Error Model Research Articles

Pooled Population Pharmacokinetic Analysis and Dose Recommendations for Ciprofloxacin in Intensive Care Unit Patients with Obesity.

Recent studies have explored the influence of obesity and critical illness on ciprofloxacin pharmacokinetics. However, variation across the subpopulation of individuals with obesity admitted to the intensive care unit (ICU) with varying renal function remains unexamined. This study aims to characterize ciprofloxacin pharmacokinetics in ICU patients with obesity and provide dose recommendations for this special population. Individual patient data of 34 ICU patients with obesity (BMI >30 kg/m2) from four studies evaluating ciprofloxacin pharmacokinetics in ICU patients were pooled and combined with data from a study involving 10 individuals with obesity undergoing bariatric surgery. All samples were collected after intravenous administration. Non-linear mixed effects modeling and simulation were used to develop a population pharmacokinetic model and describe ciprofloxacin exposure in plasma. Model-based dose evaluations were performed using a pharmacokinetic/pharmacodynamic target of AUC/MIC >125. The data from patients with BMI ranging from 30.2 to 58.1 were best described by a two-compartment model with first-order elimination and a proportional error model. The inclusion of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) as a covariate on clearance reduced inter-individual variability from 57.3% to 38.5% (P <.001). Neither body weight nor ICU admission significantly influenced clearance or volume of distribution. Renal function is a viable predictor for ciprofloxacin clearance in ICU patients with obesity, while critical illness and body weight do not significantly alter clearance. As such, body weight and critical illness do not need to be accounted for when dosing ciprofloxacin in ICU patients with obesity. Individuals with CKD-EPI >60 mL/min/1.73 m2 may require higher dosages for the treatment of pathogens with minimal inhibitory concentration ≥0.25 mg/L.

How planning intervention changes the “price tags” of property rights? Towards a dynamic understanding of China's small property right housing

While studies of property rights have long gone beyond the legal-illegal dichotomy, how to quantify the varying degree of property rights remains a challenging task. Focusing on a special type of informal housing—Small Property Right Housing (SPRH) in China, this study examines how planning intervention affects the pricing mechanism of SPRH through the theoretical lens of graded property rights. A previous study has unravelled the pricing mechanism of SPRH by quantifying the impact of informal institutions inherent to the SPRH market that give different “price tags” to varying degree of property rights, while not being able to capture the impact of external interventions. Drawing on two SPRH datasets before and after a Master Plan of urban villages conservation announced in 2019, we employ difference in difference (DID) estimations to examine how planning intervention affects SPRH prices by way of altering the strength of informal property rights. We also develop spatial error models (SEM) to account for spatial autocorrelation. This research extends and enriches the robust conceptualisation of graded degree of informal property rights by capturing the changes caused by external intervention to offer a dynamic and more accurate understanding of SPRH pricing mechanism. It also demonstrates that planning can go beyond sanctioning and redlining to tolerate and steer informal development.

Landing cushioning analysis of new generation heavy airborne armored vehicles

Purpose The purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model. Design/methodology/approach To accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed. Findings The travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions. Practical implications This study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles. Originality/value The novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

Estimation of wind load on supertall buildings using partial output measurements

Supertall buildings possess distinctive characteristics such as high flexibility, low damping, and susceptibility to wind. The impact of the wind load and wind-induced responses plays a critical role in ensuring the safety and functionality of these structures. Consequently, investigating the wind load and wind-induced responses of supertall buildings has immense practical significance and engineering value to guarantee the secure operation of such buildings under strong wind conditions. However, the direct measurement of wind loads and wind-induced responses across all building floors presents substantial challenges, including considerable engineering workload and implementation difficulties. This study proposes a novel approach based on Kalman filtering to estimate wind loads and wind-induced responses in supertall buildings. This approach involves fusing partial acceleration and displacement measurements to estimate wind load and wind-induced response accurately. Additionally, optimized sensor placement was considered based on the iterative process of the effective independence method. Wind-induced responses were obtained through numerical testing and subsequently utilized to estimate the wind loads on a supertall building. Various factors, such as measurement noise, modeling errors of structural dynamic properties, sensor placement, and response synchronization, were thoroughly discussed to assess their impact on the overall analysis.

