Observation Data Research Articles

Governing the paradox of success in a hybrid supporter-owned professional football club

ABSTRACT Research question Using insights from success paradoxes and hybrid institutional logics, we analyse the institutional work undertaken to construct a novel governance structure in a supporter owned professional football club. Research methods A single exploratory case study design was adopted, focusing on the transition from existential crisis to supporter ownership. A mixed method strategy involved collecting participative observation, primary interview and secondary documentary data. Results and findings We observed a shared understanding of the overlapping priorities for the Club and (supporter) Foundation that formed the basis for collaboration. Ambiguity as to what constituted success, and awareness of latent paradoxes contributed to the institutional work necessary to develop a resilient governance structure. A distinctive aspect arose from harnessing the productive friction between the corporate and professional logics of the Club, and the community and family logics of the Foundation. Implications Institutional work undertaken in creating a new governance structure shows the potential within clubs for cooperation between different actors, demonstrating the mediating influence arising from mutual dependency between actors. When there is a high level of co-dependencies between the club and stakeholders, and contestation over what constitutes success, tensions between logics can be leveraged to benefit the hybrid organisation. While any governance solution needs to reflect a club’s specific context, the adopted governance structure should integrate resilience, mutual dependencies, business acumen, inclusion, emotion, engagement and accountability.

Unexpected stratification in the equivalent-duration distributions of flare stars

ABSTRACT In this study, the results obtained from the statistical models of flares observed with ground-based telescopes and TESS observations for the UV Ceti-type stars AD Leo and V1005 Ori are presented. The distribution of flare equivalent duration versus total flare duration for the flares of both stars, observed with different telescopes, has been modelled using the one-phase exponential association (OPEA) model. Although no bifurcation was observed in the models derived with the flare data of V1005 Ori, the OPEA model exhibited bifurcation in almost all observation data of AD Leo. While many similar studies have shown that such models derived for a star do not vary over time and exhibit a single distribution in the same observation season, the stratification in AD Leo’s distributions and consequently in its model is an unexpected result.

Quantitatively detecting ground surface changes of slope failure caused by heavy rain using ALOS-2/PALSAR-2 data: a case study in Japan

Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.

Meningkatkan Keaktifan Siswa Kelas IV Fase B dalam Pembelajaran Agama Katolik Melalui Model Problem-Based Learning (PBL) di SDN 15 Teluk Pongkal

This study aims to enhance the activity of fourth-grade students in Phase B of Catholic Religious Education at SDN 15 Teluk Pongkal through the implementation of the Problem-Based Learning (PBL) model. PBL was chosen because it encourages students to think critically and actively participate in the learning process. In this learning approach, students are engaged in solving problems that are relevant to their daily lives. The research findings indicate that the average test score of students significantly improved from 69 in Cycle 1 to 80 in Cycle 2. This increase reflects the effectiveness of the PBL model in enhancing the learning process. Additionally, the P3 observation data showed that student activity also rose, with an average of 53% in Cycle 1 and increasing to 67% in Cycle 2. Thus, the implementation of the PBL model not only improves students' academic scores but also strengthens their engagement in learning. Through this approach, students are expected to become more active, critical, and creative in addressing various problems they encounter. This study contributes significantly to the development of more interactive and engaging teaching methods, especially in the context of Catholic education.

DC_OCEAN: an open-source algorithm for identification of duplicates in ocean databases

