Estimation Techniques Research Articles

A high-order time-delay difference estimation method for signal enhancement in the distorted towed hydrophone array.

In passive sonar systems, deviations from an ideal linear configuration can significantly impair the beamforming performance of towed hydrophone arrays. This paper presents a method aimed at improving the underwater acoustic signals in the presence of array distortion. The method is centered on a high-order time-delay difference estimation technique utilizing time-frequency autofocus. Initially, a detailed signal model is established that captures the distinctive characteristics of distorted arrays. Subsequently, an algorithm is introduced for high-order time-delay difference estimation to enhance signal fidelity by leveraging phase information within narrowband components originating from incidental acoustic sources. Additionally, a quality metric to evaluate these components is introduced, facilitating the practical implementation of the method. The effectiveness of the proposed approach is validated through both simulation and experimental results, demonstrating its superiority over existing techniques. Importantly, this method does not require prior knowledge of the distortion pattern, making it adaptable to various non-linear array configurations.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information

Integrated hydrological modeling is gaining popularity due to its mechanistic representation of the surface and subsurface processes. However, estimating the parameters of such process-based models can be computationally expensive if careful consideration is not given to the length of streamflow observations used during model calibration. Here we evaluate the influence of the calibration period, the role of streamflow information content, and the gage location in parameter learning and calibration of a fully integrated hydrological model, the Advanced Terrestrial Simulator (ATS). We conducted the study at the Upper Neversink River Watershed within the Delaware River Basin, where streamflow observations are available at 11 gages with varying record lengths. We leveraged a recently proposed knowledge-informed deep learning technique for parameter estimation. To assess the impact of observation period and gage location, model parameters were learned on scenarios using different chunks of streamflow observations, including (1) using only one year or consecutive multiple years of streamflow observations at the watershed outlet and (2) using one shared year of observations at each of the sub-catchment gages, with the period from 1991-10-01 through 1999-09-30 as the overall calibration period. Using the estimated parameters, ATS was rerun for each scenario and evaluated on a subsequent period from 1999-10-01 through 2002-09-30. Results show that the basin outlet discharge prediction is mostly improved when using at least four years of observations for parameter estimation. Further, the performance of the calibrated ATS run correlates with the information content of the observed streamflows, suggesting that the information-theoretic metrics could be indicators for selecting the observation period for parameter estimation. Finally, we find that observations from an informative gage can be used in learning parameters to predict the streamflow at a nearby gage, which would potentially lower the computational expense by reducing the watershed domain used in calibration. Our success underscores the potential of using information theory to achieve robust model parameter estimation on a reduced computational budget.

Power modified XLindley distribution: Statistical properties and applications

This research introduces a novel two-parameter distribution, the power-modified XLindley distribution, developed through the application of power transformation techniques to the existing modified XLindley distribution. This new distribution enhances flexibility and adaptability in statistical modeling. We conduct a thorough examination of its statistical properties, exploring its potential to improve data fitting and modeling accuracy. To assess the effectiveness of the model, we employ multiple estimation techniques and evaluate their performance through extensive simulation experiments. Our findings indicate that the maximum product of the spacings method is particularly effective for parameter estimation. To demonstrate the practical utility of the proposed model, we apply it to two real-world datasets: one related to flood data and the other to reliability engineering. The results underscore the distribution’s superior ability to capture the characteristics of these datasets compared to existing models, highlighting its significance for applications in natural disaster analysis and reliability studies.

A Multi-Objective Direction of Arrival Estimation Technique MinimizingEnergy Consumption in Wireless Sensor Network

Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.

Corporate Governance Reforms and the Quality of Financial Information of Listed Non-Financial Services Firms in Nigeria

The increasing demand for high quality financial information by the relevant users of financial report has become a serious concern and inevitable on the management of listed non-financial services firms in Nigeria. The primary objective of this study is to examine the nexus between corporate governance reforms and the quality financial information of listed non-financial services firms in Nigeria. Using an ex-post facto as the research design for the study, data was obtained from the secondary sources through the extraction method from the annual financial reports and accounts of the listed firms for 276 firm-year observations, from 2018 to 2023 period. Out of the seventy-five (75) listed non-financial services firms in Nigerian Exchange Group as at 31st December, 2018, a sample size of fourty-six (46) listed firms were used with aid of purposive filter sampling technique. The Feasible Generalized Least Square (FGLS) regression was used as the model estimation technique for the study. Based on the regression analysis, it was found that board size, assurance services and transparency have positive significant impact on financial information quality of listed non-financial services firms in Nigeria. However, sustainability was statistically found to have insignificant impact on quality of financial information of listed non-financial services firms in Nigeria. Therefore, it is recommended that the board of directors of listed non-financial services firms in Nigeria ensure that more members are nominated to participate in the board’s decision. This is because of the fact that larger board sizes are more likely to create avenue for cross-fertilization of ideas thereby, making decisions that can safeguard, uphold and enhance the quality of the financial information of the listed non-financial services firms in Nigeria. The results also underscore the relevance of corporate governance codes and their impacts on FIQ and transparent disclosure. This study offers policy implications to regulatory agencies and standard-setters in their oversight function over the quality of financial information disclosed in annual reports.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Development of functionality principles for the automated data transmission system through wireless communication channels to ensure information protection

