Diagnostic Plots Research Articles

Deep learning-based pulsar candidate identification model using a variational autoencoder

As radio telescope technology continues to advance, the discovery of more pulsar candidates is anticipated. The swift and accurate identification of pulsars has emerged as a compelling research topic in recent years, garnering growing attention. Although deep learning has been expansively applied for the rapid identification of pulsar candidates, the significant imbalance between the larger number of non-pulsar signal samples and the fewer pulsar signal samples seriously impacts the performance of deep learning prediction models. To address this, our research proposes a method that combines a Vector Quantized Variational Autoencoder (VQVAE) with a Gated PixelCNN to equalize the number of pulsar and non-pulsar samples during sample generation. Subsequently, we establish a Residual Neural Network (ResNet) model that employs two feature diagnostic plots as input to classify and identify pulsar candidates. Validation on the High Time Resolution Universe (HTRU) dataset yielded a recall and accuracy of 99.60%, and an F1-score of 99.59%. Further evaluation on the Five-hundred-meter Aperture Spherical radio Telescope (FAST) dataset demonstrated a precision of 98.5%, a recall of 97.8%, and an F1-score of 98.1%, further confirming the effectiveness of our proposed model.

State of the art of hydraulic packer testing in clay rocks in the Swiss national radioactive waste disposal program

Abstract. To provide input for site selection and the safety case for deep geological repositories for radioactive waste, Nagra has drilled a series of deep boreholes in northern Switzerland. As part of the hydrogeological characterization of this exploratory drilling campaign, hydraulic packer tests in the sedimentary rocks were carried out under the supervision of Nagra, in depths from 300 to 1130 m b.g.l. (below ground level). Single or straddle-packer hydraulic tests were conducted in different low-permeability rocks. The hydraulic testing equipment used was specially designed to reduce uncertainty associated with the low-permeability testing conditions and included a downhole hydraulic shut-in valve (DHSIV), a hydraulic piston, a probe carrier, and inflatable packer(s). Pressure and temperature are monitored with sensors connected to the bottom of the borehole, the test interval, the annulus above the upper packer, and the test tubing above the DHSIV. Hydraulic test sequences consisted of multiple withdrawal and shut-in phases. Test phases started with a compliance phase to allow some of the pressure and temperature transients caused by tool installation and packer inflation to dissipate, followed by a pressure static recovery phase to allow the test interval pressure to establish a recovery trend after being isolated. In general, in the low-permeability formations the active test sequence consisted of a pulse withdrawal performed using a hydraulically operated piston displacing a precisely defined volume, a slug withdrawal, and lastly a slug-withdrawal shut-in phase. The pressure data collected during the hydraulic tests were analyzed using the well-test simulator nSIGHTS®. The use of a numerical model is necessary for the analyses of the tests in low-permeability rocks as borehole history and temperature equilibration during the test can have a significant impact on test responses. Analyses started with the definition of a borehole-formation conceptual flow model based on geological information and interpretation of diagnostic plots. Input model parameters composed of non-fitting parameters and fitting parameters are defined. Preliminary automated calibrations are performed to identify the appropriate conceptual model and obtain baseline estimates of the fitting parameters for each tested interval. Perturbation analyses entailing hundreds to thousands of optimizations are performed to obtain the final best-fit parameter values and the corresponding uncertainty ranges. Our presentation will give an overview on methodologies and workflows with emphasis on the numerical design and analysis techniques applied to the hydraulic packer tests performed. The applied methodology provided high-quality results and reliable estimates of the hydraulic conductivities in the low-permeability rocks. The results from the hydraulic packer tests will be presented, and implications for further research by Nagra will be discussed.

Dosage optimization of tacrolimus based on the glucocorticoid dose and pharmacogenetics in adult patients with systemic lupus erythematosus

