Estimation Method Research Articles

Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar’s coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

Enhanced QSVM with elitist non‐dominated sorting genetic optimisation algorithm for breast cancer diagnosis

AbstractMachine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.

Prospects for the economy and foreign trade of Ethiopia: fore cast estimates based on mathematical modeling

The article presents a forecast assessment of the development of the national economy and foreign trade of Ethiopia until 2030, calculated by the authors on the basis of a mathematical and systematic analysis of macroeconomic processes and methods of modeling these processes usingthe Box-Jenkins model (ARIMA). Estimation method: the exact maximum likelihood method. The topic seems relevant due to Ethiopia's accession to the BRICS alliance from 2024. Therefore, it is important to predict possible trends in Ethiopia's economic and foreign trade development, their significance for the country itself, as well as their impact on the economic dynamics of other BRICS countries and the association as a whole. The study is updated taking into account the continued increase in global volatility and the emergence of new threats, including in Africa. The subject is trends in the economic development of Ethiopia as a member of the BRICS. The purpose of the article is to develop a scientifically based forecast of the development of the economy and foreign trade of Ethiopia in the medium term. The article identifies potential threats to the development of the Ethiopian economy. The statistical basis of the study was mainly data from the United Nations Conference on Trade and Development (UNCTAD). The results of the study can be used to understand the trends of socio-economic and foreign trade development of the country, adjust this development, as well as expand and enrich the theoretical concepts of the world economy. The research and author's calculations are based solely on the current circumstances and current realities, without taking into account any fundamental changes in the politics and economy of the country under study. The author's calculations and conclusions can be useful to economists, entrepreneurs, and researchers as a basis for their further practical and scientific activities.

New Approaches to Extremal Index Estimation

The extremal index is a parameter associated with the extreme value distributions of dependent stationary sequences. Under certain local dependence conditions, exceedances above a specified threshold tend to occur in isolated clusters. The reciprocal of the extremal index can be interpreted as the limiting size of these clusters. Accurately estimating the size of such clusters is crucial for analyzing real data and can significantly influence decision making processes that impact population well being. The paper presents a recent method for the estimation of the extremal index which starts by the estimation of the parameter itself and, only then, to use that estimate in the cluster mean size estimation. The procedure starts with the estimation of a specific proportion by the corresponding relative frequency. Thus, it is very simple, intuitive, it has good statistical properties, and it does not depend on the method used for the mean cluster estimation. The interpretation of the extremal index as a proportion is known, but it has not been used directly as an estimation method. In recent years, various authors have proposed different estimators for the extremal index. This paper applies some of the latest estimation methods for the extremal index to real data and analyses their performance using training and test samples. The results are compared with other well known estimators, for which R packages are available. The results show a better performance of the Proportion estimator, followed by the Gaps estimator, when compared to the other considered index estimators.

Using generalized estimating equations to estimate nonlinear models with spatial data

. We study the estimation of nonlinear models with cross-sectional data using two-step generalized estimating equations within the quasi-maximum likelihood estimation framework. To improve efficiency, we propose a grouped estimator that accounts for potential spatial correlation in the underlying innovations of nonlinear models. Under mild weak dependence assumptions, we provide results on estimation consistency and asymptotic normality. Monte Carlo simulations demonstrate the efficiency gain of our approach compared to various estimation methods. Finally, we apply the proposed approach to examine the role of cultural distance in an extended gravity equation using international trade data from China. Compared to existing methods, our approach yields estimates with smaller standard errors and reinforces the hypothesis that both cultural and geographical distances significantly negatively influence international trade.

The Representative Points of Generalized Alpha Skew-t Distribution and Applications

Assuming the underlying statistical distribution of data is critical in information theory, as it impacts the accuracy and efficiency of communication and the definition of entropy. The real-world data are widely assumed to follow the normal distribution. To better comprehend the skewness of the data, many models more flexible than the normal distribution have been proposed, such as the generalized alpha skew-t (GAST) distribution. This paper studies some properties of the GAST distribution, including the calculation of the moments, and the relationship between the number of peaks and the GAST parameters with some proofs. For complex probability distributions, representative points (RPs) are useful due to the convenience of manipulation, computation and analysis. The relative entropy of two probability distributions could have been a good criterion for the purpose of generating RPs of a specific distribution but is not popularly used due to computational complexity. Hence, this paper only provides three ways to obtain RPs of the GAST distribution, Monte Carlo (MC), quasi-Monte Carlo (QMC), and mean square error (MSE). The three types of RPs are utilized in estimating moments and densities of the GAST distribution with known and unknown parameters. The MSE representative points perform the best among all case studies. For unknown parameter cases, a revised maximum likelihood estimation (MLE) method of parameter estimation is compared with the plain MLE method. It indicates that the revised MLE method is suitable for the GAST distribution having a unimodal or unobvious bimodal pattern. This paper includes two real-data applications in which the GAST model appears adaptable to various types of data.