Similarity Measures between Buying and Selling Prices of Various Exchange Rates with the inclusion of Multidimensionality and Multicollinearity

This paper introduces a new statistical model, the multidimensional measurement error model with multicollinearity, to study the relationship between buying and selling prices of foreign exchange rates of various currencies. Such a model is needed due to the possible rise in multidimensionality and multicollinearity issues that may occur due to the movement of the financial markets towards stock market integration of various countries since the occurrences of the financial crisis as well as the part result of globalization. As this integration involves the participation of various countries, it will affect the foreign exchange rate. Hence, the analyses are performed on seven currencies against the Malaysian Ringgit where four models’ performances are compared. From this research, it can be concluded that the proposed model comparatively performs better than the other models in representing the relationship of the stationary prices and performs as well as existing models towards non-stationary prices. It can also be seen that the Japanese Yen is the currency that has the strongest influence and closer similar trends towards other currencies while the Great British Pound showed otherwise.

Real-time prediction method of carbon concentration in carburized steel based on a BP neural network

ABSTRACT Gears are key components of mechanical transmission systems. They need to be carburized and quenched to meet the requirements of internal toughness and external hardness, to obtain higher hardness and wear resistance. Heat treatment engineers can calculate carbon concentration distribution through the finite element analysis method, facing the challenge of high computational cost and high memory requirements. To solve this problem, a real-time prediction method of 1D and 2D carburizing concentration based on a Back Propagation (BP) neural network (BPNN) is proposed. First, carburizing experiments were conducted to verify the accuracy of the carburizing numerical model. Then, by establishing an accurate carburizing model, 37,800 and 177,147 1D and 2D carburizing training samples were generated using the finite element model (FEM) method, respectively. Finally, for 1D carburizing, the average relative error of the training model on the test set is 0.2911%. For 2D carburizing, the average relative error of the reconstructed BPNN model on the test set was 0.4381%, and a real-time performance evaluation was performed. The carbon concentration prediction time for each set of process parameters is only 0.12 s, nearly 175 times faster than FEM calculation, which meets the accuracy and real-time requirements for real-time prediction of carburizing carbon concentration.

Non-invasive monitoring of microalgae cultivations using hyperspectral imager

AbstractHigh expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive monitoring method based on a hyperspectral imager was developed for laboratory scale and afterwards tested on industrial scale cultivations. In the laboratory experiments, reference data for microalgal biomass concentration was gathered to construct 1) a vegetation index-based linear regression model and 2) a one-dimensional convolutional neural network model to resolve microalgae biomass concentration from the spectral images. The two modelling approaches were compared. The mean absolute percentage error (MAPE) for the index-based model was 15–24%, with the standard deviation (SD) of 13-18 for the different species. MAPE for the convolutional neural network was 11–26% (SD = 10–22). Both models predicted the biomass well. The convolutional neural network could also classify the monocultures of green algae by species (accuracy of 97–99%). The index-based model was fast to construct and easy to interpret. The index-based monitoring was also tested in an industrial setup demonstrating a promising ability to retrieve microalgae-biomass-based signals in different cultivation systems.

Temperature compensation model for non-dispersive infrared CO2 gas sensor based on WOA-BP algorithm

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor.

Quantification of modeling uncertainty in the Rayleigh damping model

AbstractUnderstanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.