A high-quality hydrographic observational database is essential for ocean and climate studies and operational applications. Because there are numerous global and regional ocean databases, duplicate data continues to be an issue in data management, data processing and database merging, posing a challenge on effectively and accurately using oceanographic data to derive robust statistics and reliable data products. This study aims to provide algorithms to identify the duplicates and assign labels to them. We propose first a set of criteria to define the duplicate data; and second, an open-source and semi-automatic system to detect duplicate data and erroneous metadata. This system includes several algorithms for automatic checks using statistical methods (such as Principal Component Analysis and entropy weighting) and an additional expert (manual) check. The robustness of the system is then evaluated with a subset of the World Ocean Database (WOD18) with over 600,000 in-situ temperature and salinity profiles. This system is an open-source Python package (named DC_OCEAN) allowing users to effectively use the software. Users can customize their settings. The application result from the WOD18 subset also forms a benchmark dataset, which is available to support future studies on duplicate checks, metadata error identification, and machine learning applications. This duplicate checking system will be incorporated into the International Quality-controlled Ocean Database (IQuOD) data quality control system to guarantee the uniqueness of ocean observation data in this product.

Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations

Earth observation data is key for monitoring vegetation dynamics across temporal and spatial scales. The most widely used method to estimate vegetation properties from Earth observation data is vegetation indices. However, temporal dynamics in vertical leaf angles can strongly alter reflectance signals and, hence, vegetation indices. Here, we derive leaf angles from plant photographs to simulate the effect on vegetation indices with radiative transfer models. We show that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, we demonstrate that these effects are not random but tightly linked to abiotic environmental conditions. These systematic effects of vertical leaf angles have implications for monitoring plant properties, biodiversity, and ecosystem functional properties. We discuss the related challenges and opportunities to assess spatio-temporal vegetation dynamics with remote sensing and vegetation indices.

Investigating trends and causes of simultaneous high pollution from PM2.5 and ozone in China, 2015–2023

The spatiotemporal variation characteristics of simultaneous high pollution (SHP) from surface fine particulate matter (PM2.5) and ozone (O3) were explored on the basis of hourly observation data in 1092 sites from 2015–2023 across China. Statistics show that the frequency and intensity of SHP days are highest in North China, followed by South China. Seasonally, SHP days occur more frequently in spring and autumn across China, accounting for 43% and 27% over nine years, respectively. The number of SHP days across national sites first decreased (by 48.63% from 2015–2021) but then increased (by 49.37% from 2021–2023). Gridded PM2.5 component data were analyzed to explore these variations. Despite reductions in anthropogenic emissions, the proportion of secondary inorganic components in PM2.5 increased by 4.51%, 3.6%, and 5.41% in Beijing-Tianjin-Hebei, FenWei Plain, and Yangtze River Delta on SHP days from 2015–2021. Concurrently, the detrended PM2.5 and O3 concentrations showed significant positive correlations in most regions. These phenomena may be related to both high O3 concentrations and meteorological conditions. The weather analysis reveals that specific weather conditions contribute to the occurrence of SHP, with notable spatial variations: cyclonic low-pressure systems with high temperatures in northern China, anticyclonic high-pressure systems with high temperatures and low humidity in central China, and east winds in southern China. These conditions may promote secondary pollutant formation and hinder pollutant dispersion. In addition, the increase in primary PM2.5 on SHP days after 2021 underscores the importance of controlling anthropogenic emissions. Overall, a comprehensive approach is essential to tackle the SHP challenge.

Development and Validation of a Novel Nomogram for Predicting Mechanical Ventilation After Cervical Spinal Cord Injury

