Response Model Research Articles

Türkiye's responses to the 2023 earthquakes: An application of family adjustment and adaptation response model

AbstractAfter devastating earthquakes struck Türkiye in February 2023, the country faced the challenge of addressing the psychosocial needs of the survivors. This article uses the Family Adjustment and Adaptation Response (FAAR) model to conceptualize Türkiye's responses to the earthquakes in terms of its demands (i.e., cumulative strains and current stressors), capabilities (i.e., existing resources and coping mechanisms), and meaning/belief systems (i.e., shared meaning, religion/spirituality, and positive outlook/hope). A historical context is provided to better understand the cumulative tensions and resilience of the country, seeking ways to strengthen effective crisis responses in anticipation of an even bigger earthquake in the coming years. Additionally, this article may spark a conversation among family practitioners in different countries about how to collaborate on mobilizing resources during challenging times.

Enhancing performance factor analysis through skill profile and item similarity integration via an attention mechanism of artificial intelligence

IntroductionFrequent formative assessment is essential for accurately evaluating student learning, enhancing engagement, and providing personalized feedback. In STEM education, understanding the relationship between skills that students have internalized (mastered) and those they are developing (emergent) is crucial. Traditional models, including item response and cognitive diagnosis models, primarily focus on emergent skills, often overlooking internalized skills. Moreover, new tools like large language models lack a complete approach for tracking knowledge and capturing complex skill relationships.MethodsThis study incorporates artificial intelligence, specifically attention mechanisms, into educational assessment to evaluate both emergent and internalized skills. We propose a modified version of Performance Factor Analysis (PFA), which assesses student abilities by analyzing past responses and comparing them with peer performance on the same items, using parameters from a sigmoid function. This model leverages attention mechanisms to capture item order-based similarity and decay principles, providing a nuanced view of student skill profiles.ResultsThe Modified Performance Factor Analysis model significantly improved discriminative power, accuracy, precision, recall, and F1 scores across various skill areas compared to traditional PFA models.DiscussionThese results indicate that the Modified Performance Factor Analysis model allows for a more accurate and comprehensive evaluation of student performance, effectively identifying both emergent and internalized skills. By integrating AI into assessment, educators gain deeper insights, enabling them to refine teaching strategies and better support students' mastery of both types of skills.

Many hurdles to take: Explaining peacekeepers’ ability to engage in human rights activities

Human rights are a fundamental principle and purpose of the United Nations (UN). Yet, UN peacekeeping operations (PKOs) exhibit substantial variation in their ability to engage in human rights activities. While existing research has investigated deployment and mandates, we explain what peacekeepers can actually do on the ground. We argue that the UN Security Council’s permanent member states (the P5) limit human rights mandates if they have private interests in PKO host countries, thereby diminishing peacekeepers’ ability to promote and protect human rights. Moreover, armed conflict shifts priorities away from human rights activities. We use novel data on 21 human rights activities in African countries (1991–2016) and item response models to capture PKOs’ latent ability to engage in these activities. Random and fixed effects regression and mediation analyses with sensitivity tests support our expectations. We find that the P5’s economic interests in the PKO host country negatively correlate with the strength of human rights mandate provisions, which in turn negatively correlates with PKOs’ ability to engage in human rights activities. We find similar, although less consistent, correlations for P5’s security interests. Yet, while mandates partly define the scope of PKOs’ activities, field-level conditions also have an influence. Specifically, ongoing armed conflict negatively correlates with PKOs’ ability to engage in human rights activities. Our results suggest that rising challenges to the liberal international order by powerful states, coupled with the more frequent deployment of PKOs in conflict zones, will likely increase the hurdles that UN PKOs need to overcome to meet expectations regarding their human rights engagement.

Kinetic Modeling in Temperature-Modulated Semiconductor Gas Sensor Utilizing Eley-Rideal Mechanism and Its Application in Discriminative Detection of VOCs.