Machine Learning Prediction and Evaluation for Structural Damage Comfort of Suspension Footbridge

To investigate the impact of structural damages on the comfort level of suspension footbridges under human-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The aim is to achieve rapid estimation and health monitoring of comfort levels during bridge operation. To accomplish this, the study combines finite-element simulation results to establish a data-driven library and introduces three distinct machine learning algorithms. Through comparative analysis, a machine learning-based method is proposed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong Suspension Bridge, the study evaluates and researches the comfort level of the structure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when considering the mass of individuals, peak acceleration decreased. The predictive performance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performance analysis was below 5%. Furthermore, the study identified a damage coefficient of 0.13 for the bridge’s main cable, hanger, and steel longitudinal beam. Under a crowd density of 0.5 people per square meter, the predicted peak acceleration was 1.098 m/s2, with a model error of less than 10% compared to the observed value of 1.004 m/s2. These results underscore the model’s effectiveness in swiftly evaluating bridge comfort levels, thereby offering valuable insights for the health monitoring of bridge comfort levels.

A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.

Study on the influence of a magnetorheological finishing path on the mid-frequency errors of optical element surfaces

Magnetorheological finishing (MRF) is a deterministic optical processing technique based on CCOS that achieves high removal efficiency and processing accuracy while reducing subsurface damage. This technique still suffers from multiple iterations of processing due to variations in removal efficiency and the inability to fully correct mid-frequency errors below the cut-off frequency of the removal function. For the above problems, this paper attempted to establish the error model of removal function efficiency change for predicting the change of MRF efficiency. Based on the analysis of the distribution of surface shape residuals under different machining paths, a process combining spiral scanning and raster scanning is proposed, which can realize the correction of surface shape and restrain the deterioration of mid-frequency errors. The experimental results show that when the low-frequency errors of fused silica element surface converge rapidly, by optimizing the machining removal coefficient and using the spiral scanning and raster scanning combined method, the PSD analysis results show that the mid-frequency errors of the combined process is lower than the initial value, which expands the process route for the MRF of high-precision optical elements.

Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision

The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation method, which can accurately estimate the mass of free-swimming tilapia under non-contact conditions. First, image enhancement is performed on the original image, and the depth image is obtained by correcting and stereo matching the enhanced image using binocular stereo vision technology. And the fish body is segmented by an SAM model. Then, the segmented fish body is labeled with key points, thus realizing the 3D reconstruction of tilapia. Five mass estimation models are established based on the relationship between the body length and the mass of tilapia, so as to realize the mass estimation of tilapia. The results showed that the average relative errors of the method models were 5.34%~7.25%. The coefficient of determination of the final tilapia mass estimation with manual measurement was 0.99, and the average relative error was 5.90%. The improvement over existing deep learning methods is about 1.54%. This study will provide key technical support for the non-destructive measurement of tilapia, which is of great significance to the information management of aquaculture, the assessment of fish growth condition, and baiting control.

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

Statistical data transformation in agrarian sciences for variance analysis: a systematic review

In statistical analyses, a common practice for enhancing the validity of variance analysis is the application of data transformation to convert measurements into a different mathematical scale. This technique was first employed in 1898 by Edgeworth and remains relevant in current scientific publications despite the proliferation of more modern and advanced techniques that obviate the need for certain assumptions. Data transformations, when appropriately used, can make the model error terms approximate a normal distribution. It is also possible to use the technique to correct the heterogeneity of variances or to render an additive model, ensuring the validity of the analysis of variances. Given that this technique can be hastily applied, potentially leading to erroneous or invalid results, we conducted a systematic literature review of studies in the field of agrarian sciences that utilized data transformations for the validation of analysis of variances. The aim was to check the transformations employed by the scientific community, the motivation behind their use, and to identify possible errors and inconsistencies in applying the technique in publications. In this study, we identified shortcomings and misconceptions associated with using this method, and we observed incomplete and inadequate utilization of the technique in 94.28% of the analysed sample, resulting in misguided and erroneous conclusions in scientific research outcomes.

