Performance Of Various Combinations Research Articles

Nerve conduction features may serve as a diagnostic clue for neuronal intranuclear inclusion disease.

Neuronal intranuclear inclusion disease is a neurodegenerative disorder with a wide phenotypic spectrum, including peripheral neuropathy. This study aims to characterize the nerve conduction features and proposes an electrophysiological criterion to assist the diagnosis of neuronal intranuclear inclusion disease. In this study, nerve conduction studies were performed in 50 genetically confirmed neuronal intranuclear inclusion disease patients, 200 age- and sex-matched healthy controls and 40 patients with genetically unsolved leukoencephalopathy. Abnormal electrophysiological parameters were defined as mean values plus or minus two standardized deviations of the healthy controls or failure to evoke a response on the examined nerves. Compared to controls, neuronal intranuclear inclusion disease patients had significantly slower motor and sensory nerve conduction velocities, as well as lower amplitudes of compound motor action potentials and sensory nerve action potentials in all tested nerves (P < 0.05). Forty-eight of the 50 neuronal intranuclear inclusion disease patients (96%) had at least one abnormal electrophysiological parameter, with slowing of motor nerve conduction velocities being the most prevalent characteristic. The motor nerve conduction velocities of median, ulnar, peroneal and tibial nerves were 44.2 ± 5.5, 45.3 ± 6.1, 37.3 ± 5.3 and 35.6 ± 5.1 m/s, respectively, which were 12.4-13.6 m/s slower than those of the controls. The electrophysiological features were similar between neuronal intranuclear inclusion disease patients manifesting with CNS symptoms and those with PNS-predominant presentations. Thirteen of the 14 patients (93%) who underwent nerve conduction study within the first year of symptom onset exhibited abnormal findings, indicating that clinical or subclinical peripheral neuropathy is an early disease marker of neuronal intranuclear inclusion disease. We then assessed the feasibility of using motor nerve conduction velocity as a diagnostic tool of neuronal intranuclear inclusion disease and evaluated the diagnostic performance of various combinations of nerve conduction parameters using receiver operating characteristic curve analysis. The criterion of having at least two nerves with motor nerve conduction velocity ranging from 35 to 50 m/s in median/ulnar nerves and 30-40 m/s in tibial/peroneal nerves demonstrated high sensitivity (90%) and specificity (99%), with an area under the curve of 0.95, to distinguish neuronal intranuclear inclusion disease patients from healthy controls. The criterion's diagnostic performance was validated on an independent cohort of 56 literature reported neuronal intranuclear inclusion disease cases (area under the curve = 0.93, sensitivity = 87.5%, specificity = 99.0%), and in distinguishing neuronal intranuclear inclusion disease from genetically unresolved leukoencephalopathy cases (sensitivity = 90.0%, specificity = 80.0%). In conclusion, mildly to moderately decreased motor nerve conduction velocity in multiple nerves is a significant electrophysiological hallmark assisting the diagnosis of neuronal intranuclear inclusion disease, regardless of CNS- or PNS-predominant manifestations.

A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy

The content of soil organic carbon (SOC) plays an important role in maintaining ecosystem functions, protecting soil biodiversity, and understanding carbon cycling processes. The combination of visible and near-infrared spectroscopy (VIS–NIRS) and machine learning can achieve rapid prediction of SOC content. However, it is still relatively unknown how to integrate the characteristics of various machine learning models to improve the performance of SOC prediction models. In this study, a new model for predicting SOC content based on stacking ensemble learning was proposed by using VIS–NIRS. The prediction performances of six different models including Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), PartialLeast-square (PLS) and Extreme Learning Machine (ELM) on SOC content under different spectral preprocessing methods were compared. The results indicated that SVR, XGBoost, and LightGBM models provide better prediction performance after first-order derivative preprocessing. After comparing the performance of various combinations of base models applied to the first layer of a stacking ensemble model, the results showed that both the combination of XGBoost, LightGBM, and SVR models and the combination of SVR, ELM, and LightGBM models achieve the best performance. The coefficient of determination (R2) of the stacking ensemble model on the test set reaches 0.84, which improves the accuracy of the model compared with the traditional single model. The stability of the stacking ensemble model was verified by applying it to datasets of different sizes, which can replace traditional machine learning models in predicting SOC content.

