Conventional Estimation Methods Research Articles

Pharmacokinetic Simulation and Area under the Curve Estimation of Drugs Subject to Enterohepatic Circulation

Enterohepatic circulation (EHC) is a complex process where drugs undergo secretion and reabsorption from the intestinal lumen multiple times, resulting in pharmacokinetic profiles with multiple peaks. The impact of EHC on area under the curve (AUC) has been a topic of extensive debate, questioning the suitability of conventional AUC estimation methods. Moreover, a universal model for accurately estimating AUC in EHC scenarios is lacking. To address this gap, we conducted a simulation study evaluating five empirical models under various sampling strategies to assess their performance in AUC estimation. Our results identify the most suitable model for EHC scenarios and underscore the critical role of meal-based sampling strategies in accurate AUC estimation. Additionally, we demonstrate that while the trapezoidal method performs comparably to other models with a large number of samples, alternative models are essential when sample numbers are limited. These findings not only illuminate how EHC influences AUC but also pave the way for the application of empirical models in real-world drug studies.

Estimating the household secondary attack rate and serial interval of COVID-19 using social media

We propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.

Robust pixel-wise illuminant estimation algorithm for images with a low bit-depth

Conventional illuminant estimation methods were developed for scenes with a uniform illumination, while recently developed methods, such as pixel-wise methods, estimate the illuminants at the pixel level, making them applicable to a wider range of scenes. It was found that the same pixel-wise algorithm had very different performance when applied to images with different bit-depths, with up to a 30% decrease in accuracy for images having a lower bit-depth. Image signal processing (ISP) pipelines, however, prefer to deal with images with a lower bit-depth. In this paper, the analyses show that such a reduction was due to the loss of details and increase of noises, which were never identified in the past. We propose a method combining the L1 loss optimization and physical-constrained post-processing. The proposed method was found to result in around 40% higher estimation accuracy, in comparison to the state-of-the-art DNN-based methods.

Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

Spatial Spectrum Estimation of Weak Scattering Wave Signal in Range-Doppler Domain

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

Developing a semi-analytical model for estimating mechanical properties of sandstone reservoirs: Enhancing applications in hydrocarbon production and underground gas storage

Rock mechanical properties, encompassing the static Young's modulus, bulk modulus, and shear modulus, influence strategic decision-making operations, such as drilling programs and well completion designs. When precisely calculated, these variables can significantly affect drilling cost-efficiencies and production economic returns. Even though these attributes are significant, the conventional estimation method is core-lab testing, which is expensive and limited to covering the entire reservoir strata. To address this challenge, we propose a new semi-analytical model with direct equations to estimate the elastic rock properties primarily anchored around porosity. In particular, we developed an analytical framework using generalized equations for bulk and shear modulus to calculate static rock mechanical properties as porosity-driven functions. We employed lab-tested core data from 55 core samples to validate the new model's efficiency. The model's innovative design allows computing all elastic properties solely based on porosity. Impressively, the results closely matched the lab measurements. The new semi-analytical model's equations yielded a good root mean square error (RMSE) in the calculated rock mechanical properties. Specifically, the new static Young's modulus equation has the lowest RMSE, 1.489, compared to 45.168 for the Edimann equation and 3.043 for the Zhang equation. The newly developed static shear modulus equation had an RMSE of 0.497, lower than Zhang's empirical equation's 1.262. However, the Zhang equation has an RMSE of 0.891 versus 2.029, slightly outperforming the new equation. According to the RMSE values, the discrepancy between the core sample-based measured, new semi-analytical model-calculated static modulus was caused by the fact that the core samples were collected from clean sand oil-bearing reservoir intervals. However, the derived equations used porosity ranges that covered shale and sand. In essence, the new semi-analytical model provides a fast and precise estimation of rock mechanical properties, easily solving drilling concerns. This research not only advances knowledge of drilling and geomechanics, but also makes strategies more efficient and cost-effective.

Doppler spread analysis for suppressing channel time variation in high‐mobility massive MIMO V2V communications

AbstractHere, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of , where and are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications.

Angular acceleration estimation and aerodynamic parameter identification based on angular velocity equivalent model

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification.

Noise power spectrum (NPS) in computed tomography: Enabling local NPS measurement without stationarity and ergodicity assumptions.

Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems. The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPSestimations. The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the newmethod. (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPSvariations. The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improvedprecision.

