Interval Error Research Articles

A Short-term PV Power Prediction and Uncertainty Analysis Model Based on CEEMDAN and AHA-BP

The stochastic and intermittent nature of photovoltaic (PV) generation brings a series of scheduling problems to the power system. An effective prediction of PV power is essential to minimize the impact of uncertainty. Therefore, this paper presents an integrated prediction model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the artificial hummingbird algorithm (AHA), and the BP neural network (BPNN) for predicting power generation from PV power plants, and a methodology for uncertainty analysis by using the nonparametric kernel density estimation (NPKDE). First, one month of PV power is decomposed into an array of components using CEEMDAN. Then, the weights and thresholds of the BPNN are optimized by using AHA. These components are trained using the BPNN. Finally, the final prediction results are obtained by superimposing the components, and NPKDE is employed to compute the probability density and confidence interval of the prediction error. The proposed prediction method demonstrates superior predictive performance in comparison with other models. Also, the NPKDE approach better describes the accuracy of the probability density distribution.

Association between fitness levels and physical activity in apparently healthy Colombian men

Abstract Physical condition (PC) such as strength and endurance favor functional independence and disease prevention (Melo, 2014). Objective: To characterize the levels of Physical Activity (PA) and CP in apparently healthy men who practice PA in different settings in Bogotá-Colombia. This study is observational cross-sectional, 485 Men (M) participated, mean age 42±17.8 years. To measure the levels PA the IPAQ (International Physical Activity Questionnaires) was applied. The Confidence Interval (CI) and Standard Error (SE) were taken into account. For PA levels, data were analyzed with Rstudio v2022.12.0, with empirical distribution tests, Kolmogorov-Smirnov normality and independence with Chi-Square (χ²), in addition, mean (X¯), percentage (%) and standard deviation (±). 90.6% of the H met the overall PA recommendations (Leisure Time+Active Transportation) with 95% CI (88.0-93.0) and SE (0.012). The PC variables such as; Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Circumference (AC), Grip Strength (GS) and Maximal Oxygen Consumption (Vo2max). The results showed that 35.6% (26.9±1.48) of H were Overweight (Ow) and 10.6% (32.7±2.39) were Obese (Ob), 39.7% (98.4±6.8) with Cardiovascular Risk (Rcv) by AC and 24.3% (29.7±3.45) with risk by BAI. As for GS 41.5% (27.1±6.39) is weak and 32.3% (33.3±6.3) is low Vo2max. According to independence tests with χ² there is association between the G and BMI (χ²=1452.1, df = 1427, p-value=0. 315), G and AC (χ²=394.9, df = 382, p-value=0. 313), G and Vo2max (χ²=155.5, df = 6, p-value<2.2e-16), G and BAI (χ²=2970.3, df = 1502, p-value<2.2e-16), G and GS (χ²=6015.5, df = 1502, p-value<2.2e-16). This project was approved by the bioethics committee. Conclusions 149 men corresponding to 30.7% of the sample present an increased possibility of Rcv, despite compliance with global PA recommendations. It is recommended that PA programs reinforce the improvement and promotion of PC to prevent possible Rcv. Key messages • The capacities that define physical fitness (FC) such as strength and endurance favor functional independence and disease prevention (Melo, 2014). • Apparently healthy men in Colombia present a higher possibility of Cardiovascular Risk due to low levels of physical fitness, despite compliance with global BP recommendations.

Measurement error on the number of sheets of the paper on the vibratory feeder

The work includes an analysis of the accuracy of estimating the determined quantity of paper sheets on vibratory conveyors from the perspective of probability theory and mathematical statistics. A resulting formula for determining the confidence interval of measurement error was obtained. It was found that for paper with a coefficient of variation in the range of 0.52%–1.29% (with a total number of sheets of 4000), the confidence interval of error due to the weight non-uniformity of sheets exceeds the confidence interval of error due to inaccuracies in large and small weights by approximately 5 times.