Estimating Organic Matter Content in Hyperspectral Wetland Soil Using Marine-Predators-Algorithm-Based Random Forest and Multiple Differential Transformations

To achieve a rapid and accurate estimation of the soil organic matter (SOM) content in wetland soil, we focused on surface soil samples from the Xianshan Lake wetland area in Zhejiang Province and proposed a novel method called Marine-Predators-Algorithm-Based Random Forest (MPARF) to establish a fast detection model for the SOM content. This study analyzed 85 soil samples from the study area with the aim of assessing the performance of various combinations of ten differential transformation methods and five regression algorithms in predicting the SOM content. Our research findings demonstrate that the combination of second-order differentiation (SD) and MPARF yields the best results, with the highest R2 value (0.92) and the lowest RMSE (1.32 g/kg). Furthermore, we determined that the average SOM content in the study area’s soil is 9.77 g/kg. Additionally, we confirmed that different differential transformation methods contribute to improving the correlation between spectral data and the SOM content, thereby enhancing the development of predictive models. This study provides a robust methodology and foundation for future soil organic matter monitoring in the region.

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

ELECTRODERMAL ACTIVITY-BASED ANALYSIS OF EMOTION RECOGNITION USING TEMPORAL-MORPHOLOGICAL FEATURES AND MACHINE LEARNING ALGORITHMS

In this study, we evaluated the performance of tonic and phasic components of Electrodermal activity (EDA) using machine learning algorithms for accurately recognizing emotions. The EDA signals considered for this study were obtained from Continuously Annotated Signals of Emotion (CASE) dataset. Initially, we pre-processed and decomposed the EDA into tonic and phasic components using cvxEDA method. Further, we extracted the temporal and morphological features from both tonic and phasic. Finally, we tested the performance of various combinations of features using machine learning algorithms such as logistic regression, support vector machine (SVM), and random forest. Our results revealed that the tonic contributes significant information for emotional state classification. Further, the temporal features of the phasic were able to discriminate most of the emotions [Formula: see text]. In particular, the scary emotion was well discriminated against other emotions. Results of classification revealed that SVM performed best in classifying emotional states. The results of our process pipeline, which incorporated tonic, temporal features, and SVM, showed impressive classification performance with average accuracy, sensitivity, specificity, precision, and f1-score of 78.96%, 57.92%, 85.97%, 62.32%, and 56.48%, respectively. Our findings indicate that our proposed models could potentially be used to detect the positive and negative emotions in healthcare settings.

Small‐scale assessment method for the fire resistance of historic plaster system and timber structures

AbstractThe primary protection against the charring of timber is ensured by protection materials. Today, there are only a limited number of materials given in design codes as fire protection materials for timber. Historic surface finish materials such as plasters have rarely been studied with respect to fire; no design values exist in the current fire part of Eurocode 5. Full‐scale fire testing is costly to assess the fire performance of material combinations, thus this study presents a useful tool that is specifically tailored to evaluate the fire protection ability of materials in small‐scale. A review of conducted tests demonstrate that the cone heater of a cone calorimeter is a dependable device to estimate the charring performance of protected timber specimens as the test results approximate the ones obtained from furnace tests. This work contributes to the assessment of fire resistance performance of various combinations and types of plaster systems found in existing timber buildings that often require an individual approach for an adequate fire risk analysis and design decisions to meet current fire safety regulations with respect to the load‐bearing capacity and compartmentation of building structures. Increased knowledge on the fire protection performance of traditional plasters is believed to facilitate their wider use in timber buildings, primarily to preserve their significance as part of the cultural built heritage.

Nutritional Performance of Juvenile Red Drum (Sciaenops ocellatus) Fed Various Fish, Shrimp, and Squid Diets

Aquaculture is the fastest-growing sector of protein production in the world. Due to the rising costs of fishmeal used as a critical ingredient to make pelleted fish feed, the industry is moving to replace fishmeal as a primary protein source without reducing the growth rate of aquacultured fish. A 12-week feeding trial utilizing juvenile red drum (Sciaenops ocellatus) was conducted to determine the performance of various combinations of natural diet components including fish (Decapterus punctatus), shrimp (Litopenaeus vannamei), and squid (Loligo opalescens and Illex), in addition to a commercial fishmeal-based pelleted feed, fed isocalorically to identify if there is an optimal combination for red drum growth and health. These results can provide information to develop fishmeal replacement diets (FMRDs) that can more closely mimic the performance of natural diets. Traditional aquaculture metrics showed that fish fed the diet comprised only of fish had the highest specific growth rate, condition factor, and protein conversion efficiency, with the lowest feed conversion ratio, indicating the fish component was the highest performing component for red drum growth on a calorically fixed ration. There were significant differences among eight groups found for traditional aquaculture metrics ( p &lt; 0.05 ). The commercial pelleted feed performed better than all but the fish only natural diet treatments in terms of growth on a fixed ration, which indicated that it is nutritionally balanced. The results of this study show that there were performance differences between juvenile red drum fed various natural diets. There has been an investigation into the metabolome of these fish to identify potential metabolites for supplementation into FMRDs, which is not addressed in this paper.

SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data.

Simple SummaryRobust methods for modelling and estimation of cancer survival could be relevant in understanding and limiting the impact of cancer. This study was aimed at developing an efficient Machine learning (ML) pipeline that could model survival in Lung Adenocarcinoma (LUAD) patients. Image transformations of multi omics data were employed for training a machine vision-based model capable of segregating patients into high-risk and low-risk subgroups. The performance was evaluated using concordance index, Brier score, and other similar metrices. The proposed model was able to outperform similar methods with a high degree of confidence. Furthermore, critical modules in cell cycle and pathways were also identified.The utility of multi-omics in personalized therapy and cancer survival analysis has been debated and demonstrated extensively in the recent past. Most of the current methods still suffer from data constraints such as high-dimensionality, unexplained interdependence, and subpar integration methods. Here, we propose SurvCNN, an alternative approach to process multi-omics data with robust computer vision architectures, to predict cancer prognosis for Lung Adenocarcinoma patients. Numerical multi-omics data were transformed into their image representations and fed into a Convolutional Neural network with a discrete-time model to predict survival probabilities. The framework also dichotomized patients into risk subgroups based on their survival probabilities over time. SurvCNN was evaluated on multiple performance metrics and outperformed existing methods with a high degree of confidence. Moreover, comprehensive insights into the relative performance of various combinations of omics datasets were probed. Critical biological processes, pathways and cell types identified from downstream processing of differentially expressed genes suggested that the framework could elucidate elements detrimental to a patient’s survival. Such integrative models with high predictive power would have a significant impact and utility in precision oncology.

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness.

Exploring the advantages of photo-voltaic triple skin façade in hot summer conditions

A new skin of perforated metallic sheet is introduced in the cavity of a photo-voltaic double skin façade (PV-DSF) system for an efficient heat removal from the cavity without losing substantial light into the built space. Quantitative experimental analysis is conducted to evaluate its thermal and electrical performance for various combinations of ventilation modes, air cavities, and perforation ratios. The advantages of Photo-Voltaic Triple Skin Facade system (PV-TSF) over commonly used PV-DSF are also evaluated experimentally. The results showed that naturally ventilated PV-TSF provides lower temperatures and Solar Heat Gain Coefficient (SHGC) with slightly higher power when compared to no ventilation mode in PV-DSF system. The SHGC of the PV-DSF system can be reduced by nearly 4.7% with variation in air cavity (Thickness 50–200 mm); after which no noticeable improvement is observed. In the case of PV-TSF system, SHGC can be reduced by almost 14.7% compared to the PV-DSF system without significant loss of natural daylight into built space. Experimental results also showed that the reduction in perforation ratio from 60% to 20% reduced the SHGC by 3.8% with an average illuminance of 334 lux (minimum perforation, 20%). It was also observed that the addition of a perforated sheet can offset the large air cavity of a PV-DSF system. Therefore, the benefit of the PV-TSF system is profound as compared to PV-DSF.

On the Relation between Empirical Amplification and Proxies Measured at Swiss and Japanese Stations: Systematic Regression Analysis and Neural Network Prediction of Amplification

ABSTRACTWe address the relation between local amplification and site-condition indicators derived from in situ geophysical surveys for the estimation of the VS profile, and single-station recordings processed with horizontal-to-vertical spectral ratio technique. Site-condition indicators, or proxies (e.g., VS30), aim at “summarizing” the description of the local geophysical structure, with a focus on its relation to site amplification.The premise for our work was the compilation of two companion databases: one of soil condition proxies and the other of empirically derived Fourier amplification functions, for Swiss and Japanese stations.We investigated the connection between these two datasets, at first, with a systematic set of regressions correlating each proxy to amplification factors within the frequency band 0.5–20 Hz, second, with a neural network (NN) structure predicting site amplification from proxies.The regression analyses showed that, generally, site-condition parameters (SCPs) bear a better correlation with amplification within 1.7–6.7 Hz; the “best” indicators are the frequency-dependent quarter-wavelength (QWL) velocity and, among scalar parameters, VS30, the bedrock depth, and f0. Collating Swiss and Japanese datasets, the trend of variation of amplification with respect to most proxies is similar. Finally, we evaluated the prediction performance of various combinations of SCPs, for local amplification, using a NN. To attain a database large enough to constrain the estimation of the network parameters, we merged Swiss and Japanese stations into a single training and validation dataset, motivated by the similarities observed in the regression analyses. The outcome we obtained from the NN is encouraging and consistent with the results of the regressions; SCPs with higher correlation to amplification provide a better forecast of the latter (particularly within 1.7–6.7 Hz). More complete input information, such as QWL parameters (velocity, impedance contrast), or extended ensembles of scalar proxies (particularly, including f0), offer a better estimation of local amplification.

