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PubMed2002

The role of orthographic and phonological codes in the word and the pseudoword superiority effect: an analysis by means of multinomial processing tree models.

Central to the current accounts of the word and the pseudoword superiority effect (WSE and PWSE, respectively) is the concept of a unitized code that is less susceptible to masking than single-letter codes. Current explanations of the WSE and PWSE assume that this unitized code is orthographic, explaining these phenomena by the assumption of dual read-out from unitized and single-letter codes. In this article, orthographic dual read-out models are compared with a phonological dual read-out model (which is based on the assumption that the 1st unitized code is phonological). From this phonological code, an orthographic code is derived, through either lexical access or assembly. Comparison of the orthographic and phonological dual read-out models was performed by formulating both models as multinomial processing tree models. From an application of these models to the data of 2 letter identification experiments, it was clear that the orthographic dual read-out models are insufficient as an explanation of the PWSE, whereas the phonological dual read-out model is sufficient.

Cognition↗

PubMed2002

Identifying patients for blood conservation strategies.

BACKGROUND: Generally, only the type of operation is used to estimate the need for perioperative homologous blood transfusion. This study quantified the extent to which the estimation could be improved if, in addition, simple patient characteristics were taken into account. METHODS: Retrospective data on 24 509 consecutive adult surgical patients were used to derive and validate three models to predict perioperative homologous transfusion. The first model was a univariable model with type of operation as the only predictor. The second and third models were a full and a simplified multivariable logistic regression model. The performance of the multivariable models was tested in two validation sets: in similar patients who had operations in the same general hospital (internal validation) and in patients who had operations in a university hospital (external validation). The areas under the receiver-operator characteristic (ROC) curve were compared with that found in the derivation set. RESULTS: There were no important differences in characteristics between the derivation and validation sets. The ROC area of the model including surgery only was 0.92 (99 per cent confidence interval (c.i.) 0.91 to 0.94) and that of the full and simplified multivariable models 0.95 (99 per cent c.i. 0.94 to 0.96) and 0.94 (99 per cent c.i. 0.93 to 0.95) respectively. The latter two were significantly different from the first one. In the external validation set the ROC area of the simplified model was 0.84 (95 per cent c.i. 0.83 to 0.86). Patients who had a preoperative haemoglobin level lower than 13 g/dl and underwent major invasive surgery had the highest risk (43 per cent) of transfusion. CONCLUSION: A simple algorithm using type of operation and haemoglobin concentration was effective in identifying patients likely to need perioperative homologous blood transfusion.

Adult↗

PubMed2003

Bayesian forecasting of oral cyclosporin pharmacokinetics in stable lung transplant recipients with and without cystic fibrosis.

The aims of the current study were (1) to study Neoral pharmacokinetics (PK) in stable lung recipients with or without cystic fibrosis (CF), (2) to compare Neoral PK between these two groups, and (3) to design Bayesian estimators for PK forecasting and dose adjustment in these patients using a limited number of blood samples. The individual PK of 19 adult lung transplant recipients, 9 subjects with CF and 10 subjects without CF, were retrospectively studied. Three profiles obtained within 5 days were available for each patient. A PK model combining a gamma distribution to describe the absorption profile and a two-compartment model were applied. Different exposure indices were estimated using nonlinear regression and Bayesian estimation. The PK model developed reliably described the individual PK of Neoral in lung transplant patients with and without CF, and the values of the first and second half-lives were different in these two populations (lambda(1) = 4.14 +/- 3.01 vs. 2.16 +/- 1.75 h(-1); P < 0.01; lambda(2) = 0.36 +/- 0.11 vs. 0.49 +/- 0.12 h(-1); P < 0.01), while the mean absorption time and standard deviation of absorption time tended to be less in patients with cystic fibrosis (P < 0.1). Also, the patients with CF required higher doses than those without CF to achieve similar drug exposure. Consequently, population modeling was performed in CF and non-CF patients separately. Bayesian estimation allowed accurate prediction of AUC(0-12), AUC(0-4), C(max), and T(max) using three blood samples collected at T0h, T1h, and T3h in both groups. This study demonstrated the applicability and good performance of the PK model previously developed for oral cyclosporin and of the MAP Bayesian estimation of cyclosporin systemic exposure in CF and non-CF patients. Moreover, it is the first to propose a monitoring tool specifically designed for cyclosporin monitoring in patients with CF.

Administration, Oral↗

PubMed2006

Clinical and ultrasound parameters to predict the risk of cesarean delivery after induction of labor.

