Quality Of Model Fit Research Articles

The Impact of Servant Leadership on Corporate Social Responsibility: Insights from Egyptian Tourism Organizations

This study examines how servant leadership affects corporate social responsibility (CSR) practices and its dimensions (corporate commitment, governance, environmental protection, and social contribution) in the Egyptian tourism industry, focusing on Hilton and Four Seasons. Quantitative research with 386 participants was conducted. Spearman correlation, CFA, path coefficient analysis, model quality of fit, and Fornell-Larcker criterion were used for data analysis. The links between servant leadership and CSR factors were examined using SEM. A positive and significant correlation was found between servant leadership and all CSR aspects. In the Egyptian tourism industry, servant leaders can positively influence an organization's commitment to sustainable and socially responsible business practices by prioritising stakeholder well-being, fostering ethical practices, and promoting a culture of responsibility.

Construction of a predictive model of death for sepsis-associated acute kidney injury

To establish a predictive model nomogram for 30-day death in patients with sepsis-associated acute kidney injury (SA-AKI) by using the data from the large international database, the Electronic Intensive Care Unit-Collaborative Research Database (eICU-CRD), and to validate its predictive performance. A retrospective cohort study was conducted using data from the eICU-CRD. Data of SA-AKI patients were screened from the eICU-CRD database, including demographic characteristics, medical history, SA-AKI type, Kidney Disease: Improving Global Outcomes (KDIGO)-AKI staging, severity of illness scores, vital signs, laboratory indicators, and treatment measures; with admission time as the observation start point, death as the outcome event, and a follow-up time of 30 days. Relevant variables of patients with different 30-day prognoses were compared. Univariate Logistic regression analysis and multivariate Logistic regression forward likelihood ratio analysis were used to screen for risk factors associated with 30-day death in SA-AKI patients, and a predictive model nomogram was constructed. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the model. A total of 201 SA-AKI patients' data were finally enrolled, among which 51 survived for 30 days and 150 died, with a mortality of 74.63%. Compared with the survival group, patients in the death group were older [years old: 68 (60, 78) vs. 59 (52, 69), P < 0.01], had lower body weight, proportion of transient SA-AKI, platelet count (PLT) and blood glucose [body weight (kg): 79 (65, 95) vs. 91 (71, 127), proportion of transient SA-AKI: 61.33% (92/150) vs. 82.35% (42/51), PLT (×109/L): 207 (116, 313) vs. 260 (176, 338), blood glucose (mmol/L): 5.5 (4.4, 7.1) vs. 6.4 (5.1, 7.6), all P < 0.05] and higher proportion of persistent SA-AKI, sequential organ failure assessment (SOFA) score, lactic acid (Lac), and total bilirubin [TBil; proportion of persistent SA-AKI: 38.67% (58/150) vs. 17.65% (9/51), SOFA score: 7 (5, 22) vs. 5 (2, 7), Lac (mmol/L): 0.4 (0.2, 0.7) vs. 0.3 (0.2, 0.4), TBil (μmol/L): 41.0 (17.1, 51.3) vs. 18.8 (17.1, 34.2), all P < 0.05]. Univariate Logistic regression analysis showed that age [odds ratio (OR) = 1.035, 95% confidence interval (95%CI) was 1.013-1.058, P = 0.002], body weight (OR = 0.987, 95%CI was 0.977-0.996, P = 0.007), persistent SA-AKI (OR = 2.942, 95%CI was 1.333-6.491, P = 0.008), SOFA score (OR = 1.073, 95%CI was 1.020-1.129, P = 0.006), PLT (OR = 0.998, 95%CI was 0.996-1.000, P = 0.034), Lac (OR = 1.142, 95%CI was 1.009-1.292, P = 0.035), TBil (OR = 1.422, 95%CI was 1.070-1.890, P = 0.015) were associated with 30-day death risk in SA-AKI patients. Multivariate Logistic regression forward likelihood ratio analysis showed that age (OR = 1.051, 95%CI was 1.023-1.079, P = 0.000), body weight (OR = 0.985, 95%CI was 0.974-0.995, P = 0.005), cardiovascular disease (OR = 9.055, 95%CI was 1.037-79.084, P = 0.046), persistent SA-AKI (OR = 3.020, 95%CI was 1.258-7.249, P = 0.013), SOFA score (OR = 1.076, 95%CI was 1.013-1.143, P = 0.017), and PLT (OR = 0.997, 95%CI was 0.995-1.000, P = 0.030) were independent risk factors for 30-day death in SA-AKI patients. Based on the above risk factors, a predictive model nomogram for 30-day death in SA-AKI patients was constructed. ROC curve analysis showed that the area under the ROC curve (AUC) of the model was 0.798 (95%CI was 0.722-0.873), with a sensitivity of 86.7% and a specificity of 62.7%. Calibration curve showed that the fitted curve was close to the standard line, indicating that the predicted probability was close to the actual probability, suggesting good predictive performance of the model. Hosmer-Lemeshow test showed χ 2 = 6.393, df = 8, P = 0.603 > 0.05, suggesting that the model could fit the observed data well. The quality of model fitting was judged by the accuracy of model prediction. The results showed that the prediction accuracy rate of the model was 95.3%, and the overall prediction accuracy rate of the model was 81.6%, indicating good model fitting. A predictive model for 30-day death in SA-AKI patients based on risk factors can be successfully constructed, and the model has high accuracy, sensitivity, reliability, and certain specificity, which can help to early identify high-risk patients for death and adopt more proactive treatment strategies.