BackgroundThe purpose of the study was to develop a genotype-incorporated population pharmacokinetic (PPK) model of tacrolimus (TAC) in adults with systemic lupus erythematosus (SLE) to investigate the factors influencing TAC pharmacokinetics and to develop an individualized dosing regimen based on the model. In addition, a non-genotype-incorporated model was also established to assess its predictive performance compared to the genotype-incorporated model. MethodsA total of 365 trough concentrations from 133 adult SLE patients treated with TAC were collected to develop a genotype-incorporated PPK model and a non-genotype-incorporated PPK model of TAC using a nonlinear mixed-effects model (NONMEM). External validation of the two models was performed using data from an additional 29 patients. Goodness-of-fit diagnostic plots, bootstrap method, and normalized predictive distribution error test were used to validate the predictive performance and stability of the final models. The goodness-of-fit of the two final models was compared using the Akaike information criterion (AIC). The dosing regimen was optimized using Monte Carlo simulations based on the developed optimal model. ResultsThe typical value of the apparent clearance (CL/F) of TAC estimated in the final genotype-incorporated model was 14.3 L h−1 with inter-individual variability of 27.6%. CYP3A5 polymorphism and coadministered medication were significant factors affecting TAC-CL/F. CYP3A5 rs776746 GG genotype carriers had only 77.3% of the TAC-CL/F of AA or AG genotype carriers. Omeprazole reduced TAC-CL/F by 3.7 L h−1 when combined with TAC, while TAC-CL/F increased nonlinearly as glucocorticoid dose increased. Similar findings were demonstrated in the non-genotype-incorporated PPK model. Comparing these two models, the genotype-incorporated PPK model was superior to the non-genotype-incorporated PPK model (AIC = 643.19 vs. 657.425). Monte Carlo simulation based on the genotype-incorporated PPK model indicated that CYP3A5 rs776746 AA or AG genotype carriers required a 1/2–1 fold higher dose of TAC than GG genotype carriers to achieve the target concentration. And as the daily dose of prednisone increases, the dose of TAC required to reach the target concentration increases appropriately. ConclusionsWe developed the first pharmacogenetic-based PPK model of TAC in adult patients with SLE and proposed a dosing regimen based on glucocorticoid dose and CYP3A5 genotype according to the model, which could facilitate individualized dosing for TAC.

Development of an RSM-based predictive model for evaluation of corrosion efficiency of ATMP in one molar HCl for carbon steel samples

The corrosion control of carbon steel was investigated using ATMP (amino-trimethylene-phosphonic acid) by experimental weight loss and response surface methods. For this purpose, the simultaneous effect of the important parameters, including inhibitor concentration, temperature, and exposure time on the inhibition efficiency (IE) of ATMP was evaluated. A new RSM-based model was developed to predict ATMP effectiveness. The results obtained from Fit-statistics (R2 = 0.9946, Adj-R2 = 0.9916, and Pred-R2 = 0.9750), adequate precision (A.p = 63.1026), diagnostics plots, and PPE (Percentage-Prediction-Error) for non-designed experiments strongly confirmed the adequacy, accuracy, and validation of the developed IE-model. The results indicated that the parameters at the low levels of ATMP concentrations had the least interaction effect on the inhibition efficiency (IE< 35%). The addition of inhibitor concentration up to 150 ppm near the low level of temperature (30–40 °C) and high level of exposure time (85–105 h) had the most interaction effect between parameters and significantly enhanced the inhibition efficiency. Furthermore, numerical optimization was performed to maximize IE while minimizing the concentration of inhibitor. The optimization provided showed an inhibition performance of 80.434% at 126 ppm, 31 °C, and 26.4 h.

MFPIM: A Deep Learning Model Based on Multimodal Fusion Technology for Pulsar Identification

With the development of radio telescope technology, the quantity and types of acquired pulsar candidate data have increased dramatically. However, it is difficult to accurately identify pulsar candidates. Therefore, we propose to use multimodal fusion technology, called the multimodal fusion-based pulsar identification model (MFPIM), to build a deep learning model to improve the efficiency and accuracy of pulsar candidate identification. MFPIM treats each diagnostic plot of pulsar candidates as a modality and uses multiple convolutional neural networks to extract effective features from the diagnostic plots. After fusing the features, the commonality of different modalities in high-dimensional space is obtained to ensure that the model can take full advantage of the complementarity between diagnostic plots and thus identify pulsar candidates, achieved better classification performance than other current supervised learning algorithms. In addition, a channel attention mechanism is used in the model to enable it to learn the importance of different channel features so that the model focuses more on the channel information in the input data that is more meaningful for classification, reducing the model size while extracting pulsar diagnostic map features more accurately. We conducted experiments on the Five-hundred-meter Aperture Spherical radio Telescope (FAST) data set, and the results show that MFPIM can effectively identify the pulsars in the FAST data set with an identification accuracy of over 98%. To further verify the robustness of the model, we applied the MFPIM to the High Time Resolution Universe data set using transfer learning, with the test accuracy and F1 score reaching over 99%.