Model-Centric Integration of Uncertain Expert Knowledge into Importance Sampling-Based Parameter Estimation

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated.

Spectrometric method for simultaneous quantification of nadifloxacin and salicylic acid in a novel topical dosage form

Acne vulgaris is a prevalent skin disorder that affects many individuals. In the search for effective acne therapy, researchers have found that a combination of Nadifloxacin and Salicylic Acid may produce a synergistic effect. To further investigate this, a study was conducted to devise a spectrophotometric method for simultaneously measuring the concentrations of Nadifloxacin and Salicylic Acid in a novel topical formulation. Simultaneous Estimation Method was employed using methanol as the solvent. Nadifloxacin exhibited an absorbance maximum of 291 nm, while Salicylic Acid showed an absorbance maximum of 297 nm in methanol. Linearity experiments were conducted to determine the concentration ranges for both drugs, which were found to be 2–12 ppm for Nadifloxacin and 5–30 ppm for Salicylic Acid. The recovery results of Nadifloxacin ranged from 82 % to 95 %, while those of Salicylic Acid ranged from 97 % to 110 %. These results indicate the accuracy and reliability of the method. This method was further validated in accordance with the guidelines provided by the International Council for Harmonization (ICH) of Technical Requirements for Human Use. The proposed method has demonstrated its applicability for the simultaneous and quantitative assessment of Nadifloxacin and Salicylic Acid in various dosage forms. This development is significant as it provides a reliable and efficient means of measuring the concentrations of these two drugs in acne therapy formulations.

Counting the costs: understanding the extra costs of living with disability in Indonesia to advance inclusive policies within the SDG framework.

The Sustainable Development Goals (SDGs) are a multidimensional framework for monitoring progress on disability inclusion over time and among countries where reliable, disability disaggregated data is available. However, the SDGs alone do not provide insights into the causes of the social and economic disparities that people with disabilities face or offer specific policy solutions to alleviate them. This paper highlights the extra costs of living with disability in Indonesia to advance the country's commitment to further the rights of people with disabilities. It utilizes three primary estimation methods, combining an analyses of national survey data with primary data from interviews and focus group discussions. Findings reveal significant and varying costs based on disability type, severity and life cycle stages. It also highlights the unaffordability of these costs for most individuals with disabilities and their families. Leveraging these estimates, the paper proposes 'disability concessions' aligned with Indonesia's legal framework on disability inclusion, aiming to alleviate financial burdens through discounts across health, education, utilities and transportation. By contributing to methodological approaches in understanding extra costs of living with disability inclusion in emerging country context and promoting discussions on leveraging the results for disability inclusive policymaking, this paper supplements the SDG framework to foster disability inclusion.

Predicting shear strength of corroded RC columns: A probabilistic model with enhanced Gaussian Process Regression

This study proposes a new probabilistic model for predicting the shear strength of corroded reinforced concrete (RC) columns. The probabilistic model addresses limitations of traditional methods by combining mechanical understanding with enhanced Gaussian Process Regression (GPR). A novel mean function for enhanced GPR is developed first based on the shear resistance mechanism of corroded RC columns. Then the hyper-parameters of both the mean function and kernel function for the enhanced GPR are optimized using the maximum likelihood estimation method. This leads to the establishment of the probabilistic model that is based on the enhanced GPR. Finally, the accuracy and effectiveness of the enhanced GPR probabilistic model are validated by comparing it with both traditional mechanical models and machine learning models. The results indicate that the proposed probabilistic model can not only describes the probabilistic characteristics for shear strength of corroded RC columns based on probability density functions, but also provides an efficient calibration method for traditional prediction models based on confidence intervals.

Predicting the most economical option of managing electric vehicle battery at the end of its serviceable life

It is predicted that millions of electric vehicle batteries (EVBs) will retire in the next decade. With the global Electric Vehicles (EVs) market share increasing to over 40 million by 2030, the replacement of unwanted EVBs will become more abundant and may end up in landfills. These discarded EVBs can become a threat to the environment and to human health if they are not treated, disposed of, and recycled appropriately. Numerous end-of-life (EOL) cost estimation methods that have been developed are mainly for evaluating fossil fuel vehicle components. Research within the scope of EOL of EVBs however, is slow in progress, therefore, this work focuses on developing a cost estimation method for predicting EOL costs of EVBs by adopting a circular economy approach. This proposed method is validated through a case study using two Nissan's EV battery packs. The significance of this study may provide valuable information which could, indirectly lead to the redesign of EVBs for better business circular economy and less waste pollution for future EVs. The main results show that the potential economic benefit that generated by reusing/repurposing EOL EVBs is 92% more profitable than recycling and 52% more profitable than remanufacturing. This study concludes that reuse/repurpose becomes the most profitable process when EVBs reach the end of their serviceable life. Remanufacturing method can also be economically viable if it considers multi-cell replacement of an EVB.