This study presents a comprehensive kinetic investigation for describing the dynamic sensing process of semiconductor metal oxide (SMO)-based gas sensors, with a focus on the Eley-Rideal mechanism as a valid pathway. The modeling elucidates the direct interactions between volatile organic compounds (VOCs) and adsorbed oxygen on the material surface, providing insights into the temperature-dependent response characteristics of the sensor and addressing selectivity toward different VOCs fundamentally. By incorporating the effects of thermal modulation, the kinetic model was validated through theoretical fitting of experimental data obtained from a fabricated SnO2 nanoparticle gas sensor exposed to ethanol, n-propanol, toluene, and butanone at systematically varying operating temperatures. The results demonstrated that the model accurately captures response transient values, enabling a general framework for qualitatively discriminating specific gases based on their unique reaction kinetics. Furthermore, a machine learning procedure containing the above model and power laws of quantitative concentrations after identifying compounds was developed for the prediction of target VOCs. The qualitative accuracy was determined to be 99.4%, while quantifying with the mean absolute errors of 5.0, 7.5, 4.0, and 8.9 within the range of 25-200 ppm, respectively. The precise and straightforward strategy facilitates the response modeling of SMO-based gas sensors, offering a valuable platform for the designing model-driven algorithms applicable in gas analysis and monitoring.

A Markov regime-switching event response model: beef price spread response to processing capacity shocks

AbstractThis research introduces a new method for event studies in time-series analysis named the Markov regime-switching event response model (MS-ERM). The MS-ERM is a comprehensive approach that integrates two different event study approaches: 1) measuring the impact of an event through structural shift and 2) measuring the impact via additional distributional components. As an empirical application, the study measures the impact of beef-packing plant closures on the weekly live-to-cutout beef price spread. The results indicate that the MS-ERM is a promising tool for event studies, particularly when an empirical dataset has both groups of events that cause and do not cause structural changes.

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization. This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments. This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared. The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely. We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

Diclofenac sodium removal by powdered activated carbon of coconut endocarp: process optimization, kinetics, and isotherms

ABSTRACT Emerging contaminants (ECs) have been detected in various environmental compartments, especially in aquatic bodies. Among EC, diclofenac sodium (DS) is one of the most ecotoxic and causes hemodynamic changes, thyroid tumors, and adverse effects under chronic exposure. Therefore, some countries have adopted restrictive legislation, encouraging the development of technology to mitigate this situation. Among the available water treatment processes, adsorption is an effective alternative in technical and economic aspects. In this context, this study aimed to evaluate, on a bench scale, the efficiency of DS removal in powdered activated carbon (PAC) of coconut endocarp. The adsorption of DS was analyzed through central composite design (CCD), using four factors: diclofenac sodium concentration (CDS from 50 to 450 mg · L−1), adsorbent concentration (CPAC from 0.2 to 5 g · L−1), contact time (Ct from 5 to 45 min), and pH (from 5 to 9). The results supported response modeling for adsorption capacity, pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetics, intraparticle diffusion (IPD), and Langmuir and Freundlich isotherms. DS demonstrated an affinity for adsorption on PAC. The maximum adsorption capacity was 169.39 mg · g−1 for PAC (CDS of 331.64 mg · L−1, CPAC of 0.2 g · L−1, Ct of 40.6 min, and pH 5) obtained through duplicate confirmation batches.

Multiple environmental regulations and green technology innovation: a boundary theory perspective

Purpose Based on boundary theory, the authors try to clarify the boundaries of multiple environmental regulations. This paper aims to explore the strategic choices of multiple environmental regulations, analyze their spatial impact on green technology innovation and clarify the implementation priorities of multiple environmental regulations in various regions. Design/methodology/approach The authors construct an asymmetric response model based on the boundary theory perspective to analyze the strategic choices of different environmental regulations. In addition, dynamic spatial econometric methods are used to empirically analyze the differentiated regulatory boundaries of command-control type, market-incentive and voluntary-participation in environmental regulations and their spatial effects on green technology innovation. Findings Strict compulsory boundaries are formed in China when the command-control and market-incentive types are selected in a dynamic spatial and competitive game environment. When voluntary participation is selected, strict compulsory or loose basic boundaries are formed in each region. The command-control type is highly dependent on geographical distance, the market-incentive type is more dependent on an economic connection and the voluntary-participation type has obvious spatial spillover effects under the spatial weight matrix of geographical distance and economic connection. When Eastern China chooses voluntary participation in environmental regulation, the incentive effect on green technology innovation is the strongest. Middle China is the market-incentive type, and Western China is the command-control type. Command-control type and market-incentive type form strong complementary effects in dynamic spatial environments. Originality/value From the boundary theory perspective, this study analyzes the differentiated dynamic spatial boundaries of command-control, market-incentive and voluntary-participation in environmental regulations. Analyze the spatial impact of multiple environmental regulations on green technology innovation and clarify the implementation priorities of multiple environmental regulations in various regions.