Diagnostic value of serum human epididymis protein 4, carbohydrate antigen 125 and their combination in endometrial cancer: A meta-analysis.

To systematically analyze the value of human epididymis protein 4 (HE4) and carbohydrate antigen 125 (CA125) in the diagnosis of endometrial cancer, so as to provide evidence-based medical evidence for the selection of serum tumor markers in the early screening of endometrial cancer. We comprehensively searched relevant literature in the Cochrane Library, EMBASE, PubMed, Web of Science, CNKI, VIP, WanFang, and CBM from the date of establishment to November 31, 2021. Quality assessment of diagnostic accuracy studies 2 was applied to evaluate the quality of the included literature. We used Stata 16.0 to calculate the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) and plot summary receiver operating characteristic curve, as well as to assess diagnostic accuracy using the area under the curve (AUC). A total of 25 studies, including 1980 patients and 2345 controls, were included in this meta-analysis. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of HE4 were 0.58 (95% CI 0.52-0.63), 0.95 (95% CI 0.92-0.97), 11.57 (95% CI 6.88-19.48), 0.45 (95% CI 0.39-0.51), 25.92 (95% CI 14.84-45.26), and 0.80 (95% CI 0.76-0.83), respectively. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of CA125 were 0.41 (95% CI 0.34-0.49), 0.91 (95% CI 0.85-0.95), 4.55 (95% CI 2.73-7.58), 0.65 (95% CI 0.57-0.74), 7.03 (95% CI 3.92-12.62), and 0.68 (95% CI 0.64-0.72), respectively. The pooled SEN, SPE, PLR, NLR, DOR, and AUC of HE4 + CA125 were 0.67 (95% CI 0.60-0.73), 0.92 (95% CI 0.87-0.95), 8.59 (95% CI 5.32-13.86), 0.36 (95% CI 0.30-0.44), 23.80 (95% CI 13.86-40.86), and 0.85 (95% CI 0.82-0.88), respectively. This Meta-analysis found that HE4 alone or in combination with CA125 showed better diagnostic efficacy than CA125, regardless of clinical stage and pathological type. HE4 + CA125 had slightly higher diagnostic efficiency than HE4, but did not show significant advantages. While the studies were heterogeneous, the credibility of the findings needs to be further confirmed by more homogeneous, prospective, and large sample size studies.

Application of information theory-based decision support system for high precision modeling of the length-weight relationship (LWR) for five marine shrimps from the northwestern Bay of Bengal

The study was conducted to develop an information theory-based decision support system to understand the variance distribution structure of the data so that a proper modeling approach could be implemented to explain the relationship between the body length and weight of shrimps. Based on biological reasoning, initially, a log-normal multiplicative error structure was assumed and therefore, a log-linearized model was applied. Secondly, the support for normal additive error structure was assessed by fitting a weighted nonlinear model with a power variance structure (wNLM) to address the heteroscedasticity in shrimp weight. The likelihood support for the error structures was ascertained by comparing the AICc of the two competing models. As the general cut-off criterion (ΔAICc>2.0) did not give conclusive evidence from the scrutiny of the probability density diagnostic plot of the residuals, an alternative model scaling criteria, i.e., Akaike weight (Aw) of 0.9 was used for model selection. The corresponding ΔAICc cut-off score of 4.2 was estimated by regressing the ΔAICc score of the competing model against the Aw scores of the best model. The competing models with ΔAICc > 4.2 were rejected and the alternate models with Aw ≥ 0.9 were selected for modeling the length–weight relationship. Both the models were observed to be well founded, as narrow differences in the root mean squared error (RMSE) were observed. A lower RMSE was almost always observed from wNLM despite a higher ΔAICc score, which indicates that RMSE may not be efficient in detecting the model overfitting issue. Contrary to popular belief, only 26.7% of the datasets exhibited a log-normal error structure, whereas, a normal error structure was evident in 33.3% of the datasets. Interestingly, 40.0% of the datasets showed data ambivalence (ΔAICc < 4.2) and therefore, an Akaike weighted model averaging was performed to reduce model uncertainty for the accurate estimation of model parameters and their confidence intervals.