ObjectiveTo investigate the risk factors relating to the need for mechanical ventilation (MV) in isolated patients with cervical spinal cord injury (cSCI) and to construct a nomogram prediction model. DesignRetrospective analysis study. SettingNational Spinal Cord Injury Model System Database (NSCID) observation data were initially collected during rehabilitation hospitalization. ParticipantsA total of 5784 patients (N=5784) who had a cSCI were admitted to the NSCID between 2006 and 2021. InterventionsNot applicable. Main Outcome Measure(s)A univariate and multivariate logistic regression analysis was used to identify the independent factors affecting the use of MV in patients with cSCI, and these independent influencing factors were used to develop a nomogram prediction model. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the efficiency and the clinical application value of the model, respectively. ResultsIn a series of 5784 included patients, 926 cases (16.0%) were admitted to spinal cord model system inpatient rehabilitation with the need for MV. Logistic regression analysis demonstrated that associated injury, American Spinal Cord Injury Association Impairment Scale (AIS), the sum of unilateral optimal motor scores for each muscle segment of upper extremities (sUEM), and neurologic level of injury (NLI) were independent predictors for the use of MV (P<.05). The prediction nomogram of MV usage in patients with cSCI was established based on the above independent predictors. The AUROC of the training set, internal verification set, and external verification set were 0.871 (0.857-0.886), 0.867 (0.843-0.891), and 0.850 (0.824-0.875), respectively. The calibration curve and DCA results showed that the model had good calibration and clinical practicability. ConclusionsThe nomograph prediction model based on sUEM, NLI, associated injury, and AIS can accurately and effectively predict the risk of MV in patients with cSCI, to help clinicians screen high-risk patients and formulate targeted intervention measures.

Improving the Accuracy of Building Damage Estimation Model Due to Earthquake Using 10 Explanatory Variables

Aiming to support disaster recovery, we have developed a new method to extract damaged buildings by using machine learning that combines 10 explanatory variables obtained from analysis of aerial photographs and observation data. We used site amplification factors, seismic intensities of foreshock and mainshock, distance from faults, estimated building structures and ages, coverage by blue tarps, texture analysis, and digital surface model differences before and after the earthquake as explanatory variables, in addition to convolutional neural network prediction results based on post-earthquake aerial photographs. The random forest method resulted in an overall accuracy of about 81% and an average F-measure of three classes was about 70%, indicating that it can classify possible damage to buildings more accurately than our previous studies.

Reconstructing 169 years of historical atmospheric and hydrologic conditions in the American River Watershed through the Watershed Environmental Hydrology Hydro-Climate Model system

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987–1992 and 2012–2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed.

Multitasking during Medication Management in a Nursing Home: A Time Motion Study.

Multitasking, defined as performing two or more interventions simultaneously, increases the cognitive burden of clinicians. This may, in turn, lead to higher risk of medication and procedural errors. Time motion study (TMS) data for nurses in nursing homes revealed an extensive amount of multitasking while managing medications. Further investigation of multitasked nursing interventions will provide a foundation for optimizing medication management workflows. Using a continuous observational TMS method, this study aimed to describe pairs of multitasked nursing interventions associated with medication management interventions, including preparing and administering medications, assessing medication effects, instructing on medications, and documenting medication administration. An external nurse observer used 57 predefined Omaha System nursing interventions embedded within TimeCaT (version 3.9), TMS data recording software to collect observation data in a single nursing home. A total of 120 hours of time-stamped observation data from nine nurses were downloaded from TimeCaT and analyzed using descriptive and inferential statistics. The majority (74%) of medication management interventions were multitasked, resulting in 2,003 pairs of multitasked interventions. Of the 57 Omaha System nursing interventions, 35 were involved in these multitasking pairs. When nurses multitasked, the average duration of medication preparation was longer (non-multitasked: 81 seconds; multitasked: 162 seconds, p < 0.05), while the average duration of medication administration record documentation was shorter (non-multitasked: 93 seconds; multitasked: 66 seconds, p < 0.05). The findings reveal the complexity of medication management in nursing homes with numerous and diverse multitasking pairs. Findings provide a platform for in-depth study of medication management multitasking in the clinical context, and inform future efforts to create clinical and informatics solutions to optimize medication management workflow. This method may be also applied to examine medication management and multitasking in other clinical settings.