A retrospective observational cohort study on the postoperative respiratory complications and their risk factors in brachycephalic dogs undergoing BOAS surgery: 199 cases (2019-2021).

To observe the occurrence of postanaesthetic respiratory complications and to determine their prevalence and risk factors in dogs undergoing brachycephalic obstructive airway syndrome surgery. Data from 199 clinical records were retrospectively analysed. Univariable logistic regression followed by multivariable logistic regression was used to identify associations between the dependent variables (set as the postoperative respiratory complications observed in the study dogs) and various independent covariates. The quality of model-fit was assessed using the likelihood ratio test. P≤0.05 was considered statistically significant. Four postoperative respiratory complications were observed: hypoxaemia (n=10/199; 5%), dyspnoea requiring tracheal re-intubation (n=13/199, 7%), dyspnoea requiring tracheostomy (n=10/199, 5%) and aspiration pneumonia (n=12/199, 6%). Univariable logistic regression showed an association between postoperative aspiration pneumonia and increasing body condition score and American Society of Anaesthesiology classification; however, when these covariates were evaluated in the multivariable model significance was not maintained. Risk factors for tracheostomy were preoperative and postoperative aspiration pneumonia (odds ratio: 9.52, 95% confidence interval: 1.56 to 57.93) and increasing brachycephalic obstructive airway syndrome grade (odds ratio: 4.65, 95% confidence interval: 0.79 to 27.50). High brachycephalic obstructive airway syndrome grade and aspiration pneumonia, either developing peri-operatively or as pre-existing condition, may represent risk factors for postoperative tracheostomy. Preoperative diagnosis of aspiration pneumonia may further increase the risk of postoperative complications.

Enhancing Chub Mackerel Catch Per Unit Effort (CPUE) Standardization through High-Resolution Analysis of Korean Large Purse Seine Catch and Effort Using AIS Data

Accurate determination of fishing effort from Automatic Identification System (AIS) data improves catch per unit effort (CPUE) estimation and precise spatial management. By combining AIS data with catch information, a weighted distribution method is applied to allocate catches across various fishing trajectories, accounting for temporal dynamics. A Generalized Linear Model (GLM) and Generalized Additive Model (GAM) were used to examine the influence of spatial–temporal and environmental variables (year, month, Sea Surface Temperature (SST), Sea Surface Salinity (SSS), current velocity, depth, longitude, and latitude) and assess the quality of model fit for these effects on chub mackerel CPUE. Month, SST, and year exhibited the strongest relationship with CPUE in the GLM model, while the GAM model emphasizes the importance of month and year. CPUE peaked within specific temperature and salinity ranges and increased with longitude and specific latitudinal bands. Month emerged as the most influential variable, explaining 38% of the CPUE variance, emphasizing the impact of regulatory measures on fishery performance. The GAM model performed better, explaining 69.9% of the nominal CPUE variance. The time series of nominal and standardized indices indicated strong seasonal cycles, and the application of fine-scale fishing effort improved nominal and standardized CPUE estimates and model performance.