Application of hydrogeophysics and diagnostic plots in setting up a sustainable managed aquifer recharge scheme, West Coast, South Africa

The lack of using hydrogeophysics and hydraulic data in setting up a sustainable managed aquifer recharge scheme leads to challenges in operations and maintenance of such infrastructures. Managed aquifer recharge is an engineering and technological based water management approach that is used to maximize storage and improve groundwater security. However, this approach is under-utilized in many countries and South Africa is no exception. This study aimed at improving knowledge on aquifer dynamics for managed aquifer recharge scheme using hydrogeophysics and diagnostic plots. This study argued that integrating hydrogeophysics data and diagnostic plots for setting up managed aquifer recharge schemes lead to improved operations and maintenance through improved scientific knowledge on the dynamics of the aquifer. SkyTEM and electrical resistivity tomography data were used to delineate subsurface structures. Pumping test data were used to develop diagnostic plots, for identifying flow regimes and boundary conditions in the proposed managed aquifer recharge area. Results from geophysics investigation inferred pathways for potential managed aquifer recharge and areas to avoid. The integration of hydrogeophysics and diagnostic plots provided a more practical interpretation for the study area. It is recommended that hydrogeophysics investigation should be carried out before and after setting managed aquifer recharge scheme as it leads to enhanced decisions on groundwater usage, monitoring and management.

PyNMME: A python toolkit to retrieve, calibrate and verify seasonal precipitation forecasts

The North American Multi-Model Ensemble (NMME) experiment assembles valuable ensemble forecasts from more than ten global climate models (GCMs). Focusing on environmental applications of NMME forecasts, this paper develops the pyNMME, a Python-based toolkit to implement data retrieval, forecast calibration and forecast verification. Specifically, the toolkit is composed of three modules that streamline the processes of retrieving the NMME data for locations and seasons of interest from high-dimensional datasets, calibrating raw GCM forecasts by the linear scaling, quantile mapping and Bernoulli-Gamma-Gaussian models and verifying predictive performances by twenty verification metrics and six types of diagnostic plots. The results show that the characteristics of bias, reliability and skill of raw forecasts vary across the globe and that the three calibration models in the pyNMME effectively improve raw forecasts to different extents. Overall, the pyNMME can serve as a useful tool to exploit valuable NMME precipitation forecasts for environmental modelling and management.

Estimating formation and fracturing water production using simple diagnostic plots

The main goal of this paper is to evaluate the possibility of distinguishing between formation and fracturing water based on the water-flowback response without analyzing water chemical data. We analyze flowback production data of 100 multi-fractured horizontal oil wells completed in the Montney Formation and 89 oil and gas wells completed in the Duvernay and Horn River formations. We hypothesize that 1) the slope of water-flowback harmonic decline (HD) profiles (mHD) is proportional to the slope of rate-normalized pressure (RNP) plots (mRNP), and 2) both mRNP and mHD are inversely proportional to the amount of water influx from matrix into fractures. To verify our hypothesis, we 1) extend the previous HD and single-phase flowing material balance models by considering the water influx from matrix into fractures, 2) classify the studied wells based on observed trends, 3) construct RNP diagnostic plots of the studied wells, 4) investigate the relationship between mHD and mRNP, 5) analyze log data and estimate average initial water saturation (Swi) for the studied wells, and 6) investigate the relationship between of mHD and Swi. The results show that mHD for all the studied wells ranges from 0.00001 to 0.036 1/ m3 with a mean value of 0.0056 1/ m3. Wells with higher mHD have relatively higher mRNP values. In contrast, wells with very low mHD show no significant decline in water rate through the entire flowback process and relatively low or inconsistent mRNP values on the corresponding plots of RNP versus material balance time (MBT). We also observe a negative correlation between mHD and Swi obtained by analysis of wireline log data. This correlation and the positive correlation between mHD and mRNP indicate that as Swi increases, water influx from matrix to fracture increases, and thus, both mHD and mRNP decrease. Based on our modified models and the observed correlations, we develop a method to approximately differentiate between formation and fracturing water. The applications of our heuristic method on field data shows that 1) fracturing water production dominates the total water recovery for wells with higher mRNP, and 2) formation water production dominates the total water recovery for wells with lower mRNP.

