Estimation Method Research Articles

A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure.

In this article we review the history of key epidemiological studies of populations exposed to ionizing radiation. We highlight historical and recent findings regarding radiation-associated risks for incidence and mortality of cancer and non-cancer outcomes with emphasis on study design and methods of exposure assessment and dose estimation along with brief consideration of sources of bias for a few of the more important studies. We examine the findings from the epidemiological studies of the Japanese atomic bomb survivors, persons exposed to radiation for diagnostic or therapeutic purposes, those exposed to environmental sources including Chornobyl and other reactor accidents, and occupationally exposed cohorts. We also summarize results of pooled studies. These summaries are necessarily brief, but we provide references to more detailed information. We discuss possible future directions of study, to include assessment of susceptible populations, and possible new populations, data sources, study designs and methods of analysis.

Benchmarking South African water utilities using three efficiency analysis methods

ABSTRACT This paper compares efficiency scores from the data envelopment analysis (DEA), stochastic frontier analysis (SFA), and stochastic non-parametric envelopment of data (StoNED) methods in the context of regulating South African water utilities. We estimate cost efficiency based on cross-sectional data from 102 South African water utilities in the period 2013/14. We compare the impact of methodological choices on the efficiency scores. For StoNED, we compare scores from two different estimation techniques (method of moments and pseudolikelihood). We also use the naïve method of averaging (NMA) and compare its results to those generated by the other methods. Results show that the choice of method has an impact on efficiency estimates and monetary cost reduction targets. When water sector regulators in the developing countries intend to benchmark utilities based on each of these methods, heterogeneity and the operating environments of utilities should be considered before deciding on the estimation method.

The water weakening effect on the progressive slope failure under excavation and rainfall conditions

The water weakening effect refers to the gradual deterioration of soil mechanical properties under long-term saturation. This paper analyzed the impact of water weakening on slope stability under alternating excavation and rainfall. The field investigation speculated shale hydration as the cause of overall slope instability. The mechanical parameters of hydrated shale were determined by the parameter inversion and empirical estimation methods. The simulations were used to restore the process of parameter weakening and slope failure, and confirmed the fact that hydration causes landslide. Furthermore, the failure mechanism of slope and support structure under hydration were investigated. The results show that the mechanical parameters of the slope decreased linearly, whereas the plastic strain–time and total displacement-maximum shear stress curves of the hydrated shale exhibited three stages: slow initial growth, rapid growth in the middle term, and rapid increase in the later period; the rate of slope deformation and the factor of safety reduction also gradually increase over time; under middle stage of hydration, the middle to rear of shale were extruded, while the front first underwent tensile shear deformation, forming a plastic zone of at rear and front excavated slope; In the late stage, hydrated shale quickly reaches its yield limit (maximum shear stress of 270 kPa), the middle and rear shale is damaged and compresses the front, causing it to transform from tensile shear to compressive shear failure. The plastic zone in the rear extends forward and connects with the front ones, forming an overall landslide. Besides, preventing deep landslides caused by hydration through waterproofing, drainage, protection, and support for excavated slopes has proven difficult. Therefore, it is necessary to redesign treatment schemes based on the characteristics of stress, strain, and seepage.

Associations between dietary potassium intake and urinary potassium excretion: a protocol for systematic review and meta-analysis