On approaches to solving problems when modeling polymer flooding at the Kalamkas oil field

Background: Currently, polymer flooding is one of the most effective methods for increasing reservoir recovery, accordingly and modeling this process is of particular relevance.&#x0D; Aim: The purpose of hydrodynamic modeling is to predict the distribution of parameters, technological indicators, and simulate all possible development scenarios. Based on the simulation results, decisions are made on the profitability of projects.&#x0D; Materials and methods: There are a number of significant problems in the process of hydrodynamic modeling, one of which is adaptation. Difficulties with adaptation are mainly associated with the incorrect determination of filtration – capacitive properties, which is directly caused by the lack of core research data. The main physical parameters that determine the filtration-capacitive properties of reservoir rocks are porosity, permeability, relative phase permeabilities, and saturation. These properties are critical for accurate fluid flow modeling and production forecasting. However, the lack of core data limits our understanding of these properties and affects the quality of model fit.&#x0D; Due to the insufficient data on the oil field in this Vostok site of horizon Ю-1 of the Kalamkas field, the approved initial geological reserves differ from the reserves according to the model by approximately 20%. For a more accurate adaptation of the hydrodynamic model, the availability of current initial geological reserves is significantly insufficient.&#x0D; Results: In this article, a number of approaches were applied to solve the above-mentioned problem in the hydrodynamic modeling of polymer flooding in the Kalamkas oil field, and as a result, the results obtained were demonstrated.&#x0D; Conclusion: Hydrodynamic modeling allows us to conduct numerical experiments to optimize the parameters of polymer flooding, helps to study their influence and select the optimal ratio to improve the efficiency of the flooding process.

Patient safety culture in nurses' clinical practice.

to assess the psychometric characteristics of the Hospital Survey on Patient Safety Culture, to characterize the patient safety culture, and to assess the influence of the sociodemographic and professional variables on the safety culture dimensions. a methodological, observational, analytical and cross-sectional study conducted with 360 nurses in which the Hospital Survey on Patient Safety Culture questionnaire was used. The data were submitted to descriptive and inferential analysis, as well as to feasibility and validity studies. the nurses' mean age is 42 years old, their mean time of professional experience is 19 years, and they are mostly female. Good internal consistency was obtained (Cronbach's alpha: 0.83), as well as acceptable model fit quality indices. Teamwork within units, Supervisor expectations and Feedback and communication about errors were the dimensions that obtained scores above 60%. Non-punitive response to error, Frequency of events reported, Support for patient safety and Staffing presented scores below 40%. These dimensions are influenced by age, schooling level and professional experience. the psychometric properties of the questionnaire certify its good quality. Teamwork can be considered as an enhancing factor for the safety culture. Assessing the safety culture allowed identifying problematic dimensions, thus enabling planning of future interventions.

Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p &lt; 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.

Minimum design requirements for a poroelastic mimic of articular cartilage

The exceptional functional performance of articular cartilage (load-bearing and lubrication) is attributed to its poroelastic structure and resulting interstitial fluid pressure. Despite this, there remains no engineered cartilage repair material capable of achieving physiologically relevant poroelasticity. In this work we develop in silico models to guide the design approach for poroelastic mimics of articular cartilage. We implement the constitutive models in FEBio, a PDE solver for multiphasic mechanics problems in biological and soft materials. We investigate the influence of strain rate, boundary conditions at the contact interface, and fiber modulus on the reaction force and load sharing between the solid and fluid phases. The results agree with the existing literature that when fibers are incorporated the fraction of load supported by fluid pressure is greatly amplified and increases with the fiber modulus. This result demonstrates that a stiff fibrous phase is a primary design requirement for poroelastic mimics of articular cartilage. The poroelastic model is fit to experimental stress-relaxation data from bovine and porcine cartilage to determine if sufficient design constraints have been identified. In addition, we fit experimental data from FiHy™, an engineered material which is claimed to be poroelastic. The fiber-reinforced poroelastic model was able to capture the primary physics of these materials and demonstrates that FiHy™ is beginning to approach a cartilage-like poroelastic response. We also develop a fiber-reinforced poroelastic model with a bonded interface (rigid contact) to fit stress relaxation data from an osteochondral explant and FiHy™ + bone substitute. The model fit quality is similar for both the chondral and osteochondral configurations and clearly captures the first order physics. Based on this, we propose that physiological poroelastic mimics of articular cartilage should be developed under a fiber-reinforced poroelastic framework.