The development of data transmission systems based on wireless radio communication channels allowed the construction of fundamentally new networks – mesh networks, which are used not only in smart technologies, but are the basis for the construction of cyber-physical and socio-cyber-physical systems (objects of critical infrastructure). The object is the process of ensuring reliable and secure data transmission based on the use of wireless radio communication channels. A mathematical model of information resources protection system functioning is proposed to ensure the signs of immunity and security of the automated data transmission system. To identify threats, a unified classifier and flow state estimation technique are used, which take into account the hybridity and synergy of targeted (mixed) attacks on communication channels. The critical points of the infrastructure elements, as well as the information that circulates and/or is stored, are determined. The assessment of compliance with the regulators’ requirements, both international and state regulatory acts, and the presence and ability of the security system elements to ensure the required level of infrastructure elements protection is taken into account. The proposed approach allows to determine: coefficients of information and internal availability of a wireless radio communication channel, the vector potential of the lagging magnetic field as a result of data transmission work. When evaluating the coefficient of a wireless radio communication channel internal availability, it is proposed to take into account coherent reception of the signal. At the same time, the immunity factor of the wireless radio communication channel is much higher than 1, which provides sufficient protection of information. A technical solution is proposed that will allow the level of confidentiality, integrity, authenticity and reliability of a wireless radio communication channel to approach 100 %The development of data transmission systems based on wireless radio communication channels allowed the construction of fundamentally new networks – mesh networks, which are used not only in smart technologies, but are the basis for the construction of cyber-physical and socio-cyber-physical systems (objects of critical infrastructure). The object is the process of ensuring reliable and secure data transmission based on the use of wireless radio communication channels. A mathematical model of information resources protection system functioning is proposed to ensure the signs of immunity and security of the automated data transmission system. To identify threats, a unified classifier and flow state estimation technique are used, which take into account the hybridity and synergy of targeted (mixed) attacks on communication channels. The critical points of the infrastructure elements, as well as the information that circulates and/or is stored, are determined. The assessment of compliance with the regulators’ requirements, both international and state regulatory acts, and the presence and ability of the security system elements to ensure the required level of infrastructure elements protection is taken into account. The proposed approach allows to determine: coefficients of information and internal availability of a wireless radio communication channel, the vector potential of the lagging magnetic field as a result of data transmission work. When evaluating the coefficient of a wireless radio communication channel internal availability, it is proposed to take into account coherent reception of the signal. At the same time, the immunity factor of the wireless radio communication channel is much higher than 1, which provides sufficient protection of information. A technical solution is proposed that will allow the level of confidentiality, integrity, authenticity and reliability of a wireless radio communication channel to approach 100 %

Sliding mode observers for set-valued Lur’e systems with uncertainties beyond observational range

In this paper, we introduce a new sliding mode observer for Lur’e set-valued dynamical systems, particularly addressing challenges posed by uncertainties not within the standard range of observation. Traditionally, most ofLuenberger-like observers and sliding mode observer have been designed only for uncertainties in the range of observation. Central to our approach is the treatment of the uncertainty term which we decompose into two components: the first part in the observation subspace and the second part in its complemented subspace. We establish that when the second part converges to zero, an exact sliding mode observer for the system can be obtained In scenarios where this convergence does not occur, our methodology allows for the estimation of errors between the actual state and the observer state. This leads to a practical interval estimation technique, valuable in situations where part of the uncertainty lies outside the observable range. Finally, we show that our observer is also a T-observer as well as a strong H∞ observer.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

The impact of financial inclusion on rural–urban households welfare inequality in Ghana: A decomposition analysis

AbstractThis article examined the impact of financial inclusion on rural–urban households' welfare inequality in Ghana using nationally representative survey data sourced from the seventh round of the Ghana Living Standard Survey. The article used a multidimensional financial exclusion index and the real per adult equivalent measures of total annual household consumption expenditure as measures of financial inclusion and welfare, respectively. The article employed the instrumental variable estimation technique to account for the issues of endogeneity and sample selectivity bias associated with measuring financial inclusion. The estimated results from recentered influenced function‐Ordinary Least Squares of the unconditional quantile regression and the Oaxaca‐Blinder techniques show significant rural–urban welfare gaps in favor of urban households at the mean and across the selected quantiles. The study also found that differences in financial inclusion between rural and urban households contribute significantly to the welfare gaps, while the economic returns to financial inclusion reduce the welfare gaps both at the mean and the selected quantiles. The study recommends that policies aimed at improving financial inclusion as well as strengthening financial institutions in rural areas be put in place to help reduce the rural–urban welfare inequality in Ghana.

