform, this work establishes fundamental metrics to be considered by software and hardware solution providers, academic institutions, and the research community concerned with the convergence towards immersive technologies.

Assessing the Productivity of the Matang Mangrove Forest Reserve: Review of One of the Best-Managed Mangrove Forests

Mangrove ecosystems are crucial for biodiversity and coastal protection but face threats from climate change and human activities. This review assesses the productivity of the Matang Mangrove Forest Reserve (MMFR) in Malaysia, which is recognised as one of the best-managed mangrove forests, while also addressing challenges such as deforestation and climate change-induced factors. This review explores the concept of productivity in mangrove forests, highlighting their role in carbon sequestration and discussing litterfall measurements as fundamental metrics for assessing primary productivity. An analysis of historical changes in MMFR’s biomass and productivity revealed fluctuations influenced by logging, reforestation, and climatic conditions. Trends in MMFR productivity indicate a concerning decline attributed to anthropogenic activities such as aquaculture and industrial projects. A regression analysis conducted on Rhizophora apiculata data with age as the predictor and AGB as the response variable indicated a positive trend (slope = 3.61, R-squared = 0.686), suggesting a quantitative increase in AGB with age. Further analysis revealed a significant negative trend in MMFR’s overall productivity over years (coefficient = −3.974, p < 0.05) with a strong inverse relationship (rho = −0.818, p < 0.05), indicating declining AGB trends. Despite these challenges, this review underscores the significance of sustainable management practices, effective conservation efforts, and community engagement in maintaining mangrove ecosystem health and productivity. In conclusion, sharing management lessons from MMFR can contribute to global conservation and sustainable mangrove forest management efforts, fostering resilience in these vital ecosystems.

End-to-End Emulation of LoRaWAN Architecture and Infrastructure in Complex Smart City Scenarios Exploiting Containers.

In a LoRaWAN network, the backend is generally distributed as Software as a Service (SaaS) based on container technology, and recently, a containerized version of the LoRaWAN node stack is also available. Exploiting the disaggregation of LoRaWAN components, this paper focuses on the emulation of complex end-to-end architecture and infrastructures for smart city scenarios, leveraging on lightweight virtualization technology. The fundamental metrics to gain insights and evaluate the scaling complexity of the emulated scenario are defined. Then, the methodology is applied to use cases taken from a real LoRaWAN application in a smart city with hundreds of nodes. As a result, the proposed approach based on containers allows for the following: (i) deployments of functionalities on diverse distributed hosts; (ii) the use of the very same SW running on real nodes; (iii) the simple configuration and management of the emulation process; (iv) affordable costs. Both premise and cloud servers are considered as emulation platforms to evaluate the resource request and emulation cost of the proposed approach. For instance, emulating one hour of an entire LoRaWAN network with hundreds of nodes requires very affordable hardware that, if realized with a cloud-based computing platform, may cost less than USD 1.

Species richness estimation of the Afrotropical Darwin wasps (Hymenoptera, Ichneumonidae).

Species richness is one of the fundamental metrics of biodiversity. Estimating species richness helps spotlight taxonomic groups that are particularly under-studied, such as the highly diverse Darwin wasps. The only available estimate of the number of Darwin wasps in the Afrotropics proposed almost 11,000 species, compared to the 2,322 recorded species. However, it relied exclusively on the ratio of morphospecies to described species in Henry Townes' personal collection. We provide an updated estimate of the Afrotropical Darwin wasp species, using empirical data from multiple sources, including the increase in species numbers following generic revisions, morphospecies sorting in natural history collections, and diversity patterns of better-studied insects (butterflies) for extrapolation. Our analyses suggest that our knowledge of Darwin wasps is highly incomplete, with only 13-22% of species known in the five most extensively studied countries in the Afrotropics. We estimate 9,206-15,577 species of Darwin wasps within the entire Afrotropics, with the highest concentration expected in the Equatorial Afrotropics and Madagascar. Due to data constraints, our approach tends to underestimate diversity at each step, rendering the upper estimate (15,577 species) more realistic. We highlight reasons contributing to the gap between recorded and estimated species richness, including logistical and financial factors, as well as post-colonial influences.