Changepoint Detection: An Analysis of the Central England Temperature Series

Abstract This paper presents a statistical analysis of structural changes in the Central England temperature series, one of the longest surface temperature records available. A changepoint analysis is performed to detect abrupt changes, which can be regarded as a preliminary step before further analysis is conducted to identify the causes of the changes (e.g., artificial, human-induced, or natural variability). Regression models with structural breaks, including mean and trend shifts, are fitted to the series and compared via two commonly used multiple changepoint penalized likelihood criteria that balance model fit quality (as measured by likelihood) against parsimony considerations. Our changepoint model fits, with independent and short-memory errors, are also compared with a different class of models termed long-memory models that have been previously used by other authors to describe persistence features in temperature series. In the end, the optimal model is judged to be one containing a changepoint in the late 1980s, with a transition to an intensified warming regime. This timing and warming conclusion is consistent across changepoint models compared in this analysis. The variability of the series is not found to be significantly changing, and shift features are judged to be more plausible than either short- or long-memory autocorrelations. The final proposed model is one including trend shifts (both intercept and slope parameters) with independent errors. The analysis serves as a walk-through tutorial of different changepoint techniques, illustrating what can be statistically inferred.

Reliability and Validity Testing of the Caregiver-Centered Communication Questionnaire.

Objective: The purpose of this study is to test the reliability and construct validity of a new instrument, the Caregiver-Centered Communication Questionnaire (CCCQ), designed to assess the extent to which family caregivers feel their perspectives and needs are appropriately acknowledged and addressed by the healthcare team. Methods: We administered the CCCQ to adult family caregivers of hospice patients. We calculated Cronbach's alpha and performed structural equation modeling. Following the estimation of the congeneric model, modification indices (MI) were examined to establish cross-loading. We used RMSEA, CFI and TLI, and SRMR to determine the quality of model fit. Results: A total of 525 caregivers completed the CCCQ instrument. The Cronbach's alpha was α = 0.98, indicating good reliability. Our final model demonstrated good fit to our data, χ2(354) = 711.653, RMSEA = 0.044(0.039, 0.049), CFI = 0.99, TLI = 0.99, SRMR = 0.029. Conclusion: Caregivers are asked to play an ever-increasing role in managing aspects of care. This role is not fully reflected in available toolkits to assess processes of care, and the CCCQ, designed to address this gap, was found to be reliable and valid. Assessing caregiver centered communication should and can be integrated into the evaluation of health care services delivered to patients and families.

Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA).

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA.

Multivariate spatial conditional extremes for extreme ocean environments

The joint extremal spatial dependence of wind speed and significant wave height in the North East Atlantic is quantified using Metop satellite scatterometer and hindcast observations for the period 2007–2018, and a multivariate spatial conditional extremes (MSCE) model, ultimately motivated by the work of Heffernan and Tawn (2004). The analysis involves (a) registering individual satellite swaths and corresponding hindcast data onto a template transect (running approximately north-east to south-west, between the British Isles and Iceland), (b) non-stationary directional-seasonal marginal extreme value analysis at a set of registration locations on the transect, (c) transformation from physical to standard Laplace scale using the fitted marginal model, (d) estimation of the MSCE model on the set of registration locations, and assessment of quality of model fit. A joint model is estimated for three spatial quantities: Metop wind speed, hindcast wind speed and hindcast significant wave height. Results suggest that, when conditioning on extreme Metop wind speed, extremal spatial dependence for all three quantities decays over approximately 600–800 km.

Modelling of Blood Lactate Time-Courses During Exercise and/or the Subsequent Recovery: Limitations and Few Perspectives.

Because lactate is an important metabolic intermediate and a signalling molecule between/within cells/organs, it appears essential to be able to describe the kinetics of this central molecule, during and/or after physical exercise. The present study aimed to confront three models and their approaches [Freund and co-workers (F&co), Beneke and co-workers (B&co), and Quittmann and co-workers (Q&co)] to investigate the lactate exchange (γ1) and removal (γ2) abilities (min−1) during and/or after exercise. Nine healthy male subjects performed 3- and 6-min easy, moderate, and heavy exercise. Blood lactate concentration (BLC) was measured every 5 s over the entire period of exercise and recovery. Approaches differ depending on the domain in which the model is applied: considering exercise and part of the recovery (B&co and Q&co) or the entire period of recovery (F&co). The different approaches result in differing γ1 and γ2 values. Model fitting is closer to the experimental values following the method (model and approach) of F&co. Complementary analyses show that consideration of (i) exercise drastically impairs the quality of model fitting and therefore the γ1 and γ2 values and (ii) the entire period of recovery considerably improves the quality of fits and therefore of the γ1 and γ2 values. We conclude that (i) it is neither realistic nor reliable to take into account exercise and recovery in the same model and (ii) the longer the period of recovery studied, the better the quality of the γ1 and γ2 values.