Kinematic Reliability of Manipulators Based on an Interval Approach

Robotic manipulators inevitably experience the impact of uncertainties and errors, such as dimensional tolerances and joint clearances. Therefore, this paper proposes a novel method based on an interval approach to evaluate the kinematic reliability of manipulators. Kinematic reliability quantifies the probability of positioning errors that fall within allowable boundaries. As a result, reliability evaluates the probability that the interval end-effector error produced by dimensional tolerances exceeds an acceptable rate. The proposed reliability index is based on the interval error that conveys an alternative approach to the kinematic reliability methods based on probabilistic frameworks reported in the literature based on probabilistic approaches. The obtained numerical results demonstrate the viability of the proposed methodology by evaluating the reliability of a serial manipulator subjected to joint clearances and a parallel manipulator with dimensional tolerances.

Analysing the informative content of the upper jaw pathology visual examination

Objective. To conduct a comparative analysis of the informative content of cone beam computed tomography (CBCT), multispiral computed tomography (MSCT) and paranasal sinuses half-axial X-ray images in the upper jaw pathology diagnosis. Materials and methods. 96 CBCT and MSCT scans of the upper jaw and the similar number of X-ray images of the same patients were analyzed. Results. The analysis of a three-dimensional image and native slice reading in the Planmeca programme makes it possible to determine metric parameters, localization, prevalence of the pathological process and involvement of the upper jaw structures validly. Software features of the Radiant and OneDemand3D had error interval of р ≥ 0,2 mm while analyzing the same criteria. Half-axial X-ray examination demonstrated the lowest informative content. Conclusion. The most informative method of visualization of the upper jaw connective tissue structures, pathological processes, their borders and mutual involvement is CBCT. This is predetermined by the interface functionalities.

Probabilistic Prediction on Seismic Responses of a Long-Span High-Speed Railway Bridge Using BPINN

Efficient probabilistic prediction of seismic responses is crucial for assessing the seismic-resistant capability of long-span continuous girder high-speed railway bridges. Thus, a Bayesian physics-informed neural network (BPINN) is adopted to rapidly and effectively predict the probabilistic seismic responses of such bridges. The BPINN model combines deep learning and physics to improve the accuracy and consistency of predictions, while also quantifying the uncertainties using Bayesian inference methods. Various seismic excitations, including pulse, far-field and near-field types, are employed to probabilistically predict the seismic responses of the top of bridge pier. Evaluation metrics, including mean squared error and prediction interval coverage probability, are used to assess the deterministic and probabilistic estimates of BPINN. Results demonstrate that BPINN performs better in deterministic results for far-/near-field ground motions compared to pulse-like earthquakes, with most cases exhibiting a close approximation to the 95% confidence interval. The flexibility and adaptability of BPINN in handling different types of ground motions can provide valuable insights for assessing the seismic performance of such structures.

A physiologically based pharmacokinetic model of voriconazole in human CNS—Integrating time-dependent inhibition of CYP3A4, genetic polymorphisms of CYP2C19 and possible transporter mechanisms

ObjectivesVoriconazole is a classical antifungal drug that is often used to treat CNS fungal infections due to its permeability through the BBB. However, its clinical use remains challenging because of its narrow therapeutic window and wide inter-individual variability. In this study, we proposed an optimised and validated PBPK model by integrating in vitro, in vivo and clinical data to simulate the distribution and PK process of voriconazole in the CNS, providing guidance for clinical individualised treatment. MethodsThe model structure was optimised and tissue-to-plasma partition coefficients were obtained through animal experiments. Using the allometric relationships, the distribution of voriconazole in the human CNS was predicted. The model integrated factors affecting inter-individual variation and drug interactions of voriconazole—polymorphisms in the CYP2C19 gene and auto-inhibition and then was validated using real clinical data. ResultsThe overall AFE value showing model predicted differences was 1.1420 in the healthy population; and in the first prediction of plasma and CSF in actual clinical patients, 89.5% of the values were within the 2-fold error interval, indicating good predictive performance of the model. The bioavailability of voriconazole varied at different doses (39%-86%), and the optimised model conformed to this pattern (46%–83%). ConclusionsCombined with the relevant pharmacodynamic indexes, the PBPK model provides a feasible way for precise medication in patients with CNS infection and improve the treatment effect and prognosis.