Update on Performance Analysis of Different Computational Architectures: Molecular Dynamics in Application to Protein-Protein Interactions

Molecular dynamics has proved itself as a powerful computer simulation method to study dynamics, conformational changes, and interactions of biological macromolecules and their complexes. In order to achieve the best performance and efficiency, it is crucial to benchmark various hardware platforms for the simulations of realistic biomolecular systems with different size and timescale. Here, we compare performance and scalability of a number of commercially available computing architectures using all-atom and coarse-grained molecular dynamics simulations of water and the Ndc80-microtubule protein complex in the GROMACS-2019.4 package. We report typical single-node performance of various combinations of modern CPUs and GPUs, as well as multiple-node performance of the “Lomonosov-2” supercomputer. These data can be used as the practical guidelines for choosing optimal hardware for molecular dynamics simulations.

Simulation-based assessment of model selection criteria during the application of benchmark dose method to quantal response data

BackgroundTo employ the benchmark dose (BMD) method in toxicological risk assessment, it is critical to understand how the BMD lower bound for reference dose calculation is selected following statistical fitting procedures of multiple mathematical models. The purpose of this study was to compare the performances of various combinations of model exclusion and selection criteria for quantal response data.MethodsSimulation-based evaluation of model exclusion and selection processes was conducted by comparing validity, reliability, and other model performance parameters. Three different empirical datasets for different chemical substances were analyzed for the assessment, each having different characteristics of the dose-response pattern (i.e. datasets with rich information in high or low response rates, or approximately linear dose-response patterns).ResultsThe best performing criteria of model exclusion and selection were different across the different datasets. Model averaging over the three models with the lowest three AIC (Akaike information criteria) values (MA-3) did not produce the worst performance, and MA-3 without model exclusion produced the best results among the model averaging. Model exclusion including the use of the Kolmogorov-Smirnov test in advance of model selection did not necessarily improve the validity and reliability of the models.ConclusionsIf a uniform methodological suggestion for the guideline is required to choose the best performing model for exclusion and selection, our results indicate that using MA-3 is the recommended option whenever applicable.

On Weighted Log-Rank Combination Tests and Companion Cox Model Estimators

In randomized clinical trials, the log-rank test and Cox proportional hazards model are the gold standard in survival data analyses. While the log-rank test is generally valid, in the presence of non-proportional hazards, the power can be substantially decreased relative to the proportional hazards assumptions under which studies are usually designed. In contrast, weighted log-rank tests can be more powerful for specific treatment differences under non-proportional hazards scenarios. However, a poor choice of the weighting form can be detrimental. Recent work on combining various weighted log-rank tests allows for tests that are capable of detecting treatment effects across a broad range of non-proportional hazards scenarios. In this paper, we expand on these ideas with a framework based on a flexible resampling approach [5] which allows for the combination of various testing procedures in addition to weighted log-rank tests. In particular, we describe how tests based on restricted mean survival time (RMST) comparisons can be included within combinations of weighted log-rank tests as well as other test statistics such as Tarone-Ware and Renyi-type supremum families. For estimation, we propose companion weighted Cox model estimators [14, 21, 22] which utilize the weighting form that is “selected” through the combination test and provide simultaneous confidence intervals. The performance of various combinations and their companion Cox estimators as well as RMST are evaluated in simulation studies under null, proportional hazards, late-separation, and early-separation scenarios. We find the combination tests perform quite well in controlling type-1 error rates and in achieving higher power than individual tests across the scenarios considered here. We suggest the companion Cox estimators are a natural link to the testing procedures and can be a useful complementary summary of treatment effects with careful interpretation. For illustration, we apply the proposals to a randomized clinical trial study of the PD-L1-targeted therapy atezolizumab in comparison with docetaxel in previously treated non-small-cell lung cancer patients. R code can be found at https://github.com/larryleon/combination-tests-and-estimators.

Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.

