Rapid Estimation Research Articles

Exploring the Synergistic Association Between Oral Health Status and Oral Health Literacy Among College Students: A Cross-Sectional Study.

Oral health is often viewed as a significant component for an indication of good general health or for good well-being together with a decent quality of life. Health literacy is considered a crucial factor in improvising a good life or excellent health. Oral health literacy (OHL) is the ability necessary for people to identify the factors that lead to poor oral health, learn and put into practice the essentials of effective oral self-care behaviors, and communicate with oral healthcare professionals in order to schedule appointments, put their names on waiting lists for dental care, and locate the dentist's office. To evaluate students' oral health, their OHL, to determine the relationship between their oral health status and OHL, and finally to suggest preventive measures for the benefit of public health. At Teerthanker Mahaveer University, a cross-sectional study involving 1500 participants, ages 18 to 25, was conducted on students studying nursing, physiotherapy, paramedicine, engineering, and law. Their informed consent was obtained. The Rapid Estimate of Adult Literacy in Dentistry (REALD-99) was used to gauge OHL levels, and the WHO's 1997 Oral Health Survey was used for their clinical evaluation. The mean REALD score was significantly higher in nursing students (88.32±6.46), followed by physiotherapy college (82.46±9.11), paramedical college (70.54±10.95), law college (46.52±7.74), and least in engineering college (38.80±10.65). The difference in the REALD score based on college was statistically significant. Along with this, the REALD score showed a correlation with gender and location too. Except for fluorosis, all the clinical parameters of dental caries, gingival bleeding, and pockets, loss of attachment, dental fluorosis, and dental enamel were associated with OHL. The results of the current study showed a relationship between educational attainment, clinical parameters examined, and OHL, leading to the conclusion that higher OHL is related to better oral health. So, we can conclude that maintaining good oral health requires OHL.

Preliminary Mechanical Evaluation of Grouting Concrete as a Protective Layer for Tunnelling.

The aim of this study is to introduce a protective layer to safeguard tunnel structures. In practice, one viable approach to create this protective layer between the tunnel structure and surrounding rocks is to pump the material during tunnel construction. The primary components of the proposed material are porous sand, rubber, and cement. Static and dynamic experiments were conducted to assess the unconfined compressive strength (UCS), flexural stiffness, and compaction resistance at various mixing ratios. The results indicate that the addition of porous sand decreases the UCS compared to the solid sand under similar mixing conditions. The addition of rubber offers the elasticity, thereby enhancing the compaction resistance. However, increasing the rubber content compromises UCS. Furthermore, this study presents a linear equation to predict the 7-day UCS, which can be used as a rapid estimation for UCS, flexural stiffness, and compaction resistance of the proposed material. It is important to note that this study only investigates the fundamental mechanical properties of the proposed material, and further comprehensive research is necessary to fully understand its workability, durability, and other behaviour before practical application.

Spatial, temporal, and social dynamics in visitation to U.S. national parks: A big data approach

The degree to which many benefits of national parks are realized hinges on public access. Traditional methods in estimating park visitation can be time-consuming, costly, and labor-intensive. Fortunately, the growing availability of ‘big data’ offers new opportunities for rapid and large-scale estimation. This study investigated the spatiotemporal dynamics in visitation to all National Park Service (NPS) units in the contiguous U.S. and explored associated sociodemographic factors using fine-grained SafeGraph mobile device location data. We found that people tend to travel less frequently and in a shorter distance to visit NPS units in both 2020 and 2021 vs. 2019. During the pandemic, people preferred visiting national parks and nature-based NPS units over culture-oriented ones relative to pre-pandemic levels. Most importantly, this study highlights a long-standing social inequity – the groups of minorities (i.e., Blacks, Asians, and Hispanics) and lower-income were less likely to visit NPS units than Whites and higher-income, respectively.