Getting started with the graded response model: An introduction and tutorial in R.

This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert-style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right-wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results.

PATH-37. A NEW SUBTYPE OF DIFFUSE MIDLINE GLIOMA, H3 K27 AND BRAF/FGFR1 CO-ALTERED, EXHIBITS SPECIFIC BIOLOGICAL CHARACTERISTICS AND NEW THERAPEUTIC VULNERABILITIES

Abstract Diffuse midline gliomas (DMG) H3 K27-altered are incurable grade 4 gliomas and represent a major challenge in neuro-oncology. This tumor type is classified in four subtypes by the 2021 edition of the WHO Classification of the Central Nervous System tumors. However, we recently identified a new subtype of DMG by assembling a retrospective cohort of 60 adult and pediatric DMG cases harboring BRAF or FGFR1 mutations in addition to the H3K27M mutation or EZHIP overexpression. Comprehensive clinical, histological, radiological, and genomic, analyses allowed to identify distinct genotype-phenotype characteristics. Contrary to other DMG, these tumors occur more frequently in the thalamus (27% vs. 70% for BRAFV600E or 58% for FGFR1MUT), and in adults in the case of FGFRMUT tumors. Importantly, these patients display a longer overall survival with a median above three years. Unsupervised analysis of DNA methylation profiles regrouped these MAPK-altered DMG in a methylation cluster distinct from other DMG H3K27, but also lower-grade midline tumors driven by FGFR1 or BRAF mutations Additionally, this new DMG subtype harbors atypical radiological and histopathological profiles with calcification and/or a solid tumor component both for BRAFV600E and FGFR1MUT cases that can suggest this differential diagnosis. Further analyses of the transcriptome of these tumors compared to canonical DMG H3K27-altered showed an expression signature of senescence with activation of a TP53/CDKN1A axis, together with a strong upregulation of the PI3K/AKT/MTOR pathway. Our current modelling of the drug response using patient-derived models of these specific DMG will permit to rapidly prioritize treatment strategies. Additionally, our clinical experience in treating these patients suggests a higher response to everolimus and imipridone ONC201 in this specific subtype. In conclusion, DMG H3 K27 and BRAF/FGFR1 co-altered, represent a new subtype of DMG with distinct specific characteristics compared to canonical DMG H3K27 and data accumulated strongly support their separate stratification in clinical trials and specific therapeutic managements.

Quantitative Assessment of Human Health Risks Associated with Heavy Metal and Bacterial Pollution in Groundwater from Mankweng in Limpopo Province, South Africa

Heavy metal and microbial pollution in groundwater raises health concerns due to its adverse effects. This study aimed to assess the health risks associated with heavy metal and bacterial pollution in groundwater in Mankweng. Heavy metals and Escherichia coli were detected using inductively coupled plasma mass spectrophotometry and a Colilert system, respectively. The heavy metal pollution index (HPI) and non-carcinogenic and carcinogenic risks were computed. The β-Poisson dose–response model was employed to predict the probability of E. coli infection. The metals’ concentrations were all within the permissible limits of the South African National Standard (SANS). However, Pb levels at sites E and G exceeded the World Health Organization (WHO) guideline (≤0.01 mg/L). HPI values were all less than 100, indicative of low contamination. The hazard quotient values were all less than 1, except for vanadium. The cumulative cancer risk ranged between 3.06 × 10−5 and 1.81 × 10−4 and between 3.55 × 10−5 and 2.20 × 10−4 for adults and children, respectively. E. coli was only detected at site L. The annual risk of E. coli infection exceeded the WHO risk limit of 10−4. The results underscore the need for the regular monitoring of groundwater.