Formula-led methods using first morning fasting spot urine to assess usual salt intake: a secondary analysis of PURE study data.

Observational studies that assess the relationship between salt intake and long-term outcomes require a valid estimate of usual salt intake. The gold-standard measure in individuals is sodium excretion in multiple nonconsecutive 24-h urines. Multiple studies have demonstrated that random spot urine samples are not valid for estimating usual salt intake; however, some researchers believe that fasting morning spot urine samples produce a better measure of usual salt intake than random spot samples. We have used publicly available data from a PURE China validation study to compare estimates of usual salt intake from morning spot urine samples and three published formulae with mean of two 24-h urine samples (reference). We estimated the means and 95% confidence intervals of absolute and relative errors for each formula-led method and the degree to which estimates were able to be classified into the correct quartile of intake. Bland-Altman plots were used to test the level of agreement. The results show that compared with the reference method, all formulae-led estimates from spot urine collections have high error rates: both random and systematic. This is demonstrated for individual estimates, as well as by quartiles of reference salt intake. This study conclusively demonstrates the unsuitability of morning spot urine formula-led estimates of usual salt intake. Our findings support international recommendations to not conduct, fund, or publish research studies that use spot urine samples with estimating equations to assess individuals' salt intake in association with health outcomes.

Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design.

Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in the clinical development. The main concerns in dose-finding study are to identify a minimum effective dose (MED) in anesthesia studies and maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, we propose two modifications of Firth's logistic regression using reparametrization, called reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, we develop a method on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals.

Preliminary field estimation of vertical displacements of prestressed concrete bridges under construction based on measured strains and inclinations

The development of continuous and automatic quantitative monitoring methods will improve bridge construction. This paper proposes a method for estimating vertical bridge displacement during construction based on the Bayesian inverse problem. The proposed method is applied to a bridge under construction in Japan. Case studies suggest that the observed rotation angle is more important than the observed strain for the estimation of vertical displacement. Based on strain and rotation angle measurements, vertical bridge displacement was estimated with an accuracy of less than 2 mm, as determined from a comparison with displacements obtained in a leveling survey. The 95 % confidence interval of the estimation error is consistent with the scatter of measured data of displacement, rotation angle, and strain.

Conditional bias adjusted estimator of treatment effect in 2-in-1 adaptive design

ABSTRACT For implementation of adaptive design, the adjustment of bias in treatment effect estimation becomes an increasingly important topic in recent years. While adaptive design literature traditionally focuses on the control of type I error rate and the adjustment of overall unconditional bias, the research on adjusting conditional bias has been limited. This paper proposes a conditional bias adjustment estimator of treatment effect under the context of 2-in-1 adaptive design and aims to provide a comprehensive investigation on their statistical properties including bias, mean squared error and coverage probability of confidence intervals. It demonstrated that conditional bias adjusted estimators greatly reduce the conditional bias and have similarly negligible unconditional bias compared with mean and median (unconditional) unbiased estimators. In addition, the test statistics is constructed based on the conditional bias adjustment estimators and compared with the naive unadjusted test.