Revisiting the nexus between governance and poverty in Sub‐Saharan Africa

AbstractThe aim of this study is to investigate the influence of different components of governance on poverty. Hence, the study investigates the direct impact of four pillars of governance and 16 subindicators of governance on poverty in Sub‐Saharan Africa (SSA). This is unlike most previous studies that use a composite index of governance which does not have a bearing on specific policy variables. A panel data set of 41 SSA countries over a period from 2012 to 2022 is analyzed using the System GMM (Generalised Method of Moment) estimation technique. The findings show that improvement in the four pillars of governance (security and rule of law; participation, rights and inclusion; foundation for economic opportunity; and human development) would yield significant reduction of poverty in SSA. However, not all the sub‐indicators have a direct impact on poverty, those that show significant effects are: (a) rule of law and justice, (b) accountability and transparency, (c) anticorruption, (d) public administration, (e) infrastructure, (f) rural development, (g) social welfare, and (h) sustainable environment. All the significant indicators have bearing on the creation and protection of an enabling environment and opportunity for productive activities. Hence, it is recommended that policymakers should undertake judicial system reforms including reforms of the police services. Civil service reform is also recommended, while the business of government should be carried out transparently with zero tolerance for corruption.

Simultaneous Multiparameter Mapping of the Liver in a Single Breath-Hold or Respiratory-Triggered Acquisition Using Multi-Inversion Spin and Gradient Echo MRI.

Quantitative parametric mapping is an increasingly important tool for noninvasive assessment of chronic liver disease. Conventional parametric mapping techniques require multiple breath-held acquisitions and provide limited anatomic coverage. To investigate a multi-inversion spin and gradient echo (MI-SAGE) technique for simultaneous estimation of T1, T2, and T2* of the liver. Prospective. Sixteen research participants, both adult and pediatric (age 17.5 ± 4.6 years, eight male), with and without known liver disease (seven asymptomatic healthy controls, two fibrotic liver disease, five steatotic liver disease, and two fibrotic and steatotic liver disease). 1.5 T, single breath-hold and respiratory triggered MI-SAGE, breath-hold modified Look-Locker inversion recovery (MOLLI, T1 mapping), breath-hold gradient and spin echo (GRASE, T2 mapping), and multiple gradient echo (mGRE, T2* mapping) sequences. Agreement between hepatic T1, T2, and T2* estimated using MI-SAGE and conventional parametric mapping sequences was evaluated. Repeatability and reproducibility of MI-SAGE were evaluated using a same-session acquisition and second-session acquisition. Bland-Altman analysis with bias assessment and limits of agreement (LOA) and intraclass correlation coefficients (ICC). Hepatic T1, T2, and T2* estimates obtained using the MI-SAGE technique had mean biases of 72 (LOA: -22 to 166) msec, -3 (LOA: -10 to 5) msec, and 2 (LOA: -5 to 8) msec (single breath-hold) and 36 (LOA: -43 to 120) msec, -3 (LOA: -17 to 11) msec, and 4 (LOA: -3 to 11) msec (respiratory triggered), respectively, in comparison to conventional acquisitions using MOLLI, GRASE, and mGRE. All MI-SAGE estimates had strong repeatability and reproducibility (ICC > 0.72). Hepatic T1, T2, and T2* estimates obtained using an MI-SAGE technique were comparable to conventional methods, although there was a 12%/6% for breath-hold/respiratory triggered underestimation of T1 values compared to MOLLI. Both respiratory triggered and breath-hold MI-SAGE parameter maps demonstrated strong repeatability and reproducibility. 1 TECHNICAL EFFICACY: Stage 2.

Application of data analysis techniques for characterization and estimation in electrical substations

With the continued growth of smart grids in electrical systems around the world, large amounts of data are continuously being generated and new opportunities are emerging to use this data in a wide variety of applications. In particular, the analysis of data from distribution systems (such as electrical substations) can lead to improvements in real-time monitoring and load forecasting. This paper presents a methodology for substation data analysis based on the application of a series of data analysis methods aimed at three main objectives: the characterization of demand by identifying different types of consumption, the statistical analysis of the distribution of consumption, and the identification of anomalous behavior. The methodology is tested on a data set of hourly measurements from substations located in various geographical regions of Colombia. The results of this methodology show that the analysis of substations data can effectively detect several common consumption patterns and also isolate anomalous ones, with approximately 4% of the substations being identified as outliers. Therefore, the proposed methodology could be a useful tool for decision-making processes of electricity distributors.

Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis

ABSTRACT Wood density and chemical extractives are important parameters influencing timber quality and utility in wood industry. Several wood properties, particularly mechanical parameters and durability, depend respectively on density and extractive contents. This study aimed to apply the near-infrared (NIR) spectroscopy technique for rapid and accurate estimation of density and extractive contents of Dalbergia latifolia and D. sissoo wood species. The objective was to develop a predictive model for properties, addressing the critical gap in efficient and large-scale assessment techniques for wood industries. Density and extractive contents were determined by conventional and wet chemistry methods. Spectral data of woods were subjected to multivariate analysis to improve the accuracy of calibration models and performance was assessed using coefficient of determination (R²) and root mean square error of prediction. The optimised models exhibited good predictive accuracy with high R²of 0.85 and 0.82 for density and extractive contents respectively. Spectral patterns correlating density and extractive variations identified provided valuable insights into the chemical composition and density relationship in Dalbergia species. The study thus highlights the potential of this non-invasive analytical approach in assessing the quality parameters, which would facilitate sustainable utilisation and contribute to the advancement of woodworking industries.

Intergenerational income mobility and returns to education in Southern Brazil

This study analyzes intergenerational income mobility in Southern Brazil. Previous research has demonstrated that Brazil continues to be one of the least mobile countries in the world; however, the country has experienced strong socioeconomic advances in recent decades that may have affected mobility. The use of data from the 1982 birth cohort in the city of Pelotas, in the extreme south of Brazil, makes this one of the first studies to estimate mobility using direct observations of parental and filial income in Brazil. Using various estimation techniques, the results reveal relatively high income mobility compared with past Brazilian standards. Mobility is higher for nonwhites, extremely similar across genders, and higher among the poor. The findings indicate that increased government investment in education and reduced returns to education in recent decades are possible channels.

A comprehensive analysis of the impact of greenhouse gases, energy usage, institutional quality and globalization on health: A case study of selected developing nations

AbstractThis study inspects the health consequences of greenhouse gases, energy use, institutional quality and globalization for the 23 developing economies. Utilizing panel data from 2000 to 2022, various estimation techniques, including static models (pooled, fixed and random), the two‐step systemGeneralized Methods of Moments (GMM and Panel Auto Regressive Distributive Model (PARDL), are applied to investigate the short‐ and long‐term effects of greenhouse gases, energy use, institutional quality and globalization on health. Panel causality tests, Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) are employed to ensure robustness and causal linkage among variables. The results confirmed a positive association between health and its lag value. Institutional quality negatively influences health expenditure, while greenhouse gases, energy use and globalization exhibit an increasing effect on health expenditure. Life expectancy, institutional quality, energy use and globalization have favourable effects, while greenhouse gases negatively affect health outcomes. In Model 1, energy usage and health expenditures have two‐way causality, and green house gases and energy use in Model 2 have a bi‐directional causal linkage. All variables exhibit unidirectional causality, except globalization and institutional quality, as shown by the Dumitrescu and Hurlin causality test. The study complements the literature by stating that institutional quality is the key instrument for managing the negative consequences of the recent increase in globalization and energy use and their detrimental impact on health in developing economies.

A review of unsupervised k-value selection techniques in clustering algorithms

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years.Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared.Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed.Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference.Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques.Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.

Systematic Method Development and Validation of Paclitaxel and Tamoxifen by RP-HPLC for Simultaneous Estimation in Nanoformulations

Background: Paclitaxel (PTX) is well known anticancer drug used for treatment of a wide range of cancers. Tamoxifen (TMX) is a gold-standard drug for the treatment of breast cancer. They show synergistic action against breast cancer. Quantitative analytical methods are still not available for simultaneous quantification of these drugs. So for the first time, we developed and validated a new systematic analytical technique for simultaneous estimation of PTX and TMX in nanoformulations. Objective: Systematic method development and validation of Paclitaxel and Tamoxifen by RP-HPLC for simultaneous estimation in nanoformulations. Methods: PTX and TMX were successfully separated and quantified using RP-HPLC. The detection was done based on the isobastic point of both drugs. Thermo Scientific Company's C18 column, measuring 2.5 cm × 4.5 cm x 5 µm, was utilized for separation. A 50:50 (vol/vol) ratio of ACN: Phosphate buffer solution (pH 3.0) was employed as the mobile phase, with a flow rate of 1 mL/minute. The detection was done at 235nm. The method was developed and validated as per the criteria. The developed method was then evaluated for applicability by detecting both analytes in prepared SLN and LPHNPs. Results: The well-resolved separate peaks of both analytes were found and distinct RT for PTX and TMX. The linear relationship of both the analytes against concentration was found over the concentration range of 0.25-9 µg/mL. The PTX RT was 10.358 minutes and that of TMX was 12.57 minutes. Every criterion for method validation fell within the acceptable range. The method is precise with inter-day and intra-day precision and shows a %RSD of < 2%.