OBJECTIVE: To evaluate whether factors in the maternal history and/or ultrasound parameters are useful in predicting the risk of cesarean delivery after induction of labor. METHODS: Maternal age, height, body mass index, parity, gestational age, Bishop score, ultrasonic amniotic fluid volume, fetal head position, estimated fetal weight, and transvaginal cervical length were studied prospectively in 267 women at 36 or more weeks of gestation immediately before induction of labor. Logistic regression analysis was used to determine which factors best predicted the risk of cesarean delivery. Receiver operating characteristic curves and a resampling technique were used to evaluate the model's performance. RESULTS: Eighty (30%) of these 267 women had cesarean delivery. Logistic regression was performed and a final model chosen, which included parity (odds ratio [OR] 20.56, 95% confidence interval [CI] 7.97-53.05, P < .001), body mass index (OR 6.17, 95% CI 2.10-18.13, P < .001), height (OR 0.94, 95% CI 0.89-0.98, P = .005), and ultrasonic transvaginal cervical length (OR 1.07, 95% CI 1.04-1.11, P < .001) as the best predictors of cesarean delivery. A risk score was calculated containing these 4 parameters, which predicted reasonably accurately the risk of cesarean delivery. CONCLUSION: Parity, body mass index, height, and ultrasonic transvaginal cervical length are the most accurate parameters in predicting the risk of cesarean delivery after induction of labor. A predictive model using these would allow more accurate counseling and better informed consent in the decision-making process regarding induction of labor LEVEL OF EVIDENCE: II-2.

Adult↗

PubMed2006

Common angiotensin receptor blockers may directly modulate the immune system via VDR, PPAR and CCR2b.

BACKGROUND: There have been indications that common Angiotensin Receptor Blockers (ARBs) may be exerting anti-inflammatory actions by directly modulating the immune system. We decided to use molecular modelling to rapidly assess which of the potential targets might justify the expense of detailed laboratory validation. We first studied the VDR nuclear receptor, which is activated by the secosteroid hormone 1,25-dihydroxyvitamin-D. This receptor mediates the expression of regulators as ubiquitous as GnRH (Gonadatrophin hormone releasing hormone) and the Parathyroid Hormone (PTH). Additionally we examined Peroxisome Proliferator-Activated Receptor Gamma (PPARgamma), which affects the function of phagocytic cells, and the C-CChemokine Receptor, type 2b, (CCR2b), which recruits monocytes to the site of inflammatory immune challenge. RESULTS: Telmisartan was predicted to strongly antagonize (Ki asymptotically equal to 0.04 nmol) the VDR. The ARBs Olmesartan, Irbesartan and Valsartan (Ki asymptotically equal to10 nmol) are likely to be useful VDR antagonists at typical in-vivo concentrations. Candesartan (Ki asymptotically equal to 30 nmol) and Losartan (Ki asymptotically equal to 70 nmol) may also usefully inhibit the VDR. Telmisartan is a strong modulator of PPARgamma (Ki asymptotically equal to 0.3 nmol), while Losartan (Ki asymptotically equal to 3 nmol), Irbesartan (Ki asymptotically equal to 6 nmol), Olmesartan and Valsartan (Ki asymptotically equal to 12 nmol) also seem likely to have significant PPAR modulatory activity. Olmesartan and Irbesartan (Ki asymptotically equal to 9 nmol) additionally act as antagonists of a theoretical model of CCR2b. Initial validation of this CCR2b model was performed, and a proposed model for the Angiotensin II Type1 receptor (AT2R1) has been presented. CONCLUSION: Molecular modeling has proven valuable to generate testable hypotheses concerning receptor/ligand binding and is an important tool in drug design. ARBs were designed to act as antagonists for AT2R1, and it was not surprising to discover their affinity for the structurally similar CCR2b. However, this study also found evidence that ARBs modulate the activation of two key nuclear receptors-VDR and PPARgamma. If our simulations are confirmed by experiment, it is possible that ARBs may become useful as potent anti-inflammatory agents, in addition to their current indication as cardiovascular drugs.

Amino Acid Sequence↗

PubMed2006

From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles.

