scholarly journals Segmental Modification of the Mualem Model by Remolded Loess

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Le-fan Wang ◽  
Xing-zhong Weng ◽  
Zhi-hua Yao ◽  
Ren-yi Zhang ◽  
Wan Li ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Linear Relation ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Variable Parameter ◽  
Dry Density ◽  
Parameter Method ◽  
Test Results ◽  
Segmentation Method ◽  
Through Diffusion

The measured diffusion coefficient and soil-water characteristic curve (SWCC) of remolded loess were used to modify the Mualem model for increasing its accuracy. The obtained results show that the goodness of fit between the Mualem model and the variable parameter-modified Mualem method comparing with the test results was not high. The saturation of 0.65 was introduced as the boundary to divide the curve of the measured diffusion coefficient into two segments. When the segmentation method combined with the variable parameter method was used to modify the Mualem model, the fitting correlation coefficient was increased to 0.921–0.998. The modified parameters Ko and L corresponding to remolded loess were calculated for different dry densities. Based on the exponential function between Ko and dry density and the linear relation between L and dry density, the segmentally modified Mualem model was established for remolded loess by considering variation in dry density. The results of the study can be used for directly determining the unsaturated infiltration coefficient and for indirectly determining the SWCC through diffusion coefficient.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shulun Nie ◽  
Yufang Zhu ◽  
Jia Yang ◽  
Tao Xin ◽  
Song Xue ◽  
...  
Keyword(s):  
Dna Methyltransferase ◽  
Goodness Of Fit ◽  
Clinical Target Volume ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
High Grade Glioma ◽  
Target Volume ◽  
Tumor Location ◽  
High Grade ◽  
Clinical Trial Registry

Abstract Introduction In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy. Materials and methods The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model. Results Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O6-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: YME = 0.672 + 0.513XGrade + 0.380XSVZ + 0.439XMGMT + 0.320XIDH + 0.333X1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001). Conclusion ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients. Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

scholarly journals RESEARCH ON MODEL FITTING AND STRENGTH CHARACTERISTICS OF CRITICAL STATE FOR EXPANSIVE SOIL

2013 ◽  
Vol 19 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Zhiqing Li ◽  
Chuan Tang ◽  
Ruilin Hu ◽  
Yingxin Zhou
Keyword(s):  
Critical State ◽  
Model Fitting ◽  
Expansive Soil ◽  
Dry Density ◽  
Initial Water Content ◽  
Test Results ◽  
Stress Strain ◽  
Initial Water ◽  
Curve Model ◽  
Shearing Strength

According to Mengzi expansive soil, consolidated drained tests and undrained tests are carried on under saturated and remoulded conditions. The stress-strain characteristics of saturated soil are researched systematically under different confining pressure, initial dry density, initial water content, shearing rate and drainage condition. The inherent unity of diversity of shearing strength for the same samples measured by different experimental methods is indicated according to the normalization of critical state test results. And the failure lines in p ‘- q - ν space of remoulded saturated expansive soil under consolidated drained and undrained conditions are attained. The hyperbolic curve model can fit well the weak hardening stress-strain curves and the exponential curve model can fit the weak softening stress-strain curves. The test results can provide technical parameters and theoretical help for shearing strength variation of slope during rainfall and strength state of soil structure in normal water level.

The Analysis of Stress Distribution of Hammer Bottom during Dynamic Compaction on Sandy Soil Foundation

2011 ◽  
Vol 250-253 ◽  
pp. 1460-1463
Author(s):  
Jian Qi Wu ◽  
Jian Hong Deng ◽  
Xiao Ping Wang
Keyword(s):  
Comparative Analysis ◽  
Stress Distribution ◽  
Sandy Soil ◽  
Field Monitoring ◽  
Dynamic Compaction ◽  
Dry Density ◽  
Test Results ◽  
Red Sandstone ◽  
Soil Foundation

Obtained stress distribution of hammer bottom according to the analysis of horizontal and vertical red sandstone fill dry density of the hammer bottom after dynamic compaction; affirmed the stress distribution situation of the hammer bottom through comparative analysis of the test results by laboratory and field monitoring.

scholarly journals Prediction of successful de-cannulation of tracheostomised patients in medical intensive care units

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chul Park ◽  
Ryoung-Eun Ko ◽  
Jinhee Jung ◽  
Soo Jin Na ◽  
Kyeongman Jeon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Multivariable Logistic Regression Analysis ◽  
Limited Data ◽  
Underlying Malignancy ◽  
Medical Intensive Care ◽  
Good Calibration