Experimental and modeling analysis on the performance of 2-mercaptobenzimidazole corrosion inhibitor in hydrochloric acid solution during acidizing in the petroleum industry

In this work, the corrosion inhibition of carbon steel in 1 molar HCl solution was evaluated by experimental and modeling approaches using 2-mercaptobenzimidazole (2-MBI). To this end, an experimental design for the weight loss method using response surface methodology (RSM) was carried out and the corrosion rate (CR) and inhibition efficiency (IE) were determined. The study was completed at various values of temperature, exposure time, and inhibitor concentration to determine the optimal conditions for corrosion prevention. Using experimental data on the corrosion rate and inhibition efficiency of 2-MBI, new models were developed, the significance of which was tested using ANOVA-analysis of variance. The developed RSM-based CR and IE models were highly accurate and reliable, and their P-values were less than 0.0001. The novelty of this study lies in the newly developed model for the evaluation of 2-MBI inhibition performance and its application to high-temperature conditions in the petroleum industry. Besides, the R2-statistics (R2, adjusted-R2, and predicted-R2), adequate precision and diagnostic plots were used as main measures to verify the accuracy and adequacy of both CR and IE models. In addition, it was observed that inhibitor concentration had the most impact on both CR and IE models compared to other parameters due to its largest F-values (561.65 for CR and 535.56 for IE models). Moreover, the results indicated that adding 140–150 ppm of 2-MBI at low-level temperatures of 30–35 °C had the most interaction effect on the performance of the corrosion inhibition process. In this case, the CR was less than 0.9 mm/y and the IE more than 94%, even after a high exposure time of 105 h. Furthermore, numerical optimization of the corrosion inhibition process for 2-MBI showed that the optimum conditions for maximum IE and minimum CR were achieved at a concentration of 115 ppm, temperature of 30.7 °C, and exposure time of 60.4 h. Under these conditions, the efficiency and corrosion rate were 92.76% and 0.53 mm/y, respectively. Finally, the adsorption of 2-MBI on the sample surface was studied at various exposure times and temperatures. In all cases, the adsorption behavior obeyed the Langmuir isotherm. In this case, the Gibbs adsorption free energy varied from − 33 to − 37 kJ/mol, which reflects both physical and chemical adsorption of the corrosion inhibitor at all tested temperatures and test times.

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long-term renal function to inform clinical trial design in kidney transplantation.

Kidney transplantation is the preferred treatment for individuals with end-stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post-transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post-transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well-known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post-transplant. Our model is a nonlinear, mixed-effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long-term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long-term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post-transplant in various clinically relevant populations.

Two-sample inference procedures under nonproportional hazards.

We introduce a new two-sample inference procedure to assess the relative performance of two groups over time. Our model-free method does not assume proportional hazards, making it suitable for scenarios where nonproportional hazards may exist. Our procedure includes a diagnostic tau plot to identify changes in hazard timing and a formal inference procedure. The tau-based measures we develop are clinically meaningful and provide interpretable estimands to summarize the treatment effect over time. Our proposed statistic is a U-statistic and exhibits a martingale structure, allowing us to construct confidence intervals and perform hypothesis testing. Our approach is robust with respect to the censoring distribution. We also demonstrate how our method can be applied for sensitivity analysis in scenarios with missing tail information due to insufficient follow-up. Without censoring, Kendall's tau estimator we propose reduces to the Wilcoxon-Mann-Whitney statistic. We evaluate our method using simulations to compare its performance with the restricted mean survival time and log-rank statistics. We also apply our approach to data from several published oncology clinical trials where nonproportional hazards may exist.

Synthesis of Green Polyethylene Glycol-Lauric Acid Demulsifier from a Natural Source and Its Application in Demulsification of Field Emulsion: Experimental and Modeling Approach

Summary Green demulsifier was synthesized through an esterification technique by using polyethylene glycol (PEG) and fatty acid (lauric acid). The synthesized demulsifier was characterized through several tests to analyze the functional groups and determined the molecular structure, thermal stability, and biodegradability of the demulsifier molecule. The performance of the synthesized demulsifier was investigated using the standard static bottle test method to break the water-in-oil (W/O) tight emulsion. Optical microscopic and viscosity studies of emulsions were also performed to understand the demulsification process and mechanism. Based on the response surface method (RSM), central composite design (CCD) was used to develop the statistical model of demulsification efficiency by considering the four most influencing factors—demulsifier concentration, water content, settling time, and temperature—and to examine the optimal condition for maximum water separation from the emulsion. The statistical model’s accuracy and significance were evaluated using analysis of variance (ANOVA) and diagnostic plots. The effect of each factor was analyzed through 3D graphs and contour maps. The result indicates that all the factors significantly influenced the demulsification efficiency with a p-value of &amp;lt;0.0001, among which the presence of water is the dominating variable. At the optimal condition, the lauric acid-PEG-demulsifier (LPED) achieves a maximum demulsification efficiency of 95% in 30.9 minutes. Furthermore, the percent absolute deviation was computed after comparing the experimental findings to those predicted by the model and it was observed that the model’s prediction accuracy was &amp;gt;97%. Finally, the biodegradability test results showed that the developed demulsifier is completely biodegradable in 21 days. Because the synthesized demulsifier is eco-friendly and has an excellent dehydration rate, it may be used in the petroleum industry for breaking field-tight emulsions as an alternative to chemical demulsifiers.