Process‐Based Modeling of Ecosystem‐Level Monoterpene From a Japanese Larch (Larix kaempferi) Forest

AbstractGlobally, the emission of biogenic volatile organic compounds (BVOC) by plants represents the dominant source of volatile organic compounds emitted to the atmosphere. Monoterpenes, as the major BVOC group, can contribute to forming secondary organic aerosols and influence cloud properties. In this study, we developed a process‐based monoterpene module in the Vegetation Integrative SImulator for Trace gases (VISIT) model by considering the production, storage, and emission of monoterpene as three main processes. We further evaluated the modeled monoterpene emissions against the ecosystem‐level observation data at a half‐hour scale at a Japanese larch (Larix kaempferi) forest site on Mt. Fuji, Japan. The VISIT model performed with relatively higher accuracy with a Willmott's index of agreement at 0.61, a mean bias error (MBE) at 0.29, and a root mean squared error (RMSE) at 0.43, comparable to that of Model of Emissions of Gases and Aerosols from Nature model with a Willmott's index of agreement at 0.63, a MBE at 0.40, and a RMSE at 0.54. In a long‐term simulation under high CO2 emission scenarios, the ratio between monoterpene emission and gross primary production exhibited a stronger correlation with CO2 concentration than temperature. Our study provides a process‐based modeling approach for more accurately simulating monoterpene emissions from Japanese larch.

Hunting for Sulfur-containing Molecules in Space: A Spectroscopic Characterization of Cyclopropenethione Based on High-level Ab Initio Calculations

Cyclopropenethione falls into the class of complex organic molecules but has not yet been observed in the interstellar medium or any circumstellar disks. However, its existence is very likely, and thus this study provides high-level ab initio predictions, which may serve as reference data for future observations or experimental work. Rovibrational configuration interaction theory based on multidimensional potential energy surfaces being obtained from explicitly correlated coupled-cluster calculations has been used to predict the fundamental vibrational modes, the microwave spectrum, and low-lying rovibrational transitions. Rotational constants as well as quartic and sextic centrifugal distortion constants were obtained from vibrational perturbation theory.

Comparative Analysis of Biblical Statements and Contemporary Science Views

This comparative analysis delves into the intricate relationship between Biblical science views and contemporary scientific perspectives. Examining biblical narratives through the lens of modern scientific understanding, the study navigates the complexities inherent in reconciling ancient wisdom with empirical methodologies. The analysis encompasses key themes such as the creation narrative, cosmology, and natural phenomena, emphasizing the symbolic richness and cultural contexts embedded in biblical texts. Acknowledging the Bible’s role as a sacred and moral guide, the study contrasts it with the evidence-based approaches of contemporary science. While the Bible offers timeless wisdom and moral teachings, its scientific views often reflect the understanding of the ancient world. Contemporary science, grounded in observation, experimentation, and empirical data, explores the natural world’s intricacies with a methodological rigor distinct from religious texts. The dialogue between these perspectives underscores the importance of recognizing the distinct purposes and realms of religious and scientific knowledge. Recommendations include fostering interdisciplinary collaborations, promoting educational initiatives that integrate both perspectives, and facilitating public dialogues to enhance mutual understanding. Ethical reflections derived from shared principles offer common ground, while continued research into the historical and cultural contexts of biblical texts contributes to nuanced interpretations. Ultimately, the study advocates for an inclusive and enlightened approach that nurtures a harmonious coexistence between faith and reason. By implementing these recommendations, it aims to create an environment fostering dialogue and collaboration between theologians, scientists, and the wider public. Through such efforts, a richer understanding of the universe and the diversity of human knowledge emerges, embracing the complexities of existence and celebrating the shared pursuit of wisdom.