Effects of local time on the variations of the total electron contents at an American and Asian longitudes and their comparison with IRI-2016, IRI-Plas2017 and NeQuick-2 models during solar cycle 24

Ionospheric modelling is one of the major tools to study the behavior of the ionosphere. Ionospheric models have been useful in predicting the true state of the ionosphere particularly in regions where Global Positioning System (GPS) are not readily available. This research paper aims to study the longitudinal variations and the effects of local time on the total electron content (TEC) recorded in two different sectors (Asia and America) during the ascending, maximum and descending phases of solar cycle 24 (2011–2017) and also to compare its values to IRI-2016, IRI-Plas2017 and NeQuick-2 models in order to evaluate their performances. An hourly interval profile computed on seasonal basis were used to study the behaviors of TEC diurnally and seasonally. A monthly interval error profile plotted on annual basis was also used to investigate the deviations of the models from the GPS values. Our results showed that the peak values of TEC in the Asian and American sectors were recorded around the dawn,06:00UT (13:00LT) and dusk, 18:00UT (15:00LT) respectively. We also affirmed from our results that seasonal/winter anomalies were recorded in all the phases of the solar cycle in both sectors. Equinoctial Asymmetry was also observed to be predominant during different phases of the solar cycle in both sectors except during ascending and descending phases in the Asian and American sectors respectively. Out of the 168 months of data collated for this study, only 162 months of data were available. The IRI-2016, IRI-Plas2017 and NeQuick-2 models have 11.7%, 23.5% and 64.8% better performance in all the months under consideration. Therefore, the NeQuick-2 model had the best performance in both the Asian and American sectors. Finally, from the results of our statistical analysis, Mean Absolute Error (MAE) has ∼3 TECU lower than the Root Mean Square Error (RMSE) values in both sectors and in all the solar cycle phase. Hence, MAE can evaluate the performance of ionospheric models better than RMSE.

Development of a Comprehensive Tool for Assessing Cardiorespiratory, Muscular Strength, and Endurance Function in People With Intellectual Disabilities

Background and Aims Accurate assessment of cardiorespiratory function, muscular strength, and endurance seems necessary to be aware of, provide exercise interventions, and prevent subsequent diseases. Therefore, the current research aimed to develop a comprehensive tool for assessing cardiorespiratory, muscular strength, and endurance function in people with intellectual disabilities. Methods The current research is fundamental and practical in terms of purpose. In this study, the psychometrics of 51 specific intellectual disability tests were used to develop a tool for measuring cardiorespiratory function, muscular strength, and endurance. The face validity of the instrument through a 51-test questionnaire by 15 experts and 15 intellectually disabled educators, the content validity through a 51-test questionnaire by 15 experts, and the construct validity through a 19-test questionnaire by 243 intellectually disabled educators were examined. The tool’s reliability was checked through internal, intra-rater, and inter-rater consistency. Internal consistency was estimated by calculating the Cronbach alpha. The intra-rater and inter-rater reliabilities were also checked through the implementation of the tests by 18 people with intellectual disability in three time points and with three raters through the internal correlation index (ICC), confidence interval (CI), and standard error of measurement (SEM). Data analysis was done through exploratory and confirmatory factor analysis with AMOS version 26 and SPSS software, version 22. Results Based on the content validity ratio (CVR) estimation, 19 out of 51 tests had a content validity ratio higher than 0.49, and the rest were excluded. Regarding construct validity, 10 out of 19 tests were removed, and the final 9 tests were approved for evaluating cardiorespiratory function, muscular strength, and endurance. Exploratory factor analysis extracted three latent factors that explained 46.21% of the total variance. Confirmatory factor analysis confirmed the final construct model. The factor analysis results included KMO=0.829 and BT=4706.93 (P<0.0001). Conclusion The current study provided a special set of tests for people with intellectual disability regarding cardiorespiratory assessment (one test), strength (four tests), and muscular endurance (four tests) after psychometry of 51 specific tests. By using this set of tests, it is possible to make an accurate assessment of the physical fitness of the health of people with intellectual disability.