A Tracking and Mapping Method for Visually-degraded Environment

Abstract. When exploring uncertain areas, the system usually relies on Simultaneous Localization and Mapping (SLAM) to map the surrounding environment and track the location through the environment. Recent work has shown that the SLAM algorithm can exhibit strong robustness when observing distinctive features. However, some environments, such as tunnels or empty rooms, may lack sufficient features to navigate reliably. This is called environmental degradation, which refers to the difficulties encountered in positioning and mapping in specific scenarios and then lead to poor system performance. We propose an improved algorithm based on Non-Rigid Structure-from-Motion (NRSfM) and Deformable SLAM (DefSLAM). The method proposed in this paper can be used to deal with the degraded environment of visual sparse and repetitive features. In the experiment, the algorithm processes the close-up sequence of the degraded scene, both in the laboratory-controlled experiment and in the real-world sequence, to generate a 3D model of the scene relative to the mobile camera. It has been proved that the algorithm proposed in this paper achieves impressive performance on the dataset and the estimation method is robust to degradation.

Using the Capture–Recapture Technique to Supplement a Point-in-Time Count of Homeless Adults in Kittitas County, Washington

Abstract Capture–recapture (CRC) is a method of population estimation pioneered in biology that allows the surveyor to approximate the proportion of individuals who evaded sampling efforts (i.e., captures) based on repeated samples from the same population. The current national standard for estimating homelessness is a single-day enumeration called the point-in-time (PIT) count. By using a local community action agency’s homeless outreach program and a cold weather shelter as two distinct sampling sources to capture data using CRC methodology, the current study sought to estimate the homeless population in Kittitas County, Washington, and compare CRC with PIT count estimates. Four months of data collection for the two sampling sources yielded an estimate of 115 (95% CI [93, 137]) homeless individuals in the county using CRC methodology. The PIT count conducted in the same year sampled 61 individuals, supporting the idea that cross-sectional enumeration (i.e., PIT counts) underestimate the homeless population and could be supplemented by CRC methods, particularly in rural areas where resources to conduct PIT counts may be limited. Greater accuracy in homelessness enumeration may aid in advocacy efforts, provide baseline data for researching interventions designed to reduce homelessness, and yield statistics to better guide legislative and policy initiatives.

Pedestrian Path Choice in Crowded Indoor Environments

Abstract. Indoor crowds can be regarded as dynamic obstacles and they could impede the movement of pedestrians. For instance, the scope of crowds could significantly impact accurate path choices. There are few discussions from an individual’s perspective to reveal the decision-making process of reasonable path choices. In this paper, we aim to address how an indoor pedestrian would walk in a crowded indoor environment. We propose a method to simulate a pedestrian’s path choices during wayfinding process. It is wrapped in a workflow that can capture near-real-time crowd uncertainty. First, the measurement errors of crowds are alleviated with a Bayesian filter. According to crowd locations at each moment, the method of Kernel Density Estimation (KDE) is applied to a grid model of the building to obtain the uncertainty map of the crowds. Based on a two-level spatial model, the logical path represented by a room sequence can be derived from a ’room-door’ network, while detailed path choices on the ’operational’ level are refined in each room. By considering both crowd uncertainty and time cost, path choices for a user are determined by using the A* algorithm at each time slot. By iterating the optimal path to the next door/the target in the current room, the user location of the next moment can be inferred. Our test initially validated the feasibility of the method on the path choices of a pedestrian. In the future, we plan to further conduct field tests with different users in crowded indoor environments.

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

Abstract In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect replacements for the target outcome. Instead, we supplement the available, albeit limited, observations of the target outcome with abundant observations of surrogate outcomes, without any assumptions beyond unconfounded treatment assignment and missingness and corresponding overlap conditions. To quantify the potential gains, we derive the difference in efficiency bounds on ATE estimation with and without surrogates, both when an overwhelming or comparable number of units have missing outcomes. We develop robust ATE estimation and inference methods that realize these efficiency gains. We empirically demonstrate the gains by studying long-term-earning effects of job training.

Perspective Transform-based Depth Estimation of Monocular Camera for Electrocution Threat Determination of Construction Machinery

Abstract. This paper discusses the application of perspective transform-based monocular camera depth estimation method in monitoring the safety distance between construction machinery and power lines. The traditional distance measurement relies on manual operation, which is inefficient and not precise enough, while the monocular depth estimation method based on deep learning can improve the accuracy, but faces high equipment cost and relies on a large amount of training data. In contrast, the depth estimation method based on perspective transformation proposed in this paper utilizes mathematical and physical principles to establish a mathematical model between the pixel distances of an image and the actual scene distances through perspective projection theory, which simplifies the hardware requirements and reduces the data dependence and improves the computational efficiency and applicability. Through experimental validation under different construction scenes and exposure conditions, the method demonstrates high accuracy and robustness, proving its practicality in construction safety management. This research provides a new technical direction and practical application possibility in the field of intelligent monitoring and 3D reconstruction.