Insights into PCI and DDCI as Key Metrics for Measuring Subnational Competitiveness in Vietnam

The purpose of this study is to review and understand Vietnam’s measurement of competitiveness for its local authorities. This paper delves into the application of two fundamental metrics, namely the Provincial Competitiveness Index (PCI) and the Department and District Competitiveness Index (DDCI). As Vietnam continues its trajectory of rapid economic development and regional integration understanding the dynamics of competitiveness at subnational levels has great importance. Drawing upon an overview of the competitiveness concept, case study, and perspectives, the article provides a holistic understanding of subnational competitiveness metrics in Vietnam. Results offer valuable insights for policymakers, researchers, and stakeholders involved in subnational development strategies and economic governance frameworks in Vietnam and beyond. This study also indicates future opportunities and challenges within research on pillars/indices, indicators and their impacts on creating the ease of doing business at subnational level.

Transformative Progress in Document Digitization: An In-Depth Exploration of Machine and Deep Learning Models for Character Recognition

Introduction: this paper explores the effectiveness of character recognition models for document digitization, leveraging diverse machine learning and deep learning techniques. The study, driven by the increasing relevance of image classification in various applications, focuses on evaluating Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning. The research employs a challenging French alphabet dataset, comprising 82 classes, to assess the models' capacity to discern intricate patterns and generalize across diverse characters. Objective: This study investigates the effectiveness of character recognition models for document digitization using diverse machine learning and deep learning techniques. Methods: the methodology initiates with data preparation, involving the creation of a merged dataset from distinct sections, encompassing digits, French special characters, symbols, and the French alphabet. The dataset is subsequently partitioned into training, test, and evaluation sets. Each model undergoes meticulous training and evaluation over a specific number of epochs. The recording of fundamental metrics includes accuracy, precision, recall, and F1-score for CNN, RNN, and VGG16, while SVM and KNN are evaluated based on accuracy, macro avg, and weighted avg. Results: the outcomes highlight distinct strengths and areas for improvement across the evaluated models. SVM demonstrates remarkable accuracy of 98.63%, emphasizing its efficacy in character recognition. KNN exhibits high reliability with an overall accuracy of 97%, while the RNN model faces challenges in training and generalization. The CNN model excels with an accuracy of 97.268%, and VGG16 with transfer learning achieves notable enhancements, reaching accuracy rates of 94.83% on test images and 94.55% on evaluation images. Conclusion: our study evaluates the performance of five models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning—on character recognition tasks. SVM and KNN demonstrate high accuracy, while RNN faces challenges in training. CNN excels in image classification, and VGG16, with transfer learning, enhances accuracy significantly. This comparative analysis aids in informed model selection for character recognition applications.

Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models

Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1].

Cyber security Reinforcement through Firewall Log Analysis and Machine Learning

Firewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT, MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00%. This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate a more dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.

Energy management strategies of hybrid renewable energy systems: A review

Burning fossil fuels results in more emissions than generating electricity from renewable sources. The transition to renewable energy from fossil fuels, which currently produce the majority of emissions, is essential to preventing the climatic disaster. Hybrid energy generation systems are still in their infancy. It is envisaged that future technology developments would lead to greater application and more economical goods. There will be more standardised designs, which will make it easier to select a system that is suitable for a certain application. The components will communicate more with one another. As a result, control, monitoring, and diagnosis will be made simpler. The hybrid energy system (HES), also known as hybrid power, is expected to be the long-term power solution for microgrid (MG) systems. This study compares and contrasts several theories and conventional approaches to controlling HRES’s control and energy consumption. A successful energy management strategy has been created using a variety of methods and procedures. The effectiveness of an EMS is determined by its control architecture and the solution approach used; common topologies include hierarchical, decentralised and centralised EMS. Supply side management and demand side management, two EMS components, will be discussed later. The three EMS control architectures are examined in this section. In order to determine the most practical and dependable solution with the lowest Net present cost (NPC), COE and realistic environmental consequences, various hybridisation cases of a PV panel, wind turbine, battery storage and diesel generator are designed, analysed and compared using DSM. The results of taking into account DSM indicated a reduction in CO2 emissions of 25%, NPC emissions of 14.8%, COE emissions of 14% and an increase in RF emissions of 8.5%. Two fundamental metrics – the DSM Quality Index for technical benefits and the DSM Appreciation Index for economic advantages – are used to assess the technical and economic benefits of DSM.

Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit

Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.

Source counts at 7.7–21 μm in CEERS field with JWST

ABSTRACT Source counts – the number density of sources as a function of flux density – represent one of the fundamental metrics in observational cosmology due to their straightforward and simple nature. It is an important tool that provides information on galaxy formation and evolution. Source counting is a direct measurement. Compared to advanced analyses that require more observational input such as luminosity/mass functions, it is less affected by any cosmological parameter assumptions or any errors propagated from luminosities. In this study, we present source counts at the six mid-infrared (MIR) bands, i.e. 7.7, 10, 12.8, 15, 18, and 21 μm from the MIR instrument of the JWST. Contrasted with the infrared source counts achieved by prior generations of infrared space telescopes, our source counts delve up to ∼100 times deeper, showcasing the exceptional sensitivity of the JWST, and aligning with the model predictions based on preceding observations. In a follow-up study, we utilize our source counts to establish a new IR galaxy population evolutionary model that provides a physical interpretation.

Blockchain-Based Federated Learning System: A Survey on Design Choices

The vanilla federated learning is made for a trusted environment, while in contrast, its actual use cases require collaborations in an untrusted setting. For this reason, using blockchain as a trusted platform to run federated learning algorithms has gained traction lately and has become a significant research interest. This paper performs a literature survey on state-of-the-art blockchain-based federated learning systems and analyzes several design patterns researchers often take to solve existing issues through blockchain. We find about 31 design item variations throughout the whole system. Each design is further analyzed to find pros and cons, considering fundamental metrics such as robustness, efficiency, privacy, and fairness. The result shows a linear relationship between fairness and robustness in which, if we focus on improving fairness, it will indirectly become more robust. Furthermore, improving all those metrics altogether is not viable because of the efficiency trade-off. Finally, we classify the surveyed papers to spot which designs are popular among researchers and determine which areas require immediate improvements. Our investigation shows that future blockchain-based federated learning systems require more effort regarding model compression, asynchronous aggregation, system efficiency evaluation, and the application for cross-device settings.

A practical method for connectivity and coverage reliability analysis for linear wireless sensor networks

Linear Wireless Sensor Networks (LWSNs) are the most suitable wireless sensor networks (WSNs) for monitoring the condition of infrastructures with linear typologies, e.g., railways, gas, or oil pipelines. Reliability of LWSNs is crucial for such applications due to their safety–critical or mission-critical nature. To this end, LWSNs of different designs need to be compared in terms of reliability to ascertain high-cost performance. Connectivity and coverage are fundamental metrics used to measure the reliability of LWSNs. However, to the best of our knowledge, there are no proper methods or tools to evaluate the reliability of LWSNs concurrently in terms of connectivity and coverage. In this paper, by reducing the size of system state space depending on the characteristics of LWSNs, we present a practical method for concurrent reliability analysis in terms of connectivity and coverage for LWSNs based on hybrid models of binary decision diagrams and divide-and-conquer schemes. The proposed method can be used to assess various LWSNs, e.g., networks with one or two sink nodes, as well as flat-based or cluster-based networks. Moreover, transmission and coverage ranges can be assigned to sensor nodes using this method. Case studies are presented to substantiate the viability of the proposed model, and some beneficial results are deduced, which can be considered to serve as basic principles for LWSN-design to ensure high-cost performance.

Investigative Assessment of the Impact of Perturbations on the Digital Terrestrial Television Broadcasting Signal