Fine-Resolution Forest Height Estimation by Integrating ICESat-2 and Landsat 8 OLI Data with a Spatial Downscaling Method for Aboveground Biomass Quantification

Rapid and accurate estimation of forest aboveground biomass (AGB) with fine details is crucial for effective forest monitoring and management, where forest height plays a key role in AGB quantification. In this study, we propose a random forest (RF)-based down-scaling method to map forest height and biomass at a 15-m resolution by integrating Landsat 8 OLI and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data. ICESat-2 photon data are used to derive canopy parameters along 15-m segments, which are considered sample plots for the extrapolation of discrete forest height. Fourteen variables associated with spectral features, textual features and vegetation index are extracted from pan-sharpened Landsat 8 images. A regression function is established between these variables and ICESat-2-derived forest height to produce a 15-m continuous forest height distribution data based on the 30-m forest height product using the RF algorithm. Finally, a wall-to-wall forest AGB at 15-m spatial resolution is achieved by using an allometric model specific to the forest type and height. The Jilin Province in northeast China is taken as the study area, and the forest AGB estimation results reveal a density of 61.15 Mg/ha with a standard deviation of 89.46 Mg/ha. The R2 between our predicted forest heights and the ICESat-2-derived heights reaches 0.93. Validation results at the county scale demonstrate reasonable correspondence between the estimated AGB and reference data, with consistently high R2 value exceeding 0.65. This downscaling method provides a promising scheme to estimate spatial forest AGB with fine details and to enhance the accuracy of AGB estimation, which may facilitate carbon stock measurement and carbon cycle studies.

Associations Between Oral Health Literacy and Oral Health Status Among Patients Attending Comprehensive Dental Clinic

Objectives: This study aimed to determine the association between oral health literacy (OHL), oral health status (OHS), and utilization of dental services among maintenance patients. Methods: The study design was a cross-sectional study. Participants were maintenance patients attending the Comprehensive Dental Clinic, Faculty of Dentistry, Chiang Mai University from January to April 2021. Information about the participant’s demographic details, including education, and income were collected using a questionnaire. The Decayed-Missing-Filled Teeth index (DMFT), Plaque Index (PI), and utilization of dental service were obtained from the electronic dental record. OHL was assessed using the Thai Version of Rapid Estimate of Adult Literacy in Dentistry (ThREALD-30). Data were analyzed using Spearman correlation and binary logistic regression. Results: A total of 130 participants were recruited for this study. The mean age of participants was 51.1 years. The mean ThREALD-30 score was 26.7±3.76. The OHL scores were significantly negatively correlated with DMFT (r=-0.356; p= 0.002), PI (r=-0.398; p<0.001), and utilization of dental services (r=-0.35, p<0.001). After adjusting for age, gender, education and income, OHL was significantly associated with DMFT (OR=1.36; p=0.001), and PI (OR=1.26; p=0.006). Conclusions: OHL was associated with OHS. Patients with high OHL had good OHS with regular annual dental examinations. An understanding of the OHL of patients is crucial for designing effective oral health education and creating intervention programs to promote oral health

Quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion.

Laser speckle contrast imaging (LSCI) is a widefield imaging technique that enables high spatiotemporal resolution measurement of blood flow. Laser coherence, optical aberrations, and static scattering effects restrict LSCI to relative and qualitative measurements. Multi-exposure speckle imaging (MESI) is a quantitative extension of LSCI that accounts for these factors but has been limited to post-acquisition analysis due to long data processing times. Here we propose and test a real-time quasi-analytic solution to fitting MESI data, using both simulated and real-world data from a mouse model of photothrombotic stroke. This rapid estimation of multi-exposure imaging (REMI) enables processing of full-frame MESI images at up to 8 Hz with negligible errors relative to time-intensive least-squares methods. REMI opens the door to real-time, quantitative measures of perfusion change using simple optical systems.

Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China

Soil inorganic carbon (SIC) is the primary component of the soil carbon pool in arid and semiarid regions and strongly impacts the global carbon cycle, ecosystem services, and soil functions. The global climate change and intensify of human activities, could substantially change SIC, which highlights the importance of monitoring SIC. Rapid and accurate estimation of SIC concentration is critical for soil inorganic carbon pool monitoring. Currently, visible near-infrared (Vis-NIR) spectroscopy is a promising technique for estimating SIC via a rapid and cost-effective manner. Thus, in this study, we collected 315 topsoil samples from the Alar Reclamation Area in South Xinjiang, China, and measured their Vis-NIR spectra and SIC content. Then, we used deep learning algorithms, including a one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), long short-term memory network (LSTM), and deep belief network (DBN), combined with variable selection algorithms (particle swarm algorithm (PSO), interval random frog (IRF), competitive adaptive reweighting algorithm (CARS), ant colony algorithm (ACO), and iteratively retaining informative variables (IRIV) to estimate SIC. Results showed that all five variable selection algorithms could effectively extract the featured spectral information of SIC, and reduce the number of spectral variables by >97%, simplifying the model structure. The variable selection algorithm could markedly improve the SIC spectral estimation accuracy, and the corresponding estimation accuracy follows the order: IRF > IRIV > PSO > CARS > ACO. All four deep learning models have high prediction accuracy, and the modeling accuracy of each method follow the order: LSTM > 1D-CNN > 2D-CNN > DBN. The combined IRF and LSTM model achieved the highest estimation accuracy (R2 = 0.93, RMSE = 1.26 g kg−1 in the calibration dataset; R2 = 0.92, RMSE = 1.37 g kg−1 in the validation dataset). This study demonstrated that deep learning combined with variable selection algorithms can detect SIC content quickly and accurately.

Optimal serving strategy for vehicle-to-grid business: service agreement, energy reserve estimation, and profit maximization

To advocate the adoption of electric vehicle (EV) technologies, this paper studies a practical operating paradigm for running a charge park vehicle-to-grid (V2G) service business. The operating paradigm consists of a service agreement and two consecutive day-ahead analyses. Specifically, i) the service agreement underpins the communication pattern between the EV owners and the V2G service operator, ii) day-ahead analysis-I estimates the V2G energy reserve distribution, and iii) day-ahead analysis-II aims to maximize profit by optimizing decision variables for the scheduled day, such as the output period and sale price of the V2G energy. Correspondingly, the contributions of this paper are three-fold. First, the major principles behind the design of a service agreement are highlighted, and a practical service agreement that abides by the principle is formed. Second, for day-ahead analysis-I, this paper proposes a V2G energy reserve modeling method for the rapid estimation of V2G energy reserve distribution, which is applied to a case study of New York City working and recreational environment charge parks. Third, for day-ahead analysis-II, an evaluation framework is proposed, which provides various metrics for characterizing the V2G output capacity. The metrics evaluation and profit maximization methods are presented with theoretical results and are verified also by computer experiments. For example, we show that for the simulated environment with peak time-of-use hours [13:00, 16:00] and V2G output period [13:00, 14:00], the V2G output power threshold 61 kW selected by our method achieves almost the maximum scheduled day profit (true maximum achieved at 60 kW).

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

PurposeSlope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.Design/methodology/approachWithin the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.FindingsGenerally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.Originality/valueAlthough machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

Perspectives About Racism and Patient-Clinician Communication Among Black Adults With Serious Illness

Black patients with serious illness experience higher-intensity care at the end of life. Little research has used critical, race-conscious approaches to examine factors associated with these outcomes. To investigate the lived experiences of Black patients with serious illness and how various factors may be associated with patient-clinician communication and medical decision-making. In this qualitative study, one-on-one, semistructured interviews were conducted with 25 Black patients with serious illness hospitalized at an urban academic medical center in Washington State between January 2021 and February 2023. Patients were asked to discuss experiences with racism, how those experiences affected the way they communicated with clinicians, and how racism impacted medical decision-making. Public Health Critical Race Praxis was used as framework and process. The experience and of racism and its association, as described by Black patients who had serious illness, with patient-clinician communication and medical decision-making within a racialized health care setting. A total of 25 Black patients (mean [SD] age, 62.0 [10.3] years; 20 males [80.0%]) with serious illness were interviewed. Participants had substantial socioeconomic disadvantage, with low levels of wealth (10 patients with 0 assets [40.0%]), income (annual income <$25 000 among 19 of 24 patients with income data [79.2%]), educational attainment (mean [SD] 13.4 [2.7] years of schooling), and health literacy (mean [SD] score in the Rapid Estimate of Adult Literacy in Medicine-Short Form, 5.8 [2.0]). Participants reported high levels of medical mistrust and high frequency of discrimination and microaggressions experienced in health care settings. Participants reported epistemic injustice as the most common manifestation of racism: silencing of their own knowledge and lived experiences about their bodies and illness by health care workers. Participants reported that these experiences made them feel isolated and devalued, especially if they had intersecting, marginalized identities, such as being underinsured or unhoused. These experiences were associated with exacerbation of existing medical mistrust and poor patient-clinician communication. Participants described various mechanisms of self-advocacy and medical decision-making based on prior experiences with mistreatment from health care workers and medical trauma. This study found that Black patients' experiences with racism, specifically epistemic injustice, were associated with their perspectives on medical care and decision-making during serious illness and end of life. These findings suggest that race-conscious, intersectional approaches may be needed to improve patient-clinician communication and support Black patients with serious illness to alleviate the distress and trauma of racism as these patients near the end of life.

Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment

Battery health evaluation is vital for ensuring the security and reliability of lithium-ion batteries. However, the currently proposed methods generally require high-quality input data for feature extraction in online applications. To overcome this obstacle, this paper proposes a rapid online health estimation method only based on partial constant-voltage (CV) charging segment. Firstly, through primary analysis of battery test data, the evolution of CV charging current is confirmed to be correlated with battery capacity. Subsequently, the current evolution constant of CV charging phase is mathematically formulated and quantitatively characterized using a novel health indicator (HI). Besides, charging time and charging capacity are also extracted as HIs to comprehensively capture the CV charging behavior and enhance the robustness of data-driven models. Considering the user's charging habits, an optimized CV segment is determined, enabling a significant reduction in data size and coverage. Finally, three data-driven methods are employed to construct health estimation models by using the extracted HIs, and the best performance is achieved by Gaussian process regression with MAE and RMSE lower than 0.8% and 1%, respectively. Remarkably, the proposed method demonstrates superiority in dealing with sparse sampling, and satisfactory results with 2.9% error under the sparsity of 10 s are obtained.

Data driven approach using capacitance resistance model to determine polymer in-situ retention level

Polymer retention in reservoir rock can significantly impair polymer flood effectiveness and increases the required polymer bank slug size to maintain mobility control. Laboratory measurements such as static adsorption and coreflooding test are typically performed to assess polymer retention level in the reservoir, however such measurements are sensitive to rock sample used and its grain size, and the preparation method. Hence, to obtain the most representative data, polymer retention level measurement should be done at reservoir in-situ condition.This study presents an investigation to couple capacitance resistance model with polymer transport model. By utilizing produced polymer concentration data, polymer injection allocation can be determined. The coupled model also provides rapid polymer retention level estimate based on in-situ data. This model has been validated with numerical reservoir model and applied to evaluate a field polymer flood pilot.

The Ammonite Catacadoceras Barnstoni Beds and the Problem of Separation of the Middle and Upper Substages of the Bathonian Stage in Northern Siberia

The paper presents the results of studying the taxonomic composition of ammonites of the family Cardioceratidae in the Bathonian of northern Siberia in the range of distribution of the last representatives of the genus Arcticoceras and the first representatives of the genus Catacadoceras to solve the problem of distinguishing between the Middle and Upper Bathonian. The incorrectness of all previous identifications of the Middle Bathonian species Arcticoceras cranocephaloide Callomon et Birkelund, 1985 based on the northern Siberian material has been established. These determinations were mainly based on Late Bathonian ammonites belonging to the species Catacadoceras barnstoni (Meek, 1859) or the form characterized by a narrow umbilicus and described herein as Cat. aff. barnstoni. An updated zonal scale of the Middle–Upper Bathonian of Siberia based on ammonites is proposed, which is recommended for use both in the regional stratigraphic schemes of the Jurassic deposits of Eastern (Middle) Siberia and in the Boreal (Siberian) zonal standard of the Bathonian Stage. The extreme importance of preserving an auxiliary biostratigraphic subdivision in this scale, the Cat. barnstoni Beds, is noted, which is due to the wide distribution and abundance of the index species in Siberian sections, as well as its significance for the correlation of the lower part of the Upper Bathonian of northern Canada, northern Siberia, Franz Josef Land and East Greenland. In addition, two intervals, namely interval with Cat. aff. barnstoni and interval with Cat. perrarum, are fixed as benchmarks, which are useful for the rapid age estimation and correlation of the Upper Bathonian deposits in Arctic sections within the upper part of the Cat. barnstoni Beds.