A novel hybrid-view technique for accurate mass estimation of kimchi cabbage using computer vision

This study addresses the accurate estimation of kimchi cabbage mass, as cabbage leaves exhibit size variability and complex leaf structures. Conventional mass estimation methods, which rely solely on external imaging, often overlook leaf gaps. To improve accuracy, we propose an innovative computer vision system utilizing hybrid-view images and detailed saturation analysis. Our system quantifies the impact of leaf gaps on mass using features from the saturation channel of images of bisected cabbage. Our proposed method can be easily integrated into existing workflows and has the potential to improve labor efficiency. Our approach outperforms the conventional method (R2 of 0.66 and relative error of 8.68%), achieving a 0.92 R2 value and lowering the relative error to 4.22%. This advancement offers a robust solution for the mass estimation of kimchi cabbage and suggests potential applications for other foods and crops with internal voids.

Bayesian predictive modeling for gas purification using breakthrough curves

This study proposes a predictive model for assessing adsorber performance in gas purification processes, specifically targeting the removal of chemical warfare agents (CWAs) using breakthrough curve analysis. Conventional parameter estimation methods, such as Brunauer-Emmett-Teller analysis, encounter challenges due to the limited availability of kinetic and equilibrium data for CWAs. To overcome these challenges, we implement a Bayesian parametric inference method, facilitating direct parameter estimation from breakthrough curves. The model’s efficacy is confirmed by applying it to H2S purification in a fixed-bed setup, where predicted breakthrough curves aligned closely with previous experimental and numerical studies. Furthermore, the model is applied to sarin with ASZM-TEDA carbon, estimating key parameters that could not be assessed through conventional experimental techniques. The reconstructed breakthrough curves closely match actual measurements, highlighting the model’s accuracy and robustness. This study not only enhances filter performance prediction for CWAs but also offers a streamlined approach for evaluating gas purification technologies under limited experimental data conditions.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

In Silico and Chromatographic Methods for Analysis of Biotransformation of Prospective Neuroprotective Pyrrole-Based Hydrazone in Isolated Rat Hepatocytes.

In the current study, chromatographic and in silico techniques were applied to investigate the biotransformation of ethyl 5-(4-bromophenyl)-1-(2-(2-(2-hydroxybenzylidene) hydrazinyl)-2-oxoethyl)-2-methyl-1H-pyrrole-3-carboxylate (11b) in hepatocytic media. The initial chromatographic procedure was based on the employment of the conventional octadecyl stationary phase method for estimation of the chemical stability. Subsequently, a novel and rapid chromatographic approach based on a phenyl-hexyl column was developed, aiming to separate the possible metabolites. Both methods were performed on a Dionex 3000 ThermoScientific (ACM 2, Sofia, Bulgaria) device equipped with a diode array detector set up at 272 and 279 nm for analytes detection. An acetonitrile: phosphate buffer of pH 3.5: methanol (17:30:53 v/v/v) was eluted isocratically as a mobile phase with a 1 mL/min flow rate. A preliminary purification from the biological media was achieved by protein precipitation with methanol. A validation procedure was carried out, where the method was found to correspond to all ICH (Q2) and M10 set criteria. Additionally, an in silico-based approach with the online server BioTransformer 3.0 was applied in an attempt to predict the possible metabolites of the title compound 11b. It was hypothesized that four CYP450 isoforms (1A2, 2C9, 3A4, and 2C8) were involved in the phase I metabolism, resulting in the formation of 12 metabolites. Moreover, docking studies were conducted to evaluate the formation of stable complexes between 11b and the aforementioned isoforms. The obtained data indicated three metabolites as the most probable products, two of which (M9_11b and M10_11b) were synthesized by a classical approach for verification. Finally, liquid chromatography with a mass detector was implemented for comprehensive and summarized analysis, and the obtained results revealed that the metabolism of the 11b proceeds possibly with the formation of glucuronide and glycine conjugate of M11_11b.

Smart distribution network voltage estimation using PMU technology considering zero injection constraints.

To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.

Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

Multi-dimensional distribution network voltage collaborative estimation method using wavelet transform based multiscale analysis

Conventional methods for distribution network voltage estimation may encounter resource contention issues when performing noise reduction, harmonic calculation, and singular inspection. To address such problems, this study presents a wavelet transform based multiscale analysis (WT-MSA) method for achieving multifunctional integration of multi-objective collaborative voltage estimation in distribution networks. First, the basic concepts of multiscale space and wavelet function are described, mainly used for decomposing the original signal under different scales of wavelet spaces and then conducting filtering. Second, a voltage coordinated estimation method is established, with a marginal threshold function employed as the criterion for correcting wave coefficients. Following that, an interpolation-energy based method for gradually estimating both fundamental and harmonic effective information is developed. Finally, three different types of Cases are set up for simulation verification, demonstrating that the proposed method meets the accuracy requirements for voltage estimation in distribution networks.