Calibration, validation and sensitivity analysis of a surface-based ADM1 model

A surface-based kinetic model for the anaerobic digestion of potato waste was proposed. The model was calibrated and validated, and a local sensitivity analysis was performed to investigate the most sensitive parameters. The model consisted of a modified Anaerobic Digestion Model n.1 where the disintegration process was defined through a surface-based approach able to account for the influence of the particle size on the process development. Ad-hoc experiments were carried out to calibrate and validate the model at a laboratory scale. The calibration and validation procedures accounted for the methane production and organic acid concentrations observed during experimental tests. The quality of model fitting with lab-scale data was evaluated by the Modeling Efficiency, the Index of Agreement, and the Root Mean Square Error methods. Results confirmed the high accuracy of the model for the bio-methane and organic by-products prediction during the anaerobic conversion of potato waste.

Informing Pharmacokinetic Models With Physiological Data: Oral Population Modeling of L-Serine in Humans.

To determine how to set optimal oral L-serine (serine) dose levels for a clinical trial, existing literature was surveyed. Data sufficient to set the dose was inadequate, and so an (n = 10) phase I-A calibration trial was performed, administering serine with and without other oral agents. We analyzed the trial and the literature data using pharmacokinetic (PK) modeling and statistical analysis. The therapeutic goal is to modulate specific serine-related metabolic pathways in the liver using the lowest possible dose which gives the desired effect since the upper bound was expected to be limited by toxicity. A standard PK approach, in which a common model structure was selected using a fit to data, yielded a model with a single central compartment corresponding to plasma, clearance from that compartment, and an endogenous source of serine. To improve conditioning, a parametric structure was changed to estimate ratios (bioavailability over volume, for example). Model fit quality was improved and the uncertainty in estimated parameters was reduced. Because of the particular interest in the fate of serine, the model was used to estimate whether serine is consumed in the gut, absorbed by the liver, or entered the blood in either a free state, or in a protein- or tissue-bound state that is not measured by our assay. The PK model structure was set up to represent relevant physiology, and this quantitative systems biology approach allowed a broader set of physiological data to be used to narrow parameter and prediction confidence intervals, and to better understand the biological meaning of the data. The model results allowed us to determine the optimal human dose for future trials, including a trial design component including IV and tracer studies. A key contribution is that we were able to use human physiological data from the literature to inform the PK model and to set reasonable bounds on parameters, and to improve model conditioning. Leveraging literature data produced a more predictive, useful model.

Combined utility of blood glucose and white blood cell in predicting outcome after acute ischemic stroke: The ENCHANTED trial

BackgroundAs hyperglycemia and leukocytosis individually predict poor outcome in acute ischemic stroke (AIS), we aimed to determine the significance of their combination on functional outcome and symptomatic intracerebral hemorrhage (sICH) among participants of the Enhanced Control of Hypertension and Thrombolysis Stroke Study (ENCHANTED). MethodsPost-hoc analyzes of the full ENCHANTED cohort, an international, multicenter, quasi-factorial, randomized, open, blinded outcome-assessed trial of low- versus standard-dose intravenous alteplase and early intensive versus standard blood pressure (BaP) lowering treatment in 4557 thrombolysis-eligible and treated AIS patients. Patients were divided into four groups according to baseline blood glucose and white blood cells (WBC) levels: A (normal glucose + WBC), B (hyperglycemia + normal WBC), C (normal glucose + high WBC), and D (hyperglycemia + high WBC). Logistic regression models were used to determine associations of each group and poor functional outcome (modified Rankin scale scores 2−6) at 90 days and sICH within 48 h, adjusted for confounders. Quality of model fit was examined with Akaike information classification (AIC), Bayesian information classification (BIC), and likelihood ratio test. ResultsOf 4181 AIS patients included in analyzes, and with group A as the reference, an increasing odds of poor functional outcome was evident across groups B (odds ratio [OR] 1.38, 95 % confidence interval [CI] 1.17−1.63), C (OR 1.26, 95 %CI 0.99−1.60), and D (OR 2.26, 95 %CI 1.79−2.85) (P trend <0.001). Group D patients also had a higher rate of sICH (P trend <0.05). The model fit with the combination of blood glucose and WBC was better than models of their individual components. ConclusionsUsing a combination of blood glucose and WBC provides strong prognostic significance than either alone in thrombolyzed AIS patients.