Cumulative absolute velocity prediction for earthquake early warning with deep learning

AbstractA rapid and accurate estimate of earthquake damage is a key component in a successful earthquake early warning (EEW) system. The cumulative absolute velocity (CAV) is an important and widely used parameter to measure ground motion intensity, but it cannot be correctly estimated via the traditional approach with the limited information available in typical EEW systems. Therefore, current EEW systems cannot effectively use CAV to predict earthquake damage. Herein, a CAV prediction model (DLcav) based on convolutional neural networks was proposed for EEW systems. DLcav is an end‐to‐end solution to continuously predict CAV using arriving seismic waves of increasing length and supplemented with additional auxiliary information. The effectiveness of DLcav to predict CAV was tested based on Japanese ground motion records, and the generalization ability of DLcav was assessed using the ground motion records from Chile. The results demonstrate that DLcav can rapidly predict CAV with good accuracy, which will help better estimate earthquake damage in EEW systems.

Co-inversion of island leaf area index combination morphological and spectral parameters based on UAV multi-source remote sensing data

The precise and rapid estimation of leaf area index (LAI) is crucial for quantitative ecological monitoring and assessment, as it serves as an important ecological indicator of plant growth state and canopy structure. The acquisition, integration, and application of multiple remote sensing data from Unmanned Aerial Vehicle (UAV) platforms in quantitative ecological research is a current research hotspot. This study aims to explore the potential of combining morphological and spectral parameters using the symbolic regression (SR) algorithm with allometric models to improve the estimation of Leaf Area Index (LAI) at a regional scale of island ecosystem using UAV multi-source remote sensing data. The Canopy Height Model (CHM), Vegetation Density (VD), and Vegetation Indices (VIs), derived from UAV multi-platforms, were used as independent variables in the development of the retrieval models. Subsequently, regression equations were used to construct the Singlet, Duplet, and Triplet models. The precise assessments indicated that the triplet model demonstrates the highest accuracy, with a mean absolute error (MAE) of 9.48% and root mean square error (RMSE) of 0.2345. This improvement was observed compared to the singlet model (MAE = 19.55%, RMSE = 0.4091) and duplet model (MAE = 15.88%, RMSE = 0.3450), which can be attributed to incorporating morphological parameters to reduce the impact of tree height and leaf density. The analysis also showed that the estimated LAI accuracy is generally higher for shrubs and herbs compared to trees, and that lower plants with high vegetation coverage are better retrieved. The methodology advaneced in this study demonstrates significant potential and implications for ecological monitoring and assessment of island ecosystems, thereby expanding the research scope in the field of island ecology. Furthermore, it provides scientific evidence and technical support for decision-makers and scholars to propose and implement various measures for the conservation and restoration of island ecosystems.

A Novel Hybrid Energy Efficient Model using Clustering in Wireless Sensor Networks

An innovative hybrid approach is presented in this paper aimed at rapid predicting the optimum routing path selection in WSNs using the Lagrangian method and Clustering. The main motive of this work proposed is to maximize the clustering productivity, reduce the congestion in routing system and optimize the energy range in the network. By combining the route prediction through clustering and energy flow estimates in the routing protocol design-based algorithm, this will combine both the objectives of rapid route prediction and energy flow estimation. A Distributional Heterogeneous Pathfinder (DHP) based routing algorithm was used in this optimal clustering system to predict the best routing paths in the network and to optimize network energy consumption. In general, sensor communication in the Internet of Things takes place with tremendous amounts of data transmission and other interactions. The results drawn from simulation shows the higher efficiency and fast route detection with better energy transmission of data through the network.