Abstract Background Limited data are available on practical predictors of successful de-cannulation among the patients who undergo tracheostomies. We evaluated factors associated with failed de-cannulations to develop a prediction model that could be easily be used at the time of weaning from MV. Methods In a retrospective cohort of 346 tracheostomised patients managed by a standardized de-cannulation program, multivariable logistic regression analysis identified variables that were independently associated with failed de-cannulation. Based on the logistic regression analysis, the new predictive scoring system for successful de-cannulation, referred to as the DECAN score, was developed and then internally validated. Results The model included age > 67 years, body mass index < 22 kg/m2, underlying malignancy, non-respiratory causes of mechanical ventilation (MV), presence of neurologic disease, vasopressor requirement, and presence of post-tracheostomy pneumonia, presence of delirium. The DECAN score was associated with good calibration (goodness-of-fit, 0.6477) and discrimination outcomes (area under the receiver operating characteristic curve 0.890, 95% CI 0.853–0.921). The optimal cut-off point for the DECAN score for the prediction of the successful de-cannulation was ≤ 5 points, and was associated with the specificities of 84.6% (95% CI 77.7–90.0) and sensitivities of 80.2% (95% CI 73.9–85.5). Conclusions The DECAN score for tracheostomised patients who are successfully weaned from prolonged MV can be computed at the time of weaning to assess the probability of de-cannulation based on readily available variables.

scholarly journals Differentiating COVID-19 and dengue from other febrile illnesses in co-epidemics: Development and internal validation of COVIDENGUE scores

2021 ◽  
Author(s):  
Patrick Gerardin ◽  
Olivier Maillard ◽  
Lea Bruneau ◽  
Frederic Accot ◽  
Florian Legrand ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Multinomial Logistic Regression ◽  
Characteristic Curve ◽  
Severe Dengue ◽  
Delayed Presentation ◽  
Upper Respiratory Tract ◽  
Discriminative Ability ◽  
Internal Validation ◽  
Logistic Regression Models ◽  
Testing Center

Background. In a retrospective cohort study, we previously distinguished the factors associated with coronavirus 2019 (COVID-19) or dengue from those associated with other febrile illnesses (OFIs). In this study, we developed a scoring system to discriminate both infectious diseases. Methods. Predictors of both infections were sought using multinomial logistic regression models (OFIs as controls) in all subjects suspected of COVID-19 who attended the SARS-CoV-2 testing center of Saint-Pierre teaching hospital, Reunion Island, between March 23 and May 10, 2020. Two COVIDENGUE scores were developed and internally validated by bootstrapping for predicting each infection after weighting the odd ratios according to a predefined rule. The discriminative ability of each score was assessed using the area under the receiver operating characteristic curve (AUC). Their calibration was assessed using goodness-of-fit statistics. Results. Over 49 days, 80 COVID-19, 60 non-severe dengue and 872 OFI cases were diagnosed. The translation of the best fit model yielded two COVIDENGUE scores composed of 11 criteria: contact with a COVID-19 positive case (+3 points for COVID-19; 0 point for dengue), return from travel abroad within 15 days (+3/-1), previous individual episode of dengue (+1/+3), active smoking (-3/0), body ache (0/+5), cough (0/-2), upper respiratory tract infection symptoms (-1/-1), anosmia (+7/-1), headache (0/+5), retro-orbital pain (-1/+5), and delayed presentation (>3 days) to hospital (+1/0). The AUC was of 0.79 (95%CI 0.76-0.82) for COVID-19 score and of 0.88 (95%CI 0.85-0.90) for dengue score. Calibration was satisfactory for COVID-19 score and excellent for dengue score. For predicting COVID-19, sensitivity was of 97% at the 0-point cut-off and specificity approximated 99% at the 10-point cut-off. For predicting dengue, sensitivity approximated 97% at the 3-point cut-off and specificity 98% at the 11-point cut-off. Conclusions. In conclusion, the COVIDENGUE scores proved discriminant to differentiate COVID-19 and dengue from other febrile illnesses in the context of SARS-CoV-2 testing center during a co-epidemic. Further studies are needed to validate or refine these scores in other settings.

scholarly journals Investigation of the Performances of a Diesel Engine Operating on Blended and Emulsified Biofuels from Rapeseed Oil