BackgroundWhile numerous studies have reported associations between low dietary potassium intake and adverse clinical outcomes, methods to estimate potassium intake, mainly self-reported dietary measures and urinary potassium excretion, entail certain limitations. Self-reported measures are subject to underreporting and overreporting. Urinary potassium excretion is affected by multiple factors including renal function. Revealing the degree of bias inherent in these measures would help accurately assess potassium intake and its association with disease risk. We aim to summarize evidence on the strength of the associations between potassium intake estimated from 24-h urinary potassium excretion and potassium intake estimated from self-reported dietary measures or objective quantification methods in populations with different kidney function levels and age groups. We also aim to identify factors that affect the association strength.MethodsWe will search for potentially eligible studies that examined associations between self-reported potassium intake, 24-h urinary potassium excretion, and objectively quantified potassium intake, using MEDLINE (PubMed), Embase, Web of Science, and Scopus. Studies on children, adolescents, adults, and the elderly are eligible. Studies of patients on dialysis will be excluded. Collective study results, including a meta-analysis, will be synthesized if an adequate number of studies examining similar dietary potassium intake estimation methods are found. Analyses will be performed separately according to age groups and renal function. For the meta-analysis, fixed-effects or random-effect models will be employed depending on the degree of study heterogeneity to combine across studies the correlation coefficient, ratio, or standardized mean difference for potassium intake, comparing dietary potassium intake based on self-reported or objectively quantified methods and intake based on 24-h urinary potassium excretion. The degree of heterogeneity among included studies will be examined by calculating I2 statistics. To investigate sources of study heterogeneity, random-effects meta-regression analyses will be performed.DiscussionRevealing the strength of the association between dietary and urinary measures in populations with different levels of kidney function and age groups will enhance researchers’ and clinicians’ ability to interpret studies that utilize these measures and help establish a more solid evidence base for the role of potassium intake in changing chronic disease risk. Identifying factors that modify the associations between these measures may aid in developing predictive models to estimate actual potassium intake.Systematic review registrationPROSPERO CRD42022357847.

Institutional, economic and cultural safeguards against corruption: a panel study of OECD countries

Purpose The purpose of this paper is to ascertain whether the combination of anti-corruption measures, with simultaneous impact on the institutional, economic and cultural dimensions, is associated with lower levels of corruption. Design/methodology/approach The authors have developed a research that seeks to find statistical evidence of the relationship between levels of corruption and combinations of anti-corruption measures. To test the hypotheses, the authors performed panel regression analysis using different panel estimation methods, namely, fixed effects (FE) models. Findings The results do not fully support the idea that the combination of anti-corruption measures with simultaneous impact on the institutional, economic and cultural dimensions has more impact on corruption levels than other measures taken singly, but rather that the most determining factor in preventing corruption lies in government effectiveness. Originality/value The relevance of this study lies in the shortcomings of this type of research applied to anti-corruption measures. Moreover, the development of panel data analysis makes it possible to approach the phenomenon from a double perspective, over time and in comparative terms between different countries, incorporating non-immediate effects and possible interactive effects between different anti-corruption measures.

Method for DOA Estimation of Coprime Arrays Through Joint Auxiliary Arrays with Atomic Norm Minimization in the Presence of Gain-Phase Errors

Method for DOA Estimation of Coprime Arrays Through Joint Auxiliary Arrays with Atomic Norm Minimization in the Presence of Gain-Phase Errors

Do firms with environmental, social, and governance reputational risk take into account board gender diversity? An analysis on a global scale

AbstractObjectiveThis article studies whether a firm's environmental, social, and governance (ESG) reputational risk influences board gender diversity. Besides, we are also interested in the moderating role of gender equality and country development level toward the relationship between reputational risk and board gender diversity.MethodUsing a comprehensive sample of firms from 52 countries between 2007 and 2019, we employ multiple regression with fixed effects. Our findings remain robust when using alternative measures of variables and addressing endogeneity concerns by employing a two‐stage systems generalized method of moments estimation and an instrumental variable approach.ResultsCompanies with high levels of ESG reputational risk tend to add more women directors to resolve these problems. Furthermore, the empirical results point out that the positive impact of ESG reputational risk on board gender diversity is more pronounced for countries with a higher degree of gender equality or developed countries.ConclusionsOverall, our study is the first international study to link ESG reputational risk via media channels to board gender diversity.

Real-Time Analysis of Facial Expressions for Mood Estimation

This paper proposes a model-based method for real-time automatic mood estimation in video sequences. The approach is customized by learning the person’s specific facial parameters, which are transformed into facial Action Units (AUs). A model mapping for mood representation is used to describe moods in terms of the PAD space: Pleasure, Arousal, and Dominance. From the intersection of these dimensions, eight octants represent fundamental mood categories. In the experimental evaluation, a stimulus video randomly selected from a set prepared to elicit different moods was played to participants, while the participant’s facial expressions were recorded. From the experiment, Dominance is the dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure–Arousal (PA) plane, “Exalted”, “Calm”, “Anxious” and “Bored”, were defined, with two more categories for the “Positive” and “Negative” signs of the Pleasure (P) dimension. Results showed a 73% of coincidence in the PA categorization and a 94% in the P dimension, demonstrating that facial expressions can be used to estimate moods, within these defined categories, and provide cues for assessing users’ subjective states in real-world applications.

Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient

Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradient on double-dot singlet-triplet qubits, we present two adaptive Bayesian schemes to estimate the magnitude of the gradient by a series of free induction decay experiments. We concentrate on reducing the computational overhead, with a real-time implementation of the schemes in mind. We show how it is possible to achieve a significant improvement of estimation accuracy compared to more traditional estimation methods. We include an analysis of the effects of dephasing and the drift of the gradient itself.

Least Squares Estimation of Multifactor Uncertain Differential Equations with Applications to the Stock Market

Multifactor uncertain differential equations are powerful tools for studying dynamic systems under multi-source noise. A key challenge in this study is how to accurately estimate unknown parameters based on the framework of uncertainty theory in multi-source noise environments. To address this core problem, this paper innovatively proposes a least-squares estimation method. The essence of this method lies in constructing statistical invariants with a symmetric uncertainty distribution based on observational data and determining specific parameters by minimizing the distance between the population distribution and the empirical distribution of the statistical invariant. Additionally, two numerical examples are provided to help readers better understand the practical operation and effectiveness of this method. In addition, we also provide a case study of JD.com’s stock prices to illustrate the advantages of the method proposed in this paper, which not only provides a new idea and method for addressing the problem of dynamic system parameter estimation but also provides a new perspective and tool for research and application in related fields.

Crime against women in India: district-level risk estimation using the small area estimation approach

BackgroundThe global prevalence of crimes against women has made it an enduring public health challenge that has persisted over time. The achievement of the 2030 Sustainable Development Goal (SDG) is intricately tied to the actions taken to prevent these crimes as their repercussions directly affect progress across various SDGs. This study aimed to provide a comprehensive examination of the prevalence of crimes against women across districts and states in India, analyzing changes from 2020 to 2022, and subsequently identifying associated factors.MethodsThe study is an ecological analysis conducted across all districts of India using the data on crimes against women for the period 2020 and 2022 obtained from the National Crime Records Bureau (NCRB) of India. A small area estimation method was used to obtain district-level relative risks of crime against women for both periods. Hotspot analysis was carried out to identify the current hotspots and coldspots. Further spatial regression was used to identify the factors associated with crimes against women in the year 2022.ResultsThe results indicated a rise in the reported crime against women cases between 2020 and 2022. The rate of crimes against women at the national level was found to be 57 in the year 2020, whereas, in 2022, it increased to 67. The highest crime rate in the year 2022 was found to be 145 in Delhi, while Nagaland had the lowest crime rate of 5. The relative risk of crime against women varied from 0.046 to 4.68 in 2020, while in 2022, it spanned from 0.02 to 6.10. Significant hotspots were found in parts of Rajasthan, Madhya Pradesh, Haryana, Telangana, and Odisha. The results of the spatial error regression model showed that the sex ratio and the population density of the district have significant associations with the occurrence of crimes against women.ConclusionThe rise in the incidence of crime against women emphasizes the importance of tackling the spatial inequality in relative risk across Indian districts. By thoughtfully addressing this variation and conducting targeted studies in high-risk areas, we can enhance our understanding of the obstacles to implementing effective measures against violence targeting women.

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Core functions, visitor friendliness and digitalisation: a comparative analysis of corporate museums’ performance

AbstractWe exploit a rich dataset on Italian museums to investigate whether corporate museums’ service provision is quantitatively different from the provision by the rest of private museums and by the three different types of Italian publicly owned museums (whose classification is by organisational mode: traditional, autonomous, and outsourced). We consider service provision in the dimensions of core museum functions (research, collection management, dissemination) and visitor friendliness, and we also focus on digital services. We use count data models estimation methods and include controls referring to museums’ characteristics and contextual factors to account for possible confounding effects. Our analysis reveals that corporate museums do not provide more core services than other museums, as expected given the public good component of this category of services. Contrary to expectations, we find that corporate museums are not among the museums providing the largest number of services enhancing visitor friendliness. Finally, corporate museums provide more digital services than traditional public museums and private museums owned by churches, and not less than other museum types. We argue that this latter evidence may come from their interaction with the parent firm. Corporate museums’ higher levels of digitalisation may be seen as the effect of a knowledge spillover between the more profit-oriented business world and the non-profit cultural sector.