The potential of bamboo forests as a carbon sink and allometric equations for estimating their aboveground biomass

Bamboo forests in Colombia and the Andean region of South America represent high-value ecosystems that provide ecological and economic benefits with local and global impacts. One of the ecosystem services provided by these forests is associated with their capacity to store carbon. In this study, data collected from monitoring plots were used to estimate the carbon content in different pools. Bamboo biomass (Bba), tree biomass (Btree), litter (Cli) and soil organic carbon (SOC) were assessed. The approximate total ecosystem carbon stock (TECaprox) ranged from 198.4 Mg C ha−1 to 330.9 Mg C ha−1 and bamboo carbon Cba represents approximately 50%. In addition, considering the relevance of developing tools to facilitate bamboo inventory and biomass estimates, allometric equations (AE) to estimate bamboo aboveground biomass (AGB) were fitted using the diameter of culms at breast height (dbh) and the total culm length (l) as predictor variables. The fitted AEs included the weighted linear, weighted log-transformed and weighted nonlinear fixed effect models. To compliance the additivity of biomass components a simultaneous systems of biomass equations (seemingly unrelated regressions) were also fitted. The precision and accuracy were assessed considering the residual diagnostic plots and statistics, such as the root-mean-square error (RMSE), RMSE percentage error (RMSEPE) and the Furnival’s index (Fln) for weighted log-transformed models and cross-validation. The performance of the models was similar with an RMSE of approximately 10 kg and 26% of RMSEPE, with slightly lower error for the weighted log-transformed model for the fitting and validation phases. A proper performance was also evidenced for the simultaneous approach for predicting AGB. Bamboo forests showed high relevance as carbon sinks and therefore might be considered strategic tropical ecosystems for climate change mitigation. On the other hand, the fitted AE exhibited proper performance and therefore provided reliable possibilities for estimating the AGB of bamboo during inventories. For practical reasons, the use of models with dbh as a predictor variable is recommended.

Statistical modelling and optimization of FS-welded 6061-T651 Aluminum alloy

An RSM-based experimental design, mathematical modelling and statistical optimization of friction stir welding process parameters was studied. A quadratic fitting model developed from a five-leveled four-factor parametric setting predicted the Ultimate Tensile Strength (UTS) of the welded AA6061- T651 joints. Statistical analysis at 95% Confidence Interval using ANOVA validated the conformity of the developed model with experimental data and also verified the adequacy of the model for UTS prediction and optimization. Results showed that the model was statistically significant (p&lt;0.0001) with no notable lack of fit with the four parameters and their squared terms also significant statistically. The numerical optimization resulted to an optimum UTS of 166.32MPa from rotational speed, traversing speed, tool tilt angle and axial load values of 1293.641rpm, 48.467mm/min, 1.888° and 4.720kN, respectively with a desirability of 0.944. Also, 2D contour and 3D surface plots showed that the four parameters made decreasing effects on the UTS after reaching their optimized UTS. Driving forces for high UTS were: sufficient heat generation for plastic deformation, effective material coalescence, appropriate extrusion of molten material towards the trailing edge, adequate heat and mass transfer to control grain coarsening, void and flash formations. With an SN-ratio of 45.963 and low coefficient-of-variation of 1.11%, the conformity of the predicted and adjusted regression coefficients (R²) of 0.9619 and 0.9868 respectively supported by the confirmatory test and diagnostic plots showed a strong correlation between the experimental and predicted results. These demonstrated that the developed model was sufficient for predicting and optimizing the UTS of Friction Stir Weld (FSW) AA6061T651 plates.