A self-attention knowledge domain adaptation network for commercial lithium-ion batteries state-of-health estimation under shallow cycles

Accurate state-of-health (SOH) estimation is crucial for the safety, efficiency, and reliability of battery-powered systems. Conventional SOH estimation methods are designed for the full state-of-charge (SOC) range of 0%–100%, assuming uniform distributions. Yet, batteries in real-world applications often operate under partial SOC ranges and experience shallow-cycle conditions, which result in distinct and unlabeled degradation profiles that make SOH estimation more complex. To tackle this issue, we propose a novel method—self-attention knowledge domain adaptation network (SKDAN)—which utilizes a self-attention distillation module combined with a multi-kernel maximum mean discrepancy framework to effectively navigate between these disparate domains. This strategy successfully extracts domain-specific features from charging curves, facilitating the transfer of knowledge from well-labeled full cycles to unlabeled shallow cycles. The effectiveness of the SKDAN method is validated through extensive testing on the CALCE and SNL battery datasets, demonstrating its robust capability to estimate SOH accurately across different SOC ranges, temperatures, and discharge rates. With a root-mean-square error (RMSE) of less than 2%, the SKDAN method outperforms existing transfer learning techniques for various SOC ranges. Additionally, it exhibits exceptional SOH estimation performance when applied to batteries from different manufacturers and operating under varied conditions. This study represents the first instance of accurately estimating battery SOH under shallow-cycle conditions without relying on full-cycle characteristic tests.

A comprehensive evaluation of conventional methods for estimation of fatigue parameters of steels from their monotonic properties

A number of conventional and machine learning-based methods for the estimation of strain-life fatigue parameters from monotonic properties have been proposed in the literature and new ones are continuously being developed. With the development of new steels having improved properties such as higher ultimate strength, selecting appropriate estimation method becomes both increasingly difficult and important. In this study, according to the proposed detailed evaluation methodology, 10 conventional estimation methods have been evaluated regarding their accuracy and applicability for estimation of low- and high-cycle fatigue lives of unalloyed, low-alloy and high-alloy steels which were further divided into low- and high-strength subgroups. For this purpose, detailed, fully populated material dataset was assembled, including monotonic properties, cyclic stress–strain, and strain-life fatigue parameters of 75 unalloyed steels, 104 low-alloy steels and 44 high-alloy steels. With separate analyses, averaging of evaluation results was avoided and more detailed and precise ratings of individual methods were obtained. Complete material data and full results of the evaluations are made available in the paper which may further facilitate development and objective evaluations of new estimation methods and other predictive models.

Whole value at risk for flood damage estimates through spatial data analysis

Effective disaster risk reduction (DRR) for flooding requires a comprehensive estimate of the whole value at risk (WVAR) to inform appropriate and proportionate mitigation expenditure. Conventional flood risk estimation methods focus on the direct effects of inundation on community value and generally ignore collateral effects on assets and populations outside the flooded area. Consequently, conventional methods tend to underestimate the cost of flooding, leading to an underestimate of the return on DRR investment. Using spatial data analysis in an urban case study for Toronto, Canada, we identify and capture the collateral value at risk (ColVaR) to estimate the WVAR more comprehensively. In our case study, ColVaR (mean estimate) amounts to 70% of direct losses (ColVar = $344 M; direct losses = $475 M CAD), ranging from 20%–150% (ColVar $100–$740 M) when spanning the 90% confidence intervals of our Monte Carlo simulations. Thus, we demonstrate that if the collateral value at risk is ignored, WVAR can be significantly underestimated, potentially leading to reduced disaster risk reduction resource allocations and thereby adding risk exposure for communities. We present an accessible, seven-step process using existing spatial analysis tools and techniques that infrastructure stakeholders and planners can use to estimate ColVaR and better formulate DRR measures for their communities.

Prediction of Non-Stationary Multi-Head Cloud Motion Vectors for Intra-Hourly Satellite-Derived Solar Power Forecasting

Solar photovoltaic (PV) power has become one of the major renewable energy sources in modern power systems. Hence, solar forecasting is indispensable for addressing the uncertainties of PV power outputs. Since PV power generation mainly depends on solar radiation conditions, satellite-derived methods have been proven effective in large-scale short-term solar forecasting. These methods utilize satellite optical images to track cloud motion and do not require supplemental ground cameras. However, cloud motion between two consecutive satellite images is highly non-stationary, as the motion includes both displacement and shape variation. Conventional motion estimation methods may miss the detailed cloud variation information, increasing the errors in solar forecasting. In this study, a multi-head cloud motion vector prediction method is proposed to address this challenge. The method models cloud variation as the probabilistic sum of pixels moving under multiple optical flow vectors. Time-varying solar intensity is also considered in the method. In order to estimate these optical flow vectors, a learning-based intelligent model with a self-attention structure is established. The proposed method of multi-step-ahead satellite-derived solar forecasting is superior to conventional methods, as the results of case studies indicate that the proposed method improves the precision of intra-hourly PV power prediction.