Impact of Reservoir Heterogeneity on Bitumen Content in the Mackay River Oil Sands, Athabasca (Canada)

AbstractThe Lower Cretaceous Manville Group of Upper McMurray Formation is one of the main bitumen reservoirs in Athabasca. In this study, the relationship between reservoirs heterogeneity and bitumen geochemical characteristics were analyzed through core and microscopic observation, lab analysis, petrophysics and logging data. Based on the sedimentology framework, the formation environment of high‐quality oil sand reservoirs and their significance for development were discussed. The results indicate that four types lithofacies were recognized in the Upper McMurray Formation based on their depositional characteristics. Each lithofacies reservoirs has unique physical properties, and is subject to varying degrees of degradation, resulting in diversity of bitumen content and geochemical composition. The tidal bar (TB) or tidal channel (TC) facies reservoir have excellent physical properties, which are evaluated as gas or water intervals due to strong degradation. The reservoir of sand bar (SB) facies was evaluated as oil intervals, due to its poor physical properties and weak degradation. The reservoir of mixed flat (MF) facies is composed of sand intercalated with laminated shale, which is evaluated as poor oil intervals due to its poor connectivity. The shale content in oil sand reservoir is very important for the reservoir physical properties and bitumen degradation degree. In the context of regional biodegradation, oil sand reservoirs with good physical properties will suffer from strong degradation, while oil sand reservoirs with relatively poor physical properties are more conducive to the bitumen preservation.

Spatial and seasonal variability of chlorophyll-a, total suspended matter, and colored dissolved organic matter in the Sundarban mangrove forest using earth observation and field data

The Sundarbans, the world's largest mangrove forest, confronts potential threats from various anthropogenic activities leading to degradation of its aquatic ecosystem. To examine the current status of the aquatic ecosystem, this study aimed to evaluate the spatial and seasonal fluctuation of three principal water quality attributes namely Chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and Colored Dissolved Organic Matter (CDOM) in the complex tidal river systems of the Sundarban mangroves forest using earth observation and in-situ data. A set of two bio-optical algorithms, Ocean color-2 (OC-2) and Ocean color-3 (OC-3), were applied to measure Chl-a concentration, Green/NIR and the Red/NIR band ratio algorithms were used for TSM and the Case-2 Regional Coast Color (C2RCC) processor in the SNAP software was applied to obtain CDOM concentration in study area. A total of 50 in-situ samples were collected during post-monsoon and pre-monsoon to validate the results. Our results clearly demonstrated seasonal variability with higher Chl-a concentrations in post-monsoon than pre-monsoon. This was due to the OC-2 algorithm which produced better results with R2 = 0.73, RMSE = 0.27 for post-monsoon and R2 = 0.55, RMSE = 0.32 for pre-monsoon. Whilst, TSM concentration performed the best with R2 = 0.77; RMSE = 15.82 and R2 = 0.65; RMSE = 33.96 in post-monsoon and pre-monsoon according to the Green/NIR band ratio method. The nearshore and narrow waterway regions had the highest concentrations of TSM and Chl-a, whereas the offshore regions had the lowest. Strong association were observed between the in-situ and satellite derive absorption coefficient, aCDOM (m−1). The R2 for a CDOM during pre-monsoon was 0.65 and throughout the post-monsoon, it was 0.74. Pre-monsoon concentrations were found to be higher due to marine sources and higher wind speeds, possibly due to sediment resuspension. This kind of baseline evaluation will help to detect threats, direct preventive measures for the protection of biodiversity, and deepen our knowledge of these distinct ecosystems. The results will help develop flexible management and preservation plans that can adjust to both natural and man-made changes.

Assessment of the Weimer Geomagnetic Perturbation Model for High‐Latitude Positioning and Navigation

AbstractThe Geomagnetism Group at the National Centers for Environmental Information (NCEI), in collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES), along with various government and industry partners, specializes in developing and providing access to Earth's internal magnetic field models. These models are crucial for applications such as compass navigation and wellbore positioning, offering reference values for the geomagnetic field (total field, dip or inclination, and declination) at any location across the Earth. This study assesses the Weimer geomagnetic perturbation model, proposed by Weimer in 2013, which is an empirical model of magnetic field variations at high latitudes driven by solar wind measurements, as a candidate for capturing geomagnetic field variations in high‐latitude areas. We compare the geomagnetic field variations predicted by the Weimer model against data from the INTERMAGNET and SuperMAG observatory networks. Our findings indicate that the Weimer model achieves a reduction in the standard deviations of high‐latitude magnetic field variations by approximately 20%–30% once quiet‐time baselines are removed from the data set. Furthermore, we compare the performance of the Weimer model against the Space Weather Modeling Framework's Geospace model during specific geomagnetic storms in 2017 and 2018, and we find that the Weimer model is more effective in predicting magnetic variations at high latitudes than the Geospace model during these storms. Additionally, comparisons to magnetometer data collected from high‐latitude directional‐drilling operations align with the trends observed in comparisons with INTERMAGNET and SuperMAG observatory data, confirming the Weimer model's reliability and effectiveness for such applications.