This study examines the effects of perturbations on the digital terrestrial television broadcasting (DTTB) signal in Jos, Plateau State, Nigeria, using measurement-based performance analysis. In order to examine the effects of environmental perturbations on DTTB signal, measurement campaign used the Nigerian Television Authority, Integrated Television Services (ITS) digital video broadcasting -second generation (DVB-T2) signal. The aim of the research was to identify the environmental and atmospheric perturbations with highest impact on DTTB signal. Field measurement was accurately obtained throughout the measurement campaign using the E8000 series spectrum analyzer, a DVB-T2 receiver, and other facilities in order to actualize the two important measurement characteristics, such as field strength and received signal strength (RSS). The outcome showed a significant amount of dips in route A, depriving towns surrounding rocky hills of a DVB-T2 signal, as demonstrated in the Du community with RSS values of -87.1 dBm, 85.2 dB , and -27.8 dB V/m, -23.9 dB V/m for field strength. Additionally, the results show that signal performance was better in the dry season compared to the rainy season (more primary coverage in the dry season compared to the rainy season), with the RSS value being -41.6 dBm versus -36.4 dBm and the field strength value being 34 dB/m versus 38.6 dB/m for route A, or the same on route B. Solving signal propagation issues requires understanding the fundamental metrics driving an increase in perturbations in any broadcast environment. Creating a model to address these issues will help with system evaluation, network planning, and broadcast service operation, ensuring quality of service (QoS).

Use of Mobile Technologies in the Healthcare Industry and Business: The Case of Pulmonary Function or Breathing Test Supportive Care

Digital spirometry, which is used to monitor and diagnose both acute and chronic lung diseases, has been the subject of research for many years. This research undertakes the initiative to build a novel mobile-based digital spirometry application helpful in diagnosing diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). As part of the application’s design, it consists of hardware and software components, including a pipe in which the user can breathe into, a pressure sensor that helps to collect patients’ breaths, an Arduino board that is be responsible for processing patients’ breaths received form the pressure sensor and convert the signal to the MIDI (Musical Instrument Digital Interface) message readable by the mobile application, and finally a spirometry application developed using android studio, JAVA programming language, as a user interface. Furthermore, the database applied in this project is SQLite which utilizes cloud-based storage technology rather than server-based models which might be costly. All parts work as a circuit unit that is capable of monitoring the user’s breathing system, and processing to provide an appropriate report of its performance. According to the findings, digital spirometry based on mobile application and MIDI protocol is practical and offers all the fundamental metrics that are supported by conventional spirometers.

Jamming and Link Selection for Joint Secrecy/Delay Guarantees in Buffer-Aided Relay System

This paper explores the joint secrecy and delay guarantees based on opportunistic jamming and link selection in a wireless relay system consisting of a source, a destination, multiple buffer-aided relays and a passive eavesdropper wiretapping over both hops. Based on the information of link state and buffer status, we design a novel transmission scheme based on link selection and jammer selection, which dynamically grants transmission links different priorities for packet delivery, such that the constraints on both secrecy outage probability and packet delay are jointly satisfied. To understand the performance of the new scheme, we then apply the bitmap technique and Markov chain theory to develop a complete theoretical framework for the modelling of three fundamental metrics, namely reliability outage probability, packet discarding probability and secrecy/delay constrained throughput (SDT). Finally, we provide extensive simulation and numerical results to validate our theoretical modelling, as well as to demonstrate that the proposed scheme is superior to the benchmarks in terms of SDT.

DESIGN OF AN AIRFOIL BY MATHEMATICAL MODELLING USING DATABASE


 
 
 The aerodynamic performance of low Reynolds airfoils with a Reynolds number less than 105 is critical for a variety of applications, including unmanned aerial vehicles, micro air vehicles, and low-speed/high-altitude aircraft. In general, most airfoils with Reynolds numbers less than 106 cannot be considered to have constant lift and drag characteristics. XFLR5 is a tool for analysing low-Reynolds-number airfoils, wings, and aircraft. It comes with XFOIL's direct and inverse analysis tools. The lifting line theory, the vortex lattice approach, and the 3-D panel method are all used to build and analyse wings. The lift coefficient (Cl) and pressure distribution (Cp) are fundamental metrics that determine the behaviour of airfoils. The cornerstone of aerodynamic analysis during aircraft development is pressure distribution data.
 
 

Global genetic diversity status and trends: towards a suite of Essential Biodiversity Variables (EBVs) for genetic composition.

ABSTRACTBiodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well‐being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within‐species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N e). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large‐scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long‐term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytical foundations of Genetic EBVs are well developed, and conservation practitioners should anticipate their increasing application as efforts emerge to scale up genetic biodiversity monitoring regionally and globally.