Time Prediction Model Research Articles

Effect of Time-strengthening Static Friction on Earthquake Recurrence

he effect of time-strengthening static friction on earthquake recurrence is numerically studied based on the normalized equation of motion of a one-body spring-slider model with thermal-pressurized slip-weakening friction having the characteristic distance, Uc, which is in terms of static friction coefficient, 𝜇. Considering the time-strengthening static friction coefficient, 𝜇(𝑡) = 𝜇+𝐵×𝑙𝑜𝑔(𝑡), we assume Uc=Uco/(1+𝐵×𝑙𝑜𝑔(𝑡)). Simulation results exhibit that TR, 𝜏D, D, and Vm, which represent the recurrence time of two events, the duration time of slip of an event, the final slip of an event, and the peak value of particle velocity of an event, respectively, are all similar for five values of Uco when B<0.01 and clearly different when B≥0.01. In general, Vm, D, and TR increase with B; and 𝜏D slightly decreases with increasing B and increases with Uco. An increase in D is particularly remarkable when Uco>0.3. The earthquake recurrence is almost periodic for small Uco; while the degree of periodicity decreases when either Uco or B increases. Either the time-predictable model or the slip-predictable model can approximately interpret the simulated earthquake recurrences with small Uco and small B, yet not for those with large Uco and large B. Simulation results exhibit that time-strengthening static friction produce an opposite effect on earthquake recurrence from the time-widening slip zone.

Melting Time Prediction Model for Induction Furnace Melting Using Specific Thermal Consumption from Material Charge Approach

A system-level evaluation was used to analyze the induction furnace operation and process system in this study. This paper presents an investigation into the relationship between the instantaneous chemical composition of a molten bath and its energy consumption in steelmaking. This was evaluated using numerical modelling to solve for the estimated melting time prediction for the induction furnace operation. This work provides an insight into the lowering of energy consumption and estimated production time in steelmaking using material charge balancing approach. Enthalpy computation was implemented to develop an energy consumption model for the molten metal using a specific charge composition approach. Computational simulation program engine (CastMELT) was also developed in Java programming language with a MySQL database server for seamless specific charge composition analysis and testing. The model performance was established using real-time production data from a cast iron-based foundry with a 1 and 2-ton induction furnace capacity and a medium carbon-based foundry with a 10- and 15-ton induction furnace capacity. Using parameter fitting techniques on the measured operational data of the induction furnaces at different periods of melting, the results from the model predictions and real-time melting showed good correlation between 81% - 95%. A further analysis that compared the relationship between the mass composition of a current molten bath and melting, time showed that energy consumption can be reduced with effective material balancing and controlled charge. Melting time was obtained as a function of the elemental charge composition of the molten bath in relation to the overall scrap material charge. This validates the approach taken by this research using material charge and thermodynamic of melting to optimize and better control melting operation in foundry and reduce traditional waste during iron and steel making.

From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening.

The technological advances of cutting-edge high-resolution mass spectrometry (HRMS) have set the stage for a new paradigm for exposure assessment. However, some adjustments of the metabolomics workflow are needed before HRMS-based methods can detect the low-abundant exogenous chemicals in human matrixes. It is also essential to provide tools to speed up marker identifications. Here, we first show that metabolomics software packages developed for automated optimization of XCMS parameters can lead to a false negative rate of up to 80% for chemicals spiked at low levels in blood. We then demonstrate that manual selection criteria in open-source (XCMS, MZmine2) and vendor software (MarkerView, Progenesis QI) allow to decrease the rate of false negative up to 4% (MZmine2). We next report an MS1 automatized suspect screening workflow that allows for a rapid preannotation of HRMS data sets. The novelty of this suspect screening workflow is to combine several predictors based on m/z, retention time (Rt) prediction models, and isotope ratio to generate intermediate and global scorings. Several Rt prediction models were tested and hierarchized (PredRet, Retip, retention time indices, and a log P model), and a nonlinear scoring was developed to account for Rt variations observed within individual runs. We then tested the efficiency of this suspect screening tool to detect spiked and nonspiked chemicals in human blood. Compared to other existing annotation tools, its main advantages include the use of Rt predictors using different models, its speed, and the use of efficient scoring algorithms to prioritize preannotated markers and reduce false positives.

Towards compound identification of synthetic opioids in nontargeted screening using machine learning techniques.