Sparse representation for machine learning the properties of defects in 2D materials

Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the search space for such structures is enormous, and ab-initio computations prohibitively expensive. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Also, our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part.

Comparison of Stalk Volume by Water Displacement Method and Calculation Method for Stalk Weight Determination and Its Relevance to Single Cane Weight in Sugarcane Clones

The present study was conducted to identify the association of non-destructive rapid estimation of stalk volume by calculation method with water displacement method and to predict the single cane weight by stalk volume. The stalk volume by water displacement method (SVWM) and stalk volume by calculation method (SVCM) were compared for the efficacy of stalk weight determination in sugarcane clones. Results from both methods were similar and a highly significant relationship was found between the two methods (r2 = 0.9092, P &lt; 0.0001). Both protocols provide consistent stalk volume measurements; however, the stalk volume calculation method is preferred for its quick evaluation of a large number of stalks in a rapid way besides its non-destructive nature. The calculation method saved more than 70 seconds for each sample. The stalk weight has shown a significant correlation of r=0.71***, 0.83***, 0.94***, 0.99*** with SH, CD, SVCM and SVWM respectively, while the stalk density (SD) was observed with a negative correlation (r=-0.27ns) with stalk weight. The stalk volume-based predicted stalk weight estimation was observed with high degree of correlation (&gt;0.88***) with the original single cane weight by both the studied methods, thus, measurements of stalk volume based on calculation method which provide simple, rapid, non-destructive field phenotyping of single cane weight in sugarcane crop may be recommended for the sugarcane research.

Parametric estimation of a stability factor for certified reduced basis methods via adaptive Gaussian process

The a posteriori error analysis of a reduced basis (RB) method may certify the accuracy of an RB solution without the truth finite element (FE) solution. However, the error theory necessitates a numerically expensive stability factor such as coercivity or an inf–sup factor. Consequently, we perforce resort to the successive constraint method (SCM) to achieve a stability lower bound for a rigorous albeit rapid RB analysis. As an alternative, we propose an adaptive Gaussian process (AGP) to produce a stability lower bound for the rigorous, rapid, and sharp estimation of an RB solution error. We utilize linear elastostatic and Helmholtz acoustic problems with geometric parameterization to compare the AGP with the SCM for a numerical investigation. The results show that the AGP significantly surpasses the SCM. This is because the a posteriori error estimation of an RB solution obtained with the AGP is sharper and faster than that obtained with the SCM by factors of at least 103 and 10, respectively. With regard to a rigorous error estimation, the AGP succeeds for the linear elastostatic problem but fails for the Helmholtz acoustic problem when an RB size is one at parameter values incurring only resonance. Otherwise, it manages to attain a rigorous error estimation. To conclude, the AGP is observed to be heuristically more practical than the SCM for a certified RB analysis. This research may considerably aid a static condensation RB element (scRBE) method because the technique numerously repeats the a posteriori error estimation of a reduced bubble function for the a posteriori error estimation of an scRBE solution.

Rapid Assessment of Crystal Nucleation and Growth Kinetics: Comparison of Seeded and Unseeded Experiments.

In this work, we outlined an experimental workflow enabling the rapid assessment of primary and secondary nucleation and crystal growth kinetics. We used small-scale experiments in agitated vials with in situ imaging for crystal counting and sizing to quantify nucleation and growth kinetics of α-glycine in aqueous solutions as a function of supersaturation at isothermal conditions. Seeded experiments were required to assess crystallization kinetics when primary nucleation is too slow, especially at lower supersaturations often encountered in continuous crystallization processes. At higher supersaturations, we compared results from seeded and unseeded experiments and carefully analyzed interdependencies of primary and secondary nucleation and growth kinetics. This approach allows for the rapid estimation of absolute values of primary and secondary nucleation and growth rates without relying on any specific assumptions about functional forms of corresponding rate expressions used for estimation approaches based on fitting population balance models. Quantitative relationships between nucleation and growth rates at given conditions provide useful insights into crystallization behavior and can be explored to rationally manipulate crystallization conditions for achieving desirable outcomes in batch or continuous crystallization processes.