BACKGROUND: Aim of the study was to identify the conditions under which spatial-environmental models can be used for the improved understanding of species distributions, under the explicit criterion of model predictive performance. I constructed distribution models for 17 amphibian and 21 reptile species in Portugal from atlas data and 13 selected ecological variables with stepwise logistic regression and a geographic information system. Models constructed for Portugal were extrapolated over Spain and tested against range maps and atlas data. RESULTS: Descriptive model precision ranged from 'fair' to 'very good' for 12 species showing a range border inside Portugal ('edge species', kappa (k) 0.35-0.89, average 0.57) and was at best 'moderate' for 26 species with a countrywide Portuguese distribution ('non-edge species', k = 0.03-0.54, average 0.29). The accuracy of the prediction for Spain was significantly related to the precision of the descriptive model for the group of edge species and not for the countrywide species. In the latter group data were consistently better captured with the single variable search-effort than by the panel of environmental data. CONCLUSION: Atlas data in presence-absence format are often inadequate to model the distribution of species if the considered area does not include part of the range border. Conversely, distribution models for edge-species, especially those displaying high precision, may help in the correct identification of parameters underlying the species range and assist with the informed choice of conservation measures.

Journal Article↗

PubMed2004

Periodontal repair in dogs: analysis of histometric assessments in the supraalveolar periodontal defect model.

BACKGROUND: Traditionally, the mean value from histometric assessments of several histologic sections from the same site has been, used for the histometric characterization of the site. The histometric analysis of the supraalveolar periodontal defect model uses observations from buccal and lingual sites in three step-serial sections representing the central aspect of the mesial and distal root for the third and fourth mandibular premolar teeth. The objectives of this study were to investigate the correlations and determine whether there are significant differences in the measurement values of various histometric parameters between the three step-serial sections and to test the hypothesis that, in this animal model, histometric assessments using the most central section of each root are comparable to the mathematical means of corresponding assessments of the site using the three step-serial sections. METHODS: Histometric analysis using light microscopy, an attached digital camera system, and a personal computer-based image analysis system including a custom program for the supraalveolar periodontal defect model was performed on histologic sections acquired from one jaw quadrant in each of 12 dogs. The animals had received a protocol including placement of a coral biomaterial and guided tissue regeneration (GTR) membranes, and were evaluated following a 4-week healing interval. Twelve parameters were assessed using three central step-serial sections from each root. For each parameter, pair-wise comparisons were performed using the mixed models analysis of variance, and the correlations between measurements were assessed by the intraclass correlation coefficient (ICC). RESULTS: For all parameters assessed, there were no significant differences (P > 0.05) among the three sections. The differences between the central section and the means of the three step-serial sections were small and not statistically significant. Parameters that could be assessed on most or all specimens, such as defect height and area, membrane height, and bone regeneration area, showed high correlations among the three step-serial sections (intraclass coefficient [ICC] approximately 0.91 to 0.98), and between each of the three sections and the mean for the three step-serial sections (ICC > or = 0.95). The most central section had somewhat higher correlation with the mean than measurements made on the lateral sections. CONCLUSION: The results suggest that using the supraalveolar periodontal defect model representative histometric data can be obtained evaluating only the central section from each defect site.

Animals↗

PubMed2004

[Determinant factors of community-based disaster preparedness: a case study of flood prone area].

This study examined the determinants of willingness to participate in a community-based disaster preparedness scheme. The theory of reasoned action (Ajzen & Fishbein, 1977, 1980; Fishbein & Ajzen, 1975) was applied as a basic model and was complemented by the factor of concern about disaster. A structural equation modeling was performed to validate this model. The hypothetical model was supported for the data from the residents (N = 3 036) of an area with a high risk of flood damage. It was clarified that the subjective norm and concern about flood positively contributed to the intention of participating in a community-based disaster preparedness scheme. The perceived cost of preparedness was the inhibitory factor of participating in such a scheme.

Adult↗

PubMed1998

Hypothetical neural mechanism that may play a role in mental rotation: an attractor neural network model.

We propose an attractor neural network (ANN) model that performs rotation-invariant pattern recognition in such a way that it can account for a neural mechanism being involved in the image transformation accompanying the experience of mental rotation. We compared the performance of our ANN model with the results of the chronometric psychophysical experiments of Cooper and Shepard (Cooper L A and Shepard R N 1973 Visual Information Processing (New York: Academic) pp 204-7) on discrimination of alphanumeric characters presented in various angular departures from their canonical upright position. Comparing the times required for pattern retrieval in its canonical upright position with the reaction times of human subjects, we found agreement in that (i) retrieval times for clockwise and anticlockwise departures of the same angular magnitude (up to 180 degrees) were not different, (ii) retrieval times increased with departure from upright and (iii) increased more sharply as departure from upright approached 180 degrees. The rotation-invariant retrieval of the activity pattern has been accomplished by means of the modified algorithm of Dotsenko (Dotsenko V S 1988 J. Phys. A: Math. Gen. 21 L783-7) proposed for translation-, rotation- and size-invariant pattern recognition, which uses relaxation of neuronal firing thresholds to guide the evolution of the ANN in state space towards the desired memory attractor. The dynamics of neuronal relaxation has been modified for storage and retrieval of low-activity patterns and the original gradient optimization of threshold dynamics has been replaced with optimization by simulated annealing.