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6661
Author(s):  
Vladimir Anatolyevich Markov ◽  
Bowen Sa ◽  
Sergey Nikolaevich Devyanin ◽  
Anatoly Anatolyevich Zherdev ◽  
Pablo Ramon Vallejo Maldonado ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Rapeseed Oil ◽  
Characteristic Curve ◽  
Motor Fuel ◽  
Experimental Tests ◽  
Operating Conditions ◽  
Test Results ◽  
Cfd Simulations ◽  
Brake Thermal Efficiency ◽  
Flow Simulations

The article discusses the possibility of using blended biofuels from rapeseed oil (RO) as fuel for a diesel engine. RO blended diesel fuel (DF) and emulsified multicomponent biofuels have been investigated. Fuel physicochemical properties have been analyzed. Experimental tests of a diesel engine D-245 in the operating conditions of the external characteristic curve and the 13-mode test cycle have been conducted to investigate the effect of these fuels on engine performances. CFD simulations of the nozzle inner flow were performed for DF and ethanol-emulsified RO. The possibility of a significant improvement in brake thermal efficiency of the engine has been noted. The efficiency of using blended biofuels from RO as a motor fuel for diesel engines has been evaluated based on the experimental test results. It was shown that in comparison with the presence of RO in emulsified multicomponent biofuel, the presence of water has a more significant effect on NOx emission reduction. The content of RO and the content of water in the investigated emulsified fuels have a comparable influence on exhaust smoke reduction. Nozzle inner flow simulations show that the emulsification of RO changes its flow behaviors and cavitation regime.

Temperature effects on ageing properties and diffusivity of a HDPE GM in landfill

2021 ◽  
pp. 0734242X2110570
Author(s):  
Shengwei Wang ◽  
Tao Guo ◽  
Huan Tian ◽  
Zhigang Li ◽  
Kang Fei
Keyword(s):  
Diffusion Coefficient ◽  
Tensile Properties ◽  
Ageing Time ◽  
Melt Flow Index ◽  
Engineering Properties ◽  
Flow Index ◽  
Test Results ◽  
Oxidation Induction Time ◽  
Degradation Rates ◽  
And Migration

High-density polyethylene (HDPE) geomembranes (GMs) play a crucial role in preventing the leakage and migration of pollutants. GM service life and ageing properties are the main concerns for the choice of materials. However, it is not clear how the mechanical properties and anti-fouling performance of geomembranes change with ageing time. To solve this problem, a HDPE GM was selected for testing under exposed air condition. The tests included oxidation induction time (OIT), melt flow index (MFI), tensile properties and diffusivity under four temperature conditions for 1½ years. The test results showed that the GM has higher OIT degradation rates. Stage I – depletion of antioxidants occurred at only 10 years for the GM, which was approximately 1/4 that of the GM-GSE. The GM engineering properties index showed the same changes as those of the GM-GSE. However, MI rapidly decreased with the incubation time. The molecular weight degradation of the GM was approximately 57% and far greater than that of GM-GSE after 15 months, but the tensile properties of the two GMs showed little change. The diffusion coefficient Di of GM increases gradually with the increase of temperature in methane and trichloromethane. Under the same conditions, the diffusion coefficient Di of the GM in methane is significantly higher than that in trichloromethane, indicating that the GM has better barrier to trichloromethane.

A comparison among gamma distribution, intravoxel incoherent motion, and mono-exponential models with turbo spin-echo diffusion-weighted MR imaging in the differential diagnosis of orofacial lesions

2021 ◽  
pp. 20200609
Author(s):  
Wannakamon Panyarak ◽  
Toru Chikui ◽  
Kenji Tokumori ◽  
Yasuo Yamashita ◽  
Takeshi Kamitani ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Differential Diagnosis ◽  
Diffusion Coefficient ◽  
Malignant Lymphoma ◽  
Cell Carcinoma ◽  
Gamma Distribution ◽  
Goodness Of Fit ◽  
Spin Echo ◽  
Turbo Spin Echo ◽  
Turbo Spin

Objectives: To compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions. Methods: A total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ1, cellular component; ƒ2, extracellular diffusion; ƒ3, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC). Results: The GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ1 had strong negative correlations with D and ADC (ρ = –0.901 and –0.937, respectively), while ƒ3 had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ1 and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ1 in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ1 was opposite to the trend in D. Malignant lymphoma had significant lower ƒ3 than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075). Conclusions: The strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model’s parameters were useful for characterising the pathological structure in orofacial lesions.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

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