Evaluation of the effects of a multiphysics ensemble on the simulation of an extremely hot summer in 2003 over the CORDEX‐EA‐II region

In this paper, we evaluate a 48‐member multiphysics ensemble using the Weather Research and Forecasting (WRF) model for the JJA extreme precipitation and temperature in 2003 over the CORDEX‐EA‐II domain. The simulated precipitation and temperature are reasonable in the subregions controlled by a large‐scale circulation, yet the biases for both precipitation and temperature are evident over the subregions where the effects of mesoscale processes are important. The performance of various combinations of WRF physical schemes for simulating the JJA precipitation is dependent on the region. Meanwhile, the cumulus and microphysical schemes have substantial influences on the simulation of precipitation, and the land surface models and cumulus schemes play crucial roles in the surface temperature. Our analysis shows that the combination of Noah for the land surface process, Lin for the microphysics, G3D for the cumulus parameterization, and CAM for the radiation scheme can provide the most reliable reproduction of both precipitation and temperature extremes over China.Ensemble analysis shows that the simulated climate extremes are usually accompanied by a large ensemble spread, implying sensitivities to the model physical processes in some subregions. The simulated wind fields at low‐to‐middle atmospheric levels display responses to the options of the land surface models and cumulus schemes. The essential impact of the land–atmospheric interaction on simulating the extremes can be largely attributed to the active convective processes. WRF has difficulties in reproducing the observed temporal evolution of the rainfall process, which consists of continuous large rainfall episodes in the observation data set during the simulation period.

Performance Analysis of Different Computational Architectures: Molecular Dynamics in Application to Protein Assemblies, Illustrated by Microtubule and Electron Transfer Proteins

All-atom molecular dynamics simulation represents a computationally challenging, but powerful approach for studying conformational changes and interactions of biomolecules and their assemblies of different kinds. Usually, the numbers of simulated particles in modern molecular dynamics studies range from thousands to tens of millions, while the simulated timescales span from nanoseconds to microseconds. For cost and computation efficiency, it is important to determine the optimal computer hardware for simulations of biomolecular systems of different size and timescale. Here we compare performance and scalability of 17 commercially available computational architectures, using molecular dynamics simulations of water and two different protein systems in GROMACS-5 package as computing benchmarks. We report typical single-node performance of various combinations of modern CPUs and GPUs, as well as multiple-node performance of Lomonosov-2 supercomputer in molecular dynamics simulations of different protein systems in nanoseconds per day. These data can be used as practical guidelines for selection of optimal computer hardware for various molecular dynamics simulation tasks.

Spray injection patching for pothole repair using 100 percent reclaimed asphalt pavement

Pothole repair of asphalt pavements is one of the most commonly performed roadway maintenance operations, and it is costly. Reclaimed asphalt pavement (RAP) has become an increasingly attractive material for pothole repair due to its economic and environmental advantages. This study investigated the performance of spray injection patching materials for pothole repair that utilize 100 percent RAP and compared the results to the performance of patching materials made with virgin aggregate. This study was designed to investigate how well different patching products resist critical types of distresses that are observed in the field and typically require patching. The primary characteristics examined in the study include stability, adhesion, moisture susceptibility, and durability of the patching material. The performance of various combinations of materials was observed in laboratory tests to determine the most cost-effective and best-performing patch material. The results show that the material’s performance depended on the type of aggregate and emulsion used in the mixtures. The results also indicate that the mixtures that contained RAP had good stability and adhesion properties compared to the mixtures made with virgin aggregate. This research proved the feasibility and advantages of using RAP for spray injection pothole repair.

A comparison of methods for denoising of well test pressure data

Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS–ARMA to real field data shows promising results.

Region-based prescriptive route guidance for travelers of multiple classes

The performance and complicated interactions of different classes of travelers on regional urban networks are presented and analyzed. A new multi-class extension of a regional dynamic traffic model, the Network Transmission Model is proposed. The classes in question correspond to travelers using autonomous vehicles, conventional vehicles, equipped with Route Guidance and Information Systems, and unequipped vehicles. Each class is represented by a different routing method. Incremental Route Planning, an innovative predictive simulation-based routing method, Proxy Regret Matching, a non-predictive strategic learning-based method and Multinomial Logit-based Routing for 1st, 2nd and 3rd class respectively. All routing methods include a Public Transit Diversion mechanism and are assumed to provide prescriptive route guidance, with pre-trip information dissemination for every departing vehicle. We consider the possibility of non-compliance for conventional vehicles equipped with Route Guidance and Information Systems. We also consider 2 possible scenarios for autonomous vehicles that affect their travel time prediction accuracy. We simulate regional traffic dynamics for simultaneous application of all aforementioned routing methods, employing a market penetration scheme for each class of travelers. We analyze results regarding the overall network performance for various combinations of traveler class market penetration rates and non-compliance rates. We come to the conclusion that autonomous vehicles will not only provide benefits for 1st class travelers, but for all traveler classes on the network.