Algorithms↗

PubMed2004

Probing Norwalk-like virus presence in shellfish, using artificial neural networks.

A database was examined using artificial neural network (ANN) models to investigate the efficacy of predicting PCR-identified Norwalk-like virus presence and absence in shellfish. The relative importance of variables in the model and the predictive power obtained by application of ANN modelling methods were compared with previously developed logistic regression models. In addition, two country-specific datasets were analysed separately with ANN models to determine if the relative importance of the input variables was similar for geographically diverse regions. The results of this analysis found that ANN models predicted Norwalk-like virus presence and absence in shellfish with equivalent, and better, precision than logistic regression models. For overall classification performance, ANN modelling had a rate of 93%, vs 75% for the logistic regression. ANN models were able to illuminate the site-specific relationships between indicators and pathogens.

Databases, Factual↗

PubMed1995

Using knowledge maintenance for preference assessment.

Most real-life decisions require the decision maker to make trade-offs in order to fulfill multiple conflicting objectives. This is especially true in medical decision making while selecting the optimal therapy plan from among competing therapy plans for a patient. Multi-attribute utility theory provides a framework to specify these trade-offs for optimal decision making based on the preferences of the decision maker. However traditional preference-assessment techniques are difficult to implement and rarely elicit the true preferences of the decision maker. We describe a new preference-assessment method based on the concept of knowledge maintenance where the preference model is changed each time it makes an incorrect recommendation. The method is implemented in a decision-theoretic system to evaluate competing three-dimensional radiation treatment plans. The preference-assessment method leads to preference models which perform better than preference models elicited using traditional assessment techniques.

Abdominal Neoplasms↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2004

Modeling the chemical protective performance of NBC clothing material.

The heat load, imposed by air-permeable NBC (nuclear, biological, and chemical)-protective suits, can be reduced by improving the air permeability of the suit. However, increased air permeability will reduce the chemical protective performance, since increasing the air permeability of the NBC-protective material will result in higher air velocities through the material. In this study the relation between the chemical protective performance and air velocity through NBC-clothing is evaluated. A theoretical model was developed that describes the chemical protection of air permeable NBC-protective clothing material under various conditions. The initial breakthrough concentration and the 50% breakthrough time are modeled as function of parameters like the air velocity and the challenge concentration. Using this model, the effect of airflow through the material on the breakthrough concentration of mustard vapor was calculated and compared with results of breakthrough experiments. The predictions of the model are in good agreement with the experimental results. The air velocity through the material and thus the air permeability of the material appear to be parameters of critical importance. High air velocity through the material results in high breakthrough concentrations, and therefore poor protective performance of the material. To describe the total breakthrough curve, a semiempirical model of experimental breakthrough results was made. This model describes the total breakthrough concentration of vapor through NBC-protective material as a function of parameters like the air velocity and the challenge concentration. This model can be used as a tool to optimise the protective performance of NBC-protective clothing material.

Adsorption↗

PubMed2004

Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

OBJECTIVE: To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. DESIGN: Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. SETTING: Models were replicated in 3 populations. PARTICIPANTS: Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Inpatient, outpatient, and total days of care in FY97. RESULTS: The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). CONCLUSIONS: The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

Activities of Daily Living↗

PubMedDate not supplied

An in vitro test model to study the performance and thrombogenicity of cardiovascular devices.