Does higher endorsement with collaboration lead to better performance on collaborative problem solving? An explanatory item response approach to cross-cultural comparisons

IntroductionCollaborative problem solving (CPS) is an essential competency in the 21st century. However, the understanding of how cultural background shapes individuals’ collaboration awareness and its relationship with collaborative problem-solving skills are underexplored by psychometrics.MethodsThis study employs Explanatory Item Response Modeling to examine the impact of cultural orientations on students’ endorsement with collaboration in PISA 2015.ResultsResults show that students endorsed valuing teamwork more than valuing relationships. Western countries with individualist cultures generally demonstrated higher endorsement with collaboration than Eastern countries with collectivist cultures. China has the highest collaboration endorsement, followed by the US, Canada, Korea, and Japan. In contrast, Japan has the highest CPS assessment scores, followed by Korea, Canada, the US, and China.DiscussionFindings revealed that students from collectivist cultures do not have higher endorsements of collaboration compared with students from individualist cultures. Also, higher endorsement of collaboration does not necessarily lead to better success in the CPS assessment. Further research is needed to understand the gap between students’ attitudes towards collaboration and their achievement based on cultural values and schools’ CPS training.

Modeling Drug Responses and Evolutionary Dynamics using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple Negative Breast Cancer.

The inter- and intra-tumor heterogeneity of triple negative breast cancers (TNBC), which is reflected in diverse drug responses, interplays with tumor evolution. Here, we developed a preclinical experimental and analytical framework using treatment-naive TNBC patient-derived tumor xenografts (PDTX) to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTX exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of non-genetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes with functional and therapeutic consequences. High throughput drug screening methods in ex vivo patient derived tumor xenograft cells (PDTCs) revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high inter-model variability and permanent non-genomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens.

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

The Bayesian information criterion (BIC) is a widely used statistical tool originally derived for fully observed data. The BIC formula includes the sample size and the number of estimated parameters in the penalty term. However, not all variables are available for every subject in planned missingness designs. This article demonstrates that a modified BIC, tailored for planned missingness designs, outperforms the original BIC. The modification adjusts the penalty term by using the average number of estimable parameters per subject rather than the total number of model parameters. This new criterion was successfully applied to item response theory models in two simulation studies. We recommend that future studies utilizing planned missingness designs adopt the modified BIC formula proposed here.

Data-Driven Surrogate Modeling with Microstructure-Sensitivity of Viscoplastic Creep in Grade 91 Steel

AbstractTo support the development of advanced steel alloys tailored to withstand extreme conditions, it is imperative to account for the mechanical performance of components, while considering the influence of local microstructure on the macroscopic response. To this end, this study focuses on the development of microstructure-sensitive constitutive models for the mechanical response of Grade 91 steel exposed to extreme thermo-mechanical environments. Polynomial chaos expansion (PCE) surrogates are used to emulate high-fidelity polycrystal simulations of the viscoplastic response of Grade 91 steel as a function of the microstructure fingerprint (e.g., dislocations and precipitates). To cover a wide temperature–stress domain, two separate PCE surrogates—one that captures softening and the other that captures hardening behavior—are combined using another (sparse) Gaussian process regression model. The resulting constitutive creep surrogate model is integrated within the MOOSE finite element framework to simulate the intricate effects of microstructure, in particular MX-phase precipitates, on a component with a graded microstructure. Surrogate sensitivity analysis is applied to quantify the relevant impact of spatially varying microstructure on the creep response in a test-case involving a Grade 91 alloy with a prototypical weld.

Imaging Complex Structures of the Los Angeles Basin via Adjoint-State Travel-Time Tomography

ABSTRACT In this study, we present high-resolution seismic velocity models for the Los Angeles basin (LAB) and its adjacent area using adjoint-state travel-time tomography, fitting an extensive database of P- and S-wave travel times accumulated from 1980 to 2021. We select 151,193 first P-wave travel times and 149,997 first S-wave travel times from local earthquakes archived in the Southern California Earthquake Data Center to determine the velocity models, with earthquake locations updated at each iteration. With seismic stations spaced more than 3.5 km apart, our dataset has limited resolution in the uppermost 1–2 km. However, starting from three different initial models, our VP models, which are optimally imaged between 3 and 15 km, show similar velocity heterogeneity and provide a better fit to the observed first travel-time data compared to the Community Velocity Model-Harvard 15.1.0 and Community Velocity Model-Southern California Earthquake Center 4.26. Our models provide a detailed delineation of the subsurface structure beneath the LAB, revealing significant velocity variations across active faults, a 10-km-thick sequence of sedimentary rocks within the basin, and a distinct basin margin marked by transitions from low to high-velocities. In addition, these models highlight basement structures with elevated VP and VS located at depths of 9 to 12 km and beyond. Specifically, beneath the northeastern part of the basin, the models demonstrate improved accuracy and reliability in reflecting the linear relationship between VP and VS in mafic rocks. The accurate delineation of the basin’s structure provided by our models could also offer robust constraints for seismic response modeling and seismic hazard assessment in the region.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT

Objective.This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors.Approach.The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3×3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations.Main results.Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%and 31%across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%.Significance.The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Integral equation based wellbore preconditioner for 3D electromagnetic response modeling

Modeling subsurface controlled-source electromagnetic (CSEM) responses using the finite-element (FE) method is challenging in the presence of highly conductive wellbore casing. The very large conductivity contrast between the casing and the host formation leads to increased computation time and potentially unstable solutions. We address this difficulty by preconditioning an FE solver with an integral equation (IE) primary solution that captures the CSEM response of a realistic-sized steel wellbore casing. Our hybrid IE-FE approach determines the primary field using 2D integral-equation forward modeling and then interpolates the IE-computed solution onto the nodes. Then using an existing FE simulator, we solve for the secondary electric and magnetic fields. This approach removes the need for an ultra-fine FE mesh around the wellbore, thereby improving FE solution stability while greatly reducing FE computation time. Our method is illustrated by modeling the CSEM responses of idealized fluid-bearing zones.

The mental state inferences in healthcare professionals scale: a psychometric study.

Empathizing with patients is an essential component of effective clinical care. Yet, a debate persists regarding how healthcare professionals' emotions and performance are impacted when they engage in empathetic behaviors and attempt to discern patients' mental states during clinical interactions. To approach this issue, this study explores the psychometric properties of the Mental State Inferences in Healthcare Professionals Scale (MSIHPS), a novel eight-item scale to evaluate healthcare professionals' perceptions of their own disposition to infer patients' mental states during clinical interactions. The study was conducted across various units within a regional hospital and primary care units affiliated with the Canarian Public Health Service in Spain. Data collection took place over the course of 2022, spanning from February to November. The psychometric properties of the scale were analyzed, including an exploratory and a confirmatory factor analysis, to test reliability and validity. Additionally, an item response model was run to test potentially biased items. The study collected data from a sample of 585 healthcare professionals. Overall, the results indicate that the psychometric properties of the tool are adequate. Furthermore, the unidimensionality of the scale was confirmed using the item response model, wherein the eight-items significantly contribute to predicting the latent construct. The MSIHPS offers the opportunity to explore the role of mentalizing in a diversity of healthcare settings. This measure can be useful to explore the relationship between healthcare professionals' disposition to infer patients' mental states and other relevant variables in clinical interactions, such as empathy and clinical performance.

Precise regulating the specific oxygen consumption rate to strengthen the CoQ10 biosynthesis by Rhodobater sphaeroides

Coenzyme Q10 (CoQ10) is the most consumed dietary supplement and mainly biosynthesized by aerobic fermentation of Rhodobacter sphaeroides (R. sphaeroides). Oxygen supply was identified as a bottleneck for improving CoQ10 yield in R. sphaeroides. In this study, a precise regulation strategy based on dielectric spectroscopy (DS) was applied to further improve CoQ10 biosynthesis by R. sphaeroide. First, a quantitative response model among viable cells, cell morphology, and oxygen uptake rate (OUR) was established. DS could be used to detect viable R. sphaeroides cells, and the relationship among cell morphology, CoQ10 biosynthesis, and OUR was found to be significant. Based on this model, the online specific oxygen consumption rate (QO2) control strategy was successfully applied to the CoQ10 fermentation process. QO2 controlled at 0.07 ± 0.01 × 10− 7mmol/cell/h was most favorable for CoQ10 biosynthesis, resulting in a 28.3% increase in CoQ10 production. Based on the multi-parameters analysis and online QO2 control, a precise online nutrient feeding strategy was established using conductivity detected by DS. CoQ10 production was improved by 35%, reaching 3384 mg/L in 50 L bioreactors. This online control strategy would be effectively applied for improving industrial CoQ10 production, and the precise fermentation control strategy could also be applied to other fermentation process.