Multi-objective optimization and uncertainty quantification for inductors based on neural network

PurposeThe prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes.Design/methodology/approachUnder Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements.FindingsThe NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods.Originality/valueThree objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

Structural deformation and clamping force monitoring of reconfigurable tooling motivated by strain data in aircraft assembly

The quality of aircraft assembly is mainly guaranteed by toolings which are vital to the geometrical accuracy and service performance of aviation products. In this research, a real-time monitoring system that determines the structural deformation and clamping force of reconfigurable toolings using strain data is developed to perceive the service state of the toolings. By laying fiber Bragg gratings on positioning beam and baseplate of the reconfigurable tooling, strain data of the tooling structures are gauged and transferred to curvatures. The beam and baseplate are modeled as one and two-dimensional objects respectively and shape reconstruction algorithms are established to obtain their deflection curve and surface using curvature information. Distribution of fibers is optimized to minimize the conversion error from strain to curvature. An estimation that reveals the mathematical relationship between the shape reconstruction error and measurement interval is implemented, and a mapping model from strains to clamping force of the beam is established. These algorithms are integrated into the self-developed monitoring software and undergo simulating and experimental tests. The maximum relative errors of deformation and force are 4.53% and 4.12% respectively in simulation, and 9.21% and 7.29% individually in experiment, which validates the efficiencies of the method. Tests of the monitoring system suggest that it can provide a timely and accurate sensing of the deformation and force of the tooling.

Active underactuation fault‐tolerant backstepping attitude tracking control of a satellite with interval error constraints

AbstractUnderactuation poses a significant challenge to space mission control and performance. This article investigates the non‐linear attitude tracking control problem for a remote sensing satellite underactuated by a reaction wheel (RW) actuator fault. First, a timeline close to the in‐orbit reality of an underactuation fault is presented. Then, the fault detection and diagnosis strategy is performed in a finite‐time decision window. The failed actuator is excluded from the control loop by forming the proposed reconfiguration window to transition from a 3 RWs configuration to 2 RWs. The underactuation fault‐tolerant control is designed according to the active method, where the adaptive robust control law employed for fault‐free conditions is switched to the underactuated attitude tracking control (UATC). The structure of UATC is based on kinematic and adaptive backstepping dynamic controllers. The effect of unknown bounded external disturbances is considered with an adaptive estimation term. The asymptotic stability of the closed‐loop control system is proved via Lyapunov theory in the presence of parametric uncertainty. Due to the underactuation, a new approach proposed in the prescribed performance function is interval error constraints, which include the pointing accuracy and stability requirements in imaging time intervals. Finally, the results of the multidisciplinary simulation and experimental test confirm the applicability of the underactuation fault‐tolerant control.

Self-assessment using 30-second chair stand test for patients with knee osteoarthritis – an intra- and inter-rater reliability study

Background We aimed to evaluate the intra-rater reliability in 30-second chair stand test (30CST) as a self-test, and the inter-rater reliability comparing a self-test with a physiotherapist-assessed 30CST for patients with knee osteoarthritis (KOA). The second purpose was to evaluate the classification ability of the 30CST as self-test to distinguish between reduced physical function and normal function. Methods Patients with KOA performed two self-tests of 30CST at home and thereafter a physiotherapist assessed the patient. Absolute agreements were presented with intraclass correlation coefficient (ICC), 95% confidence interval (CI) and standard error of measurement (SEM). Post-hoc analyses were conducted to calculate minimal detectable change (MDC). The classification ability was analysed with receiver operating characteristic (ROC)-curves and area under the ROC-curve (AUC). The significance level was p < 0.05. Results In total, 114 followed the protocol. Intra-rater and inter-rater reliability were good (ICC 0.97, CI 0.95–0.99, SEM 0.89 and ICC 0.81, CI 0.72–0.87, SEM 2.29 respectively). MDC were 2.5 stands when comparing self-test results individually and 0.23 on a group level. The MDC between self-tests and physiotherapist assessment were 6.4 stands for individual comparison and 0.59 for group comparison. The self-test was 80% likely to detect reduced physical function compared to a physiotherapist assessing physical function with 30CST (AUC = 0.79–0.80). Conclusions The results indicate that 30CST can be a reliable self-assessment of physical function in lower extremities for patients with KOA. These findings can be useful in areas such as self-screening for reduced physical function, self-assessment in digital physiotherapy, and as measurement in research projects.