During the development of cardiovascular devices, it is necessary to evaluate their performance in vitro under experimental conditions that closely resemble their use in vivo. Therefore, an in vitro test model was developed to evaluate the performance of cardiovascular devices with blood. The in vitro test model consists of a pneumatically driven 50 cc polyurethane ventricle with either mechanical or experimental polyurethane tricuspid semilunar valves, connected to a horseshoe-shaped blood reservoir. A sampling port permits frequent removal of blood for hematologic evaluation. All the couplings are of the quick connect type to facilitate assembly and exchange of parts. The total volume of blood in the test model is approximately 1.4 L, and experiments are conducted in the absence of an air-blood interface. Except for mechanical valves and the chosen test interface, the entire blood contacting surface consists of polyurethane. Fresh bovine heparinized blood is used for the experiments. After filling the reservoir and the ventricle with blood, the concentration of heparin is adjusted with protamine to an APTT level of three times normal (80-100 sec). Once this level of anticoagulation is achieved, the blood is circulated for the next 2-3 hr at the desired test parameters (beats/min, cardiac output, etc.). A number of hematologic parameters are monitored during the test, including APTT, PT, plasma free hemoglobin and ADP-induced platelet aggregation. With the help of this model a number of devices, including experimental polyurethane tricuspid semilunar valves, Silastic tube valves, valves with heparin coated leaflets, and small (50 cc) experimental ventricles have been studied.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMed2004

On the modeling, construction, and evaluation of a probabilistic atlas of brain perfusion.

To detect subtle, abnormal perfusion patterns in brain single photon emission computer tomography (SPECT) images, it is necessary to develop quantitative methods in which computer-aided statistical analysis takes advantage of information present in databases of normal subjects. The purpose of this study was to evaluate and examine aspects of the creation and the modeling power of three statistical models for representing brain perfusion as observed in ECD-SPECT. The first model is a local model of voxel-by-voxel mean and variance. The second model is a PCA-based global model that accounts for covariance patterns in the images. The third model is an original model that is a non-linear extension to the second model. This model is based on robust statistics for modeling abnormalities. To evaluate the models, a leave-one-out procedure combined with simulations of abnormal perfusion patterns was adopted. Abnormal perfusion patterns were simulated at different locations in the brain, with different intensities and different sizes. The procedure yields receiver operator characteristics (ROC) that present a combined measure of model-fit and model-sensitivity at detecting abnormalities. The scheme can further be used to compare models as well as the influence of different preprocessing steps. In particular, the influence of different registration approaches is studied and analyzed. The results show that the original non-linear model always performed better than the other models. Finally, location-dependent detection performance was found. Most notably, a higher variation of perfusion was observed in the right frontal cortex than in the other locations studied.

Algorithms↗

PubMed2002

Predictive performance of a physiological model for enflurane closed-circuit anaesthesia: effects of continuous cardiac output measurements and age-related solubility data.

BACKGROUND: The disposition of inhalation anaesthetics is governed by the factors described in the Fick principle. METHODS: We have recalibrated a previously validated physiological model for enflurane closed-circuit inhalation anaesthesia, using individual continuous cardiac output measurements as well as age-related enflurane solubility coefficients as inputs to the model. Two model versions using 'calculated' (Brody's formula) or 'measured' (thoracic electrical bioimpedance) cardiac output values, and two versions with 'standard' (fixed) or 'age-related' solubility coefficients were formulated. RESULTS: Data from 62 ophthalmic surgical patients were used to validate the predictive performance of the four model versions. The root mean squared errors (total error) and scatters (error variation) were similar with the extended model versions, but the group biases (systematic error component) were significantly less with the model versions that included age-related solubility compared with the versions using standard solubility coefficients (bias -0.76/-0.78% vs -3.44/-3.60%). CONCLUSION: The inclusion of age-related solubility coefficients but not of continuous cardiac output measurements improves the predictive performance of the physiological model for closed-circuit inhalation anaesthetic conditions in routine clinical practice.

Adolescent↗

PubMed2004

Performance evaluation and mathematical modeling of nitrogen reduction in waste stabilization ponds in conjunction with other treatment systems.

In this study, a widely-recognized nitrogen reduction model of waste stabilization ponds (WSPs) was tested for its universal utility in four different cases including shallow algal ponds (SAPs), WSPs followed by SAPs and water hyacinth ponds, respectively, and WSPs upgrading a secondary effluent. A goodness-of-fit study between measurements and calculated values indicates that it provides a satisfactory description of nitrogen reduction for all three cases except the WSPs treating the effluent from SAPs. This is due to the inherent structure of the model underestimating an effect of pH and hydraulic residence time for ponds system operated in series. However, when original model equation was modified as a form having a linear-relationship between reduction rate constant and pH values, it showed much better ability to reflect the process effluent. Finally, all the measured and calculated values were plotted to assess the overall performance of both models. Again, these results support an universal utility of original model, although it shows more robustness at a high range of nitrogen concentration. Meanwhile, the new approach tends to underestimate the effluent concentration at high concentrations but gives better performance at lower range.

Ecosystem↗