Using response surface methodology (RSM) for optimizing turbidity removal by electrocoagulation/electro-flotation in an internal loop airlift reactor

Abstract Response surface methodology (RSM) is used to optimize the electrocoagulation/electro-flotation process applied for the removal of turbidity from surface water in an internal loop airlift reactor. Two flat aluminium electrodes are used in monopolar arrangement for the production of coagulants. The central composite design is used as a second-order mathematical model. The model describes the change of the measured responses of turbidity removal efficiency and energy consumption according to the initial conductivity (X1), applied voltage (X2), treatment time (X3) and inter-electrode distance (X4). The evaluation of the model fit quality is done by analysis of variance (ANOVA). Fisher's F-test is used to provide information about the linear, interaction and quadratic effects of factors. Multicriteria methodology, mainly the desirability function (D), is used to determine optimal conditions. The results show that, for a maximal desirability function D = 0.79, optimal conditions estimated are X1 = 1,487 μS/cm, X2 = 5 V, X3 = 6.5 min, X4 = 14 mm. The corresponding turbidity removal rate and energy consumption are 84.15% and 0.215 kWh/m3 respectively. A confirmation study is then carried out at laboratory scale using the optimal conditions estimated. The results show a turbidity removal rate of 72.05% and an energy consumption of 0.210 kWh/m3.

Improving RANSAC for Efficient and Precise Model Fitting with Statistical Analysis

RANSAC (random sample consensus) has been widely used as a benchmark algorithm for model fitting in the presence of outliers for more than thirty years. It is robust for outlier removal and rough model fitting, but neither reliable nor efficient enough for many applications where precision and time is critical. Many other algorithms have been proposed for the improvement of RANSAC. However, no much effort has been done to systematically tackle its limitations on model fitting repeatability, quality indication, iteration termination, and multi-model fitting.A new paradigm, named as SASAC (statistical analysis for sample consensus), is introduced in this paper to relinquish the limitations of RANSAC above. Unlike RANSAC that does not consider sampling noise, which is true in most sampling cases, a term named as ? rate is defined in SASAC. It is used both as an indicator for the quality of model fitting and as a criterion for terminating iterative model searching. Iterative least square is advisably integrated in SASAC for optimal model estimation, and a strategy is proposed to handle a multi-model situation.Experiment results for linear and quadratic function model fitting demonstrate that SASAC can significantly improve the quality and reliability of model fitting and largely reduce the number of iterations for model searching. Using the ? rate as an indicator for the quality of model fitting can effectively avoid wrongly estimated model. In addition, SASAC works very well to a multi-model dataset and can provide reliable estimations to all the models. SASAC can be combined with RANSAC and its variants to dramatically improve their performance.

To Cure or Not to Cure: Consequences of Immunological Interactions in CML Treatment.

Recent clinical findings in chronic myeloid leukemia (CML) patients suggest that the number and function of immune effector cells are modulated by tyrosine kinase inhibitors (TKI) treatment. There is further evidence that the success or failure of treatment cessation at least partly depends on the patients immunological constitution. Here, we propose a general ODE model to functionally describe the interactions between immune effector cells with leukemic cells during the TKI treatment of CML. In total, we consider 20 different sub-models, which assume different functional interactions between immune effector and leukemic cells. We show that quantitative criteria, which are purely based on the quality of model fitting, are not able to identify optimal models. On the other hand, the application of qualitative criteria based on a dynamical system framework allowed us to identify nine of those models as more suitable than the others to describe clinically observed patterns and, thereby, to derive conclusion about the underlying mechanisms. Additionally, including aspects of early CML onset, we can demonstrate that certain critical parameters, such as the strength of immune response or leukemia proliferation rate, need to change during CML growth prior to diagnosis, leading to bifurcations that alter the attractor landscape. Finally, we show that the crucial parameters determining the outcome of treatment cessation are not identifiable with tumor load data only, thereby highlighting the need to measure immune cell number and function to properly derive mathematical models with predictive power.

CID Models on Real-world Social Networks and Goodness of Fit Measurements

Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.