Impact of Sharing Imprecise Information on Fixed-Bin Selective Assembly

Fixed-Bin Selective Assembly (FBSA) is widely used to improve the assembly quality of single units of different component types. FBSA has parts with similar dimensions, but with slight deviations due to production variances, sorted into pre-determined bins. A selection is made from paired bins for assembly. To ensure enough components, assemblers adopt a proactive or reactive strategy. For the proactive, predictions of component usage quantities are used to adjust processes for the internally manufactured matched component prior to production. With the reactive, component inventory levels are adjusted after assembly. The performance of each can be improved with information about incoming part dimensions. However, the communicated information may be imprecise. We developed a Bayesian Measurement Error Model to evaluate the impact of imprecision, in the supplier’s communicated information, on the efficacy of FBSA with either strategy. The model was tested with parameters obtained from a US assembler of combustion engines. We find that in most scenarios, the proactive approach results in better FBSA efficacy on average. A key exception was when imprecise information about the mean was communicated, resulting in no significant difference. The reactive approach is typically easier to implement, therefore it may be preferred when communicated information about the mean of incoming part dimensions is often inaccurate.

Backstepping and Novel Sliding Mode Trajectory Tracking Controller for Wheeled Mobile Robots

A novel variable structure controller based on sliding mode is developed for addressing the trajectory tracking challenge encountered by wheeled mobile robots. Firstly, the trajectory tracking error model under the global coordinate system is established according to the kinematic model of the wheeled mobile robot. Secondly, the novel sliding mode algorithm and backstepping method are introduced to design the motion controller of the system, respectively. Different sliding mode surfaces are formulated to guarantee rapid and stable convergence of the system’s trajectory tracking error to zero. Ultimately, comparative simulation trials validate the controller’s ability to swiftly and consistently follow the reference trajectory. In contrast to traditional controllers, this controller shows rapid convergence, minimal error, and robustness.

Spatiotemporal evolutions and reasons of land-use carbon emissions in counties, Shaanxi Province, China

Global warming caused by factors such as the expansion of construction land poses a major threat to the sustainable development of China. As an important component of China's low-carbon development strategy, there is a relative lack of analysis of the spatial and temporal evolution of land-use carbon emissions and the influencing factors at county-level. In this study, we employed Emission-Factor Approach to estimate carbon emissions from land use in 107 counties, Shaanxi province, China. Our findings revealed construction land were the primary contributors to carbon emissions, showing a substantial increase from 2000 to 2020. There was positive spatial autocorrelation in carbon emissions among counties, forming distinct aggregation patterns around the City of Xi'an and both the southern and northern regions of Shaanxi. By utilizing the Spatial Durbin Error Model (SDEM), demographic factors emerged as key drivers of carbon emissions, indicating the significance of addressing population concentration to curb emissions. Furthermore, promoting coordinated development and adjusting the economic structure in different counties can mitigate both population concentration and carbon emissions. Emphasizing industrial development and investments can also effectively suppress carbon emissions. Additionally, managing transportation-related emissions can be achieved by enhancing public transportation services and regulating private car usage.

On quantifying heterogeneous treatment effects with regression‐based individualized treatment rules: Loss function families and bounds on estimation error

SummaryHeterogeneity in response to treatment is a pervasive problem in medicine. Many researchers have proposed individualized treatment rule methods for this problem, which personalize treatment recommendations based on an individual's recorded covariates. A challenge with using these methods in practice is that they determine a treatment rule, rather than quantify treatment benefit. This can be problematic, as a recommended treatment could be burdensome and have negligible improvements in outcome for some individuals. With the aim of helping practitioners make informed modelling choices, we identify two families of loss functions to use with individualized treatment rule methods. Under the assumption of correct model specification, estimation with a loss function from one family ensures that the model's treatment recommendations can be interpreted in terms of the risk difference, while the other family of loss functions ensures that the model's treatment recommendations can be interpreted in terms of the risk ratio. We also derive two upper bounds for a model's error in risk difference and risk ratio estimation. Each upper bound can be calculated using observed data and can provide insight to practitioners regarding model error in estimating treatment effects. We illustrate our contributions with simulation studies as well as with data from the ACTG‐175 AIDS study.

Accurate kinematic calibration of a six-DoF serial robot by using hybrid models with reduced dimension and minimized linearization errors

Accurate kinematic calibration of a six-DoF serial robot by using hybrid models with reduced dimension and minimized linearization errors