The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a promising avenue to putatively identify a compound before confirmation against a standard. In this study, machine learning approaches were used to develop class prediction and retention time prediction models. The developed class prediction model used a naïve Bayes architecture to classify opioids as belonging to either the fentanyl analogues, AH series or U series, with an accuracy of 89.5%. The model was most accurate for the fentanyl analogues, most likely due to their greater number in the training data. This classification model can provide guidance to an analyst when determining a suspected structure. A retention time prediction model was also trained for a wide array of synthetic opioids. This model utilised Gaussian process regression to predict the retention time of analytes based on multiple generated molecular features with 79.7% of the samples predicted within ±0.1min of their experimental retention time. Once the suspected structure of an unknown compound is determined, molecular features can be generated and input for the prediction model to compare with experimental retention time. The incorporation of machine learning prediction models into a compound identification workflow can assist putative identifications with greater confidence and ultimately save time and money in the purchase and/or production of superfluous certified reference materials.

Online Test System Load Balancing Method Based on Learning Analytics and Prediction Model

Aiming at the problem of load imbalance such as database instability caused by high concurrency in the online test system, an online test system load balancing method based on learning analysis and prediction model is proposed. The method is divided into two steps. The first step is to build a curve of the number of questions answered according to the user test data. Simulate the test load change at different time intervals, reduce the number of concurrent connections by reasonably arranging the test; the second step is to analyze the user’s usual learning data and extract multiple features and then construct a time prediction model through the convolutional neural network. When the user accesses the test system, we can test the time required for him to complete the exam based on the predictive model. Each type of user is evenly redirected to each server by length of time. The experimental results show that the number of concurrent connections in the database generated by this method is lower, and the load of each server can be effectively balanced. In addition, the method can predict the load situation in advance so that can provide a reference for purchasing server resources before the test.

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

Estimating horizontal movement performance of patient beds and the impact on emergency evacuation time

Emergency evacuation of patients from a hospital can be challenging in the event of a fire. Most emergency evacuation studies are based on the assumption that pedestrians are ambulant and can egress by themselves. However, this is often not the case during emergency evacuations in healthcare facilities such as hospitals and nursing homes. To investigate emergency evacuations in such healthcare facilities, we performed a series of controlled experiments to study the dynamics of patient beds in horizontal movement. We considered a patient bed because it is one of the commonly used devices to transport patients within healthcare facilities. Through a series of controlled experiments, we examined the change of velocity in corner turning movements and speed reductions in multiple trips between both ends of a straight corridor. Based on the experimental results, we then developed a mathematical model of total evacuation time prediction for a patient bed horizontally moving in a healthcare facility. Factoring uncertainty in the horizontal movement, we produced the probability distribution of movement duration and estimated the probability that an evacuation can be safely performed within certain amount of time. In addition, we predicted that the evacuation time would be longer than the prediction results from an existing model which assumes constant movement speed. Our results from the model demonstrated good agreement with our experimental results.

OKCM: improving parallel task scheduling in high-performance computing systems using online learning

Task scheduling is becoming increasingly important in large-scale high-performance computing real-time systems as the parallel scale, number and types of task continue to increase. The prioritizing policies and backfilling mechanisms are the most useful practice for improving scheduling performance. In particular, these methods highly depend on the task running time prediction. Previous studies focused on improving the running time prediction accuracy, resulting in higher time overhead and deployment difficulties in real-time scheduling system. In this paper, an efficient running time prediction model, referred to as online learning and K-nearest neighbors (KNN)-based predictor with correction mechanism (OKCM), is proposed. OKCM updates in real time through online algorithm and is friendly to users with a small data accumulation by KNN-based predictor. To evaluate our model, a trace-driven simulator, named HPCsim, is designed and implemented. The experimental results demonstrated that OKCM can achieve higher prediction accuracy with a low overhead. Furthermore, OKCM can achieve significant scheduling performance improvement and can be used to enhance primary prioritizing and backfilling methods without being restricted by specific scheduling method.

Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (tR) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R2) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure–ion intensity relationship (QSIIR) model.

Development of a Prediction Model for Trauma Registry Chart Abstraction Time.

A Level I trauma center routinely faced challenges with meeting data submission deadlines and frequently struggled with a backlog of cases that limited opportunities for concurrent performance improvement. To provide a validated algorithm through which registry workload could be evaluated, the study institution designed a scientific model that predicted the amount of time required for chart abstraction on a patient-by-patient basis. As part of this quality improvement endeavor, registrars documented the amount of time required to complete each chart. A total of 600 patients' data were included by randomly selecting 150 patients from each of the 4 trauma registrars. Given that no previous study has examined the association of patient-related factors with chart abstraction time, study variables utilized to construct this predictive model were determined by the trauma program manager and the lead trauma registrar. Multiple linear regression demonstrated that inhospital mortality; transfer from a referring facility; hospital stay; ventilator days; and number of complications, specialty consults, injuries, blood products, and procedures were significant predictors of chart abstraction time. The equation for the regression line for the multivariate regression was as follows: Y = 38.95 + 31.28 × mortality + 15.33 × referring facility + 4.68 × complications+3.55 × hospital stay + 3.33 × consults + 2.83 × diagnoses + 2.00 × ventilator days + 1.78 × blood products + 1.09 × procedures. The merit of this prediction model is that it is based on patient-related variables and predicts time on a patient-by-patient basis. This innovative tool can be utilized by other trauma centers to evaluate registry productivity and identify opportunities for improvement retrospectively.

Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.

Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (“stats” module), generating informative plots (“plotting” module), identifying lipid species at different confidence levels (“identification” module), and carrying out all functions using a user-friendly text-based interface (“interactive” module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.

A simple model for breakup time prediction of water-heavy fuel oil emulsion droplets

Immiscible heavy fuel-water (W/HFO) emulsion droplets inside combustion chambers are subjected to explosive boiling and fragmentation due to the different boiling point between the water and the surrounding host fuel. These processes, termed as either puffing or micro-explosion, are investigated with the aid of a CFD model that solves the Navier-Stokes and energy conservation equations alongside with three sets of VoF transport equations resolving the formed interfaces. The model is applied in 2-D axisymmetric configuration and it is valid up to the time instant of HFO droplet initiation of disintegration, referred to as breakup time. Model predictions are obtained for a wide range of pressure, temperature, water droplet surface depth and Weber number; these are then used to calibrate the parameters of a fitting model estimating the initiation breakup time of the W/HFO droplet emulsion with a single embedded water droplet. The model assumes that the breakup time can be split in two distinct temporal stages. The first one is defined by the time needed for the embedded water droplet to heat up and reach a predefined superheat temperature and a vapor bubble to form; while the succeeding stage accounts for the time period of vapor bubble growth, leading eventually to emulsion droplet break up. It is found that the fitting parameters are ±10% accurate in the examined range of We < 220, T < 2000 K, P < 140 bar and δ < 0.15.

Investigation on the Corrosion Behavior of Steel Embedded in Basic Magnesium Sulfate Cement Concrete: An Attempt and Challenges.

Basic magnesium sulfate cement is a type of green high-performance cementitious material. In order to exert its performance advantages and expand its application field, it is urgent to study the problem of steel corrosion in basic magnesium sulfate cement concrete (BMSCC). In this paper, linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) were used to study the corrosion behavior of steel bars in different strength grades of BMSCC in seawater. Based on the relationship between the corrosion current density and the immersion time, the corresponding time-varying model was obtained. The LPR and EIS results show that the corrosion potential and polarization resistance of steel bars in BMSCC decreased with the immersion time in the seawater environment. The fitting analysis indicated concordance between the corrosion rates with the logarithmic function time-varying model. Furthermore, the cracking time of the protective layer of BMSCC was analyzed based on the cracking time prediction model of Portland cement concrete and the mechanical properties of BMSCC.

Chloride threshold value and initial corrosion time of steel bars in concrete exposed to saline soil environments

Saline soil is widely distributed in Western China. Its main components have the characteristics of high concentration and many kinds of ions, which pose a serious threat to the service life of reinforced concrete in this area. Therefore, it is of practical significance to determine the initial corrosion time and the chloride threshold value (CTV) of steel bars. In this research, the electrochemical impedance spectroscopy (EIS) method was used to determine the initial corrosion time and the CTV of steel bars immersed in saline soil simulation solutions, and the influence of sulfate and other ions on the initial corrosion time and the CTV of the steel bars was analyzed, as well as the influence mechanism. Based on Fick's second diffusion law, considering the time-dependencies of the chloride diffusion coefficient and the surface chloride content, a prediction model for the initial corrosion time of a steel bar in concrete was established. The results showed that compared with the solution containing only chloride and the solution containing low concentration of sulfate, the initial corrosion time of steel bars in saline soil simulation solution was significantly longer, and the CTV was lower. The sulfate had an obvious inhibited effect on the diffusion of chloride, the corrosion products of sulfate could fill the pores of concrete, affect the microstructure of the transition zone and concrete resistance, and caused the initial corrosion time of the steel bars to increase. other ions in the simulation solution also inhibited the diffusion of chloride, HCO3− ion reduced the pH value of concrete pore solution and made the CTV of steel bars decrease. The prediction model established in this research was in good agreement with the actual situation, which can provide experimental evidence for the service life prediction of reinforced concrete structures in a saline soil environment. In order to prolong the initial corrosion time of the reinforced concrete in a saline soil simulation solution, in addition to increasing the thickness of the concrete protective layer, other anti-corrosion measures were also needed.