Exposure to Smartphones and Tablets, Physical Activity and Sleep in Children From 5 to 10 Years Old: A Systematic Review and Meta-Analysis.

To conduct a systematic review and meta-analysis to verify the association between smartphone/tablet exposure and physical activity and sleep in children from 5 to 10years old. Data Source: This study followed the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and included studies that met eligibility criteria based on the "PECO" strategy: participants (children from 5 to 10years old), exposure (smartphone and tablet use), and outcome (physical activity and sleep). The inclusion criteria were observational studies published in indexed scientific journals and written in Portuguese, English, and Spanish that verified the association of exposure to smartphones/tablets with physical activity and sleep in children aged 5 to 10years of both sexes. Studies were considered eligible only if they met the previous criteria. Data Extraction: The search was conducted in January 2023 on databases from electronic journals without the restriction of the period. To meta-analyze were extracted and grouped using models of fixed and random effects, the coefficients Odds Ratio (OR), Beta (β), Standard Error (SE), and Confidence Intervals of 95% (95%CI). Data Synthesis: 2396 potentially relevant papers were identified, and 17 met the inclusion criteria. It can be verified that there was an inverse association between smartphones with physical activity and sleep. Studies indicate that for every additional hour of smartphone and tablet use, sleep can be expected to decrease by an average of 11 minutes (β = - 0.11; 95%CI = -0.13; -0.09). Children using smartphones and tablets were 1.79 times (OR = 1.79; 95%CI = 1.72-1.86) more likely to have shorter sleep duration and 1.53 times (OR = 1.53; 95%CI = 1.41-1.65) more likely to have worse sleep quality. Children with shorter smartphone and tablet usage were 1.19 times more likely to be active (OR = 1.19; 95% CI = 1.03-1.37). Children of 5 to 10years who are more often exposed to smartphones and tablets are prone to have worse quality and quantity of sleep, as well as less practice of physical activity. Health promotion actions can be encouraged based on the results, aiming to reduce the use time of these devices and improve children's health and quality of life.

High precision low jitter pulse generator implemented with FPGA transceiver

Precisely controlled digital pulse plays a crucial role in various cutting-edge testing applications. This paper introduces an innovative approach to digital pulse generation utilizing an FPGA transceiver, with the AMD evaluation board ZCU102 serving as a demonstrative platform. In the normal mode, the transmitter generates a serial bit stream, enabling the generation of single or repeated pulses with configurable width and period. In the high precision mode, an internal dedicated phase interpolator enhances the configuration step size for width and period by up to 64 times, providing finer phase shift capability. The demonstration system operates at a line rate of 10Gbps, achieving a resolution of 100ps in normal mode and 1.5625ps in high precision mode. This paper details the design methodology, presents test results for pulse width uncertainty and non-linearity, and showcases achieved period jitter and time interval error jitter as small as around 1ps.

Compensating trigger jitter and time interval error measurement for digital sampling oscilloscopes with hardware design on FPGA

This paper presents the creation and realization of a precision time base (PTB) for mitigating jitter and measuring time interval error (TIE) in digital sampling oscilloscopes (DSO), achieved through the utilization of a field programmable gate array (FPGA). Our proposed method focuses on mitigating jitter through PTB, which involves the sampling of two reference channels having a phase shift of approximately 90∘ (at quadrature) for an accurate timebase correction. Through extensive experimentation and analysis, we were able to achieve a reduction in root mean square (RMS) jitter, minimizing it to around 260 fs. This outcome demonstrates the effectiveness of our approach in enhancing the accuracy and reliability of DSO measurements. Expanding on the application of our previously proposed technique, we demonstrate that the same sampling error correction approach can be utilized for TIE measurement. By sampling a large amount of data at either the rising or falling edge of a sinusoidal signal, TIE can be accurately evaluated. This extension enhances the versatility of our FPGA-based implementation by enabling comprehensive TIE analysis alongside PTB compensation in DSOs.