ESG and firm performance: The effects of foreigners on board and export orientation

AbstractThis study investigates whether environmental, social, and governance (ESG) disclosures enhance industry‐adjusted firm performance and whether the presence of foreigners on board and export orientation affect this relationship. The Taiwan dataset from 2013 to 2020 indicates that ESG practices positively affect accounting‐ and market‐based performance (i.e., adjusted Tobin's Q and adjusted MTB ratio). However, ESG has no significant relationship with purely market‐based performance (i.e., adjusted Jensen Alpha and adjusted FF3). The results illustrate a significant joint effect of ESG with foreigners on board and export experience on accounting and market‐based performance. The findings provide deeper insight into the complex relationship between sustainability practices, ownership structures, and international businesses. Our findings remain consistent when using alternative estimation methods to account for endogeneity and selection bias.

State of Charge Estimation for Lithium-Ion Battery Based on Fractional-Order Kreisselmeier-Type Adaptive Observer

For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equivalent circuit model. Firstly, we constructed a fractional-order battery model that includes pseudo-capacitance and determined the values of the circuit elements offline using the least squares method from actual input–output data based on the driving profile of an automobile. Compared to the integer-order battery model, we confirmed that the proposed fractional-order battery model has higher accuracy. Secondly, we constructed a fractional-order Kreisselmeier-type adaptive observer as an observer that performs state estimation and parameter adjustment simultaneously. Applying the general adaptive law to the battery model results in a redundant design with many adjustable parameters, so we proposed an adaptive law that reduces the number of adjustable parameters without compromising the stability of the observer. The effectiveness of the proposed method was verified through numerical simulations. As a result, the high estimation accuracy and convergence of the proposed adaptive law were confirmed.

Rule of Law, Regulatory Quality and investment Growth in Nigeria: An Impact Analysis

This study focus on rule of law, regulatory quality and investment growth in Nigeria employing ordinary least square method of estimation. A time series data spanning from 1997 to 2019. The economic growth was proxied with investment growth, while other variables include rule of law, regulatory quality, consumer price index, premium lending rate, trade openness, financial deepening, investment, gross investment and gross fixed capital formation. The finding of the OLS result shows that there is significant positive relationship between rule of law, regulatory quality and economic growth. Also, the result of the ECM shows that the speed of adjustment of the investment growth in the long run would be accounted for in short run by the magnitude of 27% and 40%. In the robustness check, negative significant relationship exist between rule of law, regulatory quality, investment and gross investment. Findings from granger causality test shows that there is a unidirectional causality between rule of law and economic growth, and also a bidirectional causality between regulatory quality and economic growth. Thus, improvement in the governance and institutional framework in Nigeria is suggested as policy recommendation.

Real-world analysis of the correlation between overall survival and progression-free survival in advanced pancreatic cancer: Results of NAPOLEON-1 and 2 studies.

Fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) improve overall survival (OS) and progression-free survival (PFS) in patients with pancreatic cancer, compared with gemcitabine (GEM). However, whether PFS is a surrogate marker of OS in pancreatic cancer chemotherapy focusing on FOLFIRINOX or GEM plus nab-paclitaxel remains unknown. We aimed to verify whether PFS can be a surrogate marker of OS in prognosis prediction. This was an integrated analysis of the NAPOLEON study and retrospective cohort of the NAPOLEON-2 study-a multicenter observational study conducted in Japan, using real-world data. The primary and secondary endpoints were OS and PFS, respectively. The correlation between OS and PFS in first- and second-line treatments was assessed using Method of Moments estimation. An analysis was performed in patients with confirmed OS at the end of follow-up. The NAPOLEON-2 cohort included only patients who received 5-fluorouracil, leucovorin, and nanoliposomal irinotecan (NFF) as second-line treatment. Among 479 patients, the correlation between PFS and OS from first- and second-line chemotherapies was calculated in 310 and 225 patients, respectively. The R-squared values for the correlation between PFS and OS from first- and second-line chemotherapies were 0.74 and 0.76, respectively. There was no statistically significant difference in first-line treatment between the FOLFIRINOX and gemcitabine plus nab-paclitaxel groups (P=0.92). Therefore, the FOLFIRINOX group may not have shown a stronger correlation than the NFF group. PFS can be a surrogate marker of OS in first- and second-line therapies. Appropriate prognostic estimation might contribute to proper treatment selection.