Mathematical Modeling of Reversible Bimolecular Catalyzed Transesterification Reaction Kinetics for PKO and Methanol synthesis

Continuous appraisal for alternative renewable energy sources particularly for bio-diesel production is a great concern to engineers and scientist globally. Actually, vegetable oils and C1 to C3 alcohol group alkali catalyzed transesterification reaction leads to bio-diesel production. The aspect of developing reaction time and disappearance rate (-RA) mathematical models for effective various reactor types design seems impassive. Hence, in this work the authors developed a unified reaction time and disappearance rate (-RA) predictive models as a function of reactants and product reaction rate constants (K1 K2), molar masses (MW), conversion (XA), fractional volume change (ɛ) and density (ρ) dimensions using partial fraction and by parts integration methods. The developed models were simulated with Matlab codes programming approach exploiting the kinetics parameters of PKO , Methanol and biodiesel. The results of the reaction time (t) at equilibrium, none equilibrium and disappearance rate (-RɛA) show dependable relationship with the stipulated dimensions and were quite compatible with those of inferential laboratory physicochemical data reported. Thus;

The structural integrity of water pipelines by considering the different loads

The objective of the study was to analyse the structural integrity of water pipelines. The analysis shows how large the discrepancies in structural integrity of water pipelines can be, depending on the method used to calculate loads acting on the pipes. Factors affecting changes in the size of loads of water pipelines were introduced based on the analysis of causes of several failures of water pipelines analysed by the authors and several field measurements performed by them.The article discusses the impact of different loads on strength changes in water pipelines. The two methods used in the case study differ in the assumptions concerning the determination of the size of loads acting on the pipes.The results of the case study indicate to what extent different loads influence the amount of stress in a pipeline. The discussion indicates that it is advisable to perform detailed studies of soil parameters around the pipes because they have a very significant impact on the dead loads and the selection of correct live loads. Depending on the assumptions made, the total stresses in the pipeline can vary up to several times. Therefore it is justified to consider, in failure models and life time prediction models of long operated water pipelines, the actual loads acting on the pipes taking into account changes of their quantity that occurred during their exploitation to-date.

A Novel Gene Signature to Predict Survival Time and Incident Ventricular Arrhythmias in Patients with Dilated Cardiomyopathy.

The mortality in nonischaemic dilated cardiomyopathy (NIDCM) patients is still at a high level; sudden death in NIDCM can be caused by ventricular tachycardia. It is necessary to explore the pathogenesis of ventricular arrhythmias (VA) in NIDCM. Differentially expressed genes (DEGs) were identified by comparing the gene expression of NIDCM patients with or without VA in the gene expression profile of GSE135055. A total of 228 DEGs were obtained, and 3 genes were screened out to be significantly related to the survival time of NIDCM patients. We established a prediction model on two-gene (TOMM22, PPP2R5A) signature for the survival time of NIDCM patients. The area under the curve (AUC) was 0.75 calculated by the ROC curve analysis. These risk genes are probably new targets for exploring the pathogenesis of NIDCM with VA; the prediction model for survival time and incident ventricular arrhythmias is useful in clinical decision making for individual treatment.

Research on evaporation duct height prediction based on back propagation neural network

Evaporation duct is an abnormal refraction atmospheric stratification frequently appearing at sea. Accurately obtaining evaporation duct height (EDH) is significant for the effective application of electromagnetic system equipment. To overcome the limitations of theoretical prediction models, this study proposes a pure data-driven back propagation neural network (BPNN) EDH prediction model (referred to as BPNN model) for the first time, which uses BPNN suitable for the data characteristics of evaporation duct and then conducts two groups of experiments to fully test the proposed BPNN model. The Paulus–Jeske (PJ) model and support vector regression (SVR) model are introduced as the baseline methods in these experiments. The results of the first group of experiments prove that on the EDH prediction in all the experimental areas, the BPNN model has significantly better comprehensive performances than the PJ model and the SVR model. Also, the second group of experiments’ results reflects the BPNN model has great generalisation capacity, which makes it suitable for predicting the EDH, and the distributions of EDH have a strong spatial correlation.

An efficient model for predicting setting time of cement based on broad learning system

Cement is the main building material in the construction industry. Its setting time directly affects the setting time and strength of concrete, which further affects construction schedule and building quality. However, traditional measurement technology not only has high labor intensity and high time consumption, but has a high technical requirement. Various human factors, such as insufficient operation, will result in great errors in the measurements. The accurate prediction of setting time enables manpower savings, avoids large errors caused by insufficient operation, and guides the production of high-performance cement. In this paper, an efficient model based on the broad learning system is proposed to predict the initial and final setting time. It is committed to directly predicting setting time from clinker composition and physical properties, which is of great significance to the optimization of clinker formula. The experimental results show that it can accurately predict the setting time and behave good generalization ability, which addresses the problem of labor intensity in measurement and saves many resources. In addition, the broad learning system can rapidly build a setting time prediction model with few errors, satisfying industrial demands for the rapid modeling of various specialty cements.