Pengembangan Evaluasi Pembelajaran Berbantuan Wordwall Materi Melaksanakan Pengurusan Jenazah Untuk Kelas 11 SMA Negeri 12 Luwu Utara

The research discusses the instrument for developing learning evaluations assisted by wordwall material for carrying out corpse management for class Types of R&amp;D (Research and Development) research, ADDIE model: Analysis (analysis), Design (design), Development (development), Implementation (implementation), Evaluation (evaluation). The subject is a class XI IPA student at SMAN 12 North Luwu. Observation data collection techniques, questionnaires and documentation. Data analysis techniques, qualitative descriptive analysis and quantitative descriptive analysis. The research results showed that media experts were 95% in the "very valid" category, language validation was 95% in the "very valid" category, and PAI learning evaluation expert validation was 92% in the "very valid" category. The practicality of the product is 89.2% in the "very practical" category. Based on this, a summary of the development of a learning evaluation instrument assisted by wordwall material for carrying out corpse management for class XI at SMAN 12 North Luwu, is suitable for use as an evaluation instrument.

Data Cubes and Cloud-Native Environments for Earth Observation: An Overview

Reliable access to analysis-ready Earth observation data and infrastructures for processing them has been a challenge with the increasing volumes and variety of data being generated daily through various Earth observation programmes. Recently, concepts centered around building cloud-native infrastructures that provide access to Earth observation data in efficient manners such as data cubes which facilitate rapid querying, filtering and retrieval have been garnering popularity. Moreover, efficient means of processing such vast volumes of data stored in data cubes through cloud computing frameworks such as Kubernetes are becoming more popular. This paper investigates the current state of the art techniques, methods and technologies used in cloud-native environments with a particular focus on the data cube initiative and "bring the user to the data" paradigm.

Constraining the Origin of the Nanohertz Gravitational-wave Background by Pulsar Timing Array Observations of Both the Background and Individual Supermassive Binary Black Holes

The gravitational waves (GWs) from supermassive binary black holes (BBHs) have long been sought by pulsar timing array (PTA) experiments, in the forms of both a stochastic GW background (GWB) and individual sources. Evidence for a GWB was reported recently by several PTAs with origins to be determined. Here we use a BBH population synthesis model to investigate the detection probability of individual BBHs by the Chinese PTA (CPTA) and the constraint on the GWB origin that may be obtained by PTA observations of both GWB and individual BBHs. If the detected GWB signal is entirely due to BBHs, a significantly positive redshift evolution (∝ (1 + z)2.07) of the mass scaling relation between supermassive black holes and their host galaxies is required. In this case, we find that the detection probability of individual BBHs is ∼85% or 64% if using a period of 3.4 yr of CPTA observation data, with an expectation of ∼1.9 or 1.0 BBHs detectable with a signal-to-noise ratio ≥3 or 5, and it is expected to increase to >95% if the observation period is extended to 5 yr or longer. Even if the contribution from BBHs to the GWB power signal is as small as ∼10%, a positive detection of individual BBHs can still be expected within an observation period of ∼10 yr. A nondetection of individual BBHs within several years from now jointly with the detected GWB signal can put a strong constraint on the upper limit of the BBH contribution to the GWB signal and help identify/falsify a cosmological origin.