Wall-shear-stress measurements using volumetric µPTV

Accurately determining the wall-shear-stress, tau _textrm{w}, experimentally is challenging due to small spatial scales and large velocity gradients present in the near-wall region of turbulent flows. To avoid these resolution requirements, several indirect iterative fitting methods, most notably the Clauser chart method, exist for determining overline{tau }_{w} by fitting the mean velocity profile further away from the near-wall region in the log-law layer. These methods often require proper selection of fitting constants, assumptions of a canonical flow state, and other empirical-based generalizations. To reduce the amount of ambiguity, determining the near-wall velocity gradient by assuming a linear relationship between the mean streamwise velocity and wall normal distance in the viscous sublayer can be used. However, this requires an accurate unbiased measurement of the near-wall velocity profile in the region below five viscous spatial units, which can be less than 50 µm for high Reynolds number flows. Therefore, in this study a method for a volumetric defocusing microparticle tracking velocimetry method is presented that is capable of resolving the flow in the viscous sublayer of a turbulent boundary layer up to U_{textrm{e}}=44.7,m/s (Re_{theta }=27250). This method allows for the measurement of the near-wall flow through a single optical access for illumination and imaging and serves as an excellent complement of larger scale measurements that require near-wall information. The overline{tau }_textrm{w} values determined from the defocusing approach were found to be in good agreement values obtained from a simultaneous parallax PTV measurement. Furthermore, analysis of the diagnostic plot and cumulative distribution of measured fluctuations in the near-wall region, showed that both methods are capable of accurately determining mean velocity and fluctuation profiles in the self-similar viscous sublayer region.

RTA-Assisted Numerical History-Matching Workflow

Summary Due to strong nonlinearities in the governing diffusivity equation for flow in porous media, numerically assisted rate-transient analysis (RTA) techniques have been suggested for the analysis of multiphase production data from multifractured horizontal wells (MFHWs). However, these methods are based on some limiting assumptions that cannot be generalized for three-phase flow or when relative permeability is unknown. In this study, a new RTA-assisted history-matching technique is proposed to simultaneously match production data and diagnostic plots during the calibration process. In the proposed method, the objective function is modified to include the derivative of the integral of rate-normalized pressure for the primary phases. As such, in the history-matching process using compositional numerical simulation, the flow regimes are also matched, which can increase the reliability of the calibrated numerical model. This approach is applied to a challenging data set of production data from an MFHW completed in a Canadian shale reservoir hosting a near-critical gas condensate fluid. The results demonstrate that when the modified objective function is used, the history-matching scheme will reject models that cannot reproduce the flow regimes even if the production data are visually matched. Another benefit of this modified history-matching workflow is that, unlike other numerically assisted RTA techniques, it is not limited to any specific conceptual model or reservoir geometry. Further, interactions between parameters are accounted for during the calibration process. Including the derivative terms in the objective function can ensure a better history-matched model with improved forecast quality. However, comparing the convergence rates of the history-matching with the standard and modified objective functions indicates that adding the derivative terms comes with an additional computational cost requiring more iterations and a slower convergence rate. In this study, a modified objective function is introduced for the first time to enhance the numerical history-matching process to ensure the resulting calibrated model can also reproduce the observed transient flow regimes. This approach is easy to implement and is not limited to a specific model geometry or any input-output relationship.

Machine learning‐based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

SummaryOver the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning‐based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)‐based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM‐based optical communication network of 50 km length is designed and simulated using Optisystem‐14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be −173,627.14, providing a robust and cost‐efficient process.

Generalized Unit Half-Logistic Geometric Distribution: Properties and Regression with Applications to Insurance

The use of distributions to model and quantify risk is essential in risk assessment and management. In this study, the generalized unit half-logistic geometric (GUHLG) distribution is developed to model bounded insurance data on the unit interval. The corresponding probability density function plots indicate that the related distribution can handle data that exhibit left-skewed, right-skewed, symmetric, reversed-J, and bathtub shapes. The hazard rate function also suggests that the distribution can be applied to analyze data with bathtubs, N-shapes, and increasing failure rates. Subsequently, the inferential aspects of the proposed model are investigated. In particular, Monte Carlo simulation exercises are carried out to examine the performance of the estimation method by using an algorithm to generate random observations from the quantile function. The results of the simulation suggest that the considered estimation method is efficient. The univariate application of the distribution and the multivariate application of the associated regression using risk survey data reveal that the model provides a better fit than the other existing distributions and regression models. Under the multivariate application, we estimate the parameters of the regression model using both maximum likelihood and Bayesian estimations. The estimates of the parameters for the two methods are very close. Diagnostic plots of the Bayesian method using the trace, ergodic, and autocorrelation plots reveal that the chains converge to a stationary distribution.