Development and Validation RP-HPLC Method for Estimation of Antidiabetic Drugs in Pharmaceutical Dosage Form

This study details the development and validation of an RP-HPLC method for estimating Canagliflozin and Metformin in pharmaceutical dosage forms. Canagliflozin, an SGLT2 inhibitor, and Metformin, an antihyperglycemic agent, were obtained from Arti Drug Pvt. Ltd. Marketed formulation Invokamet was sourced locally. Chromatographic separation was achieved on a C18 column (250 x 4.6 mm, 5 µm) using a mobile phase of Methanol: Water (80:20) with 0.1% ortho phosphoric acid at a 1.0 ml/min flow rate and detection at 254 nm. The retention times for Canagliflozin and Metformin were 8.183 min and 3.633 min, respectively. Method validation parameters included system suitability, accuracy, precision, and robustness. System suitability tests confirmed adequate resolution and repeatability. Accuracy was evaluated through recovery studies at 80%, 100%, and 120% concentration levels, yielding satisfactory results. Precision was demonstrated with intra-day and inter-day % RSD values below 2. Robustness was assessed by varying mobile phase composition and flow rate, showing consistent retention times and tailing factors. The method proved selective, accurate, precise, and robust, with a short run time, facilitating rapid quantification of samples. It is suitable for routine quality control analysis of Canagliflozin and Metformin in pharmaceutical formulations

Remaining Useful Life Prediction Method Based on Stacked Autoencoder and Generalized Wiener Process for Degrading Bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

Causal inference with hidden mediators

Summary Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends classical causal mediation analysis methods for identifying natural direct and indirect effects under no unmeasured confounding to a setting where the mediator of interest is hidden, but proxies of it are available. (ii) We establish a hidden front-door criterion, which extends the classical front-door criterion to allow for hidden mediators for which proxies are available. (iii) We show that the identification of a certain causal effect called population intervention indirect effect remains possible with hidden mediators in settings where challenges in (i) and (ii) might co-exist. We view (i)-(iii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always measured with error and thus, the most one can hope for in practice is that the measurements are at best proxies of mediating mechanisms. We propose identification approaches for the parameters of interest in our considered models. For the estimation aspect, we propose an influence function-based estimation method and provide an analysis for the robustness of the estimators.

A Markov‐switching spatio‐temporal ARCH model

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime‐switching models that have abrupt regime‐switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio‐temporal log‐ARCH model. In this article, we discuss the Markov‐switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices' data that were presumably affected by the 2015–2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one‐regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

Research on Leak Detection of Low-Pressure Gas Pipelines in Buildings Based on Improved Variational Mode Decomposition and Robust Kalman Filtering

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%.

Drone Flight Detection at an Entrance to a Beehive Based on Audio Signals

Spotting a significant number of drones flying near the entrance of a beehive during late Spring could indicate the occurrence of swarming mood, as the the surge in drone presence is related to an overcrowded hive. Swarming refers to a natural reproductive process witnessed in honey bees, wherein half of the bee colony departs from their hive alongside the aging queen. In the paper, we propose an early swarming detection mechanism that relies on the behavior of the drones. The proposed method is based on audio signals registered in a close proximity to the beehive entrance. A comparative study was performed to find the most effective preprocessing method for the audio signals for the detection problem. We have compared the results for three different power spectrum density coefficients estimation methods, which are used as an input of an autoencoder neural network to discriminate drones from worker bees. Through simulations employing real-life signals, it has been demonstrated that drone detection based solely on audio signals is indeed feasible. The attained level of detection accuracy enables the creation of an efficient alarm system for beekeepers.

Effectiveness of traditional herbal Kampo medicine Goreisan on chronic subdural hematoma recurrence: a meta-analysis

Objectives: Our objective was to compare the effectiveness of Traditional Chinese herbal Kampo medicine Goreisan in improving recurrence in patients with chronic subdural hematoma (CSDH).Methods: Eligible randomized controlled trials prospective trials, and retrospective cohort studies were systematically identified through searches of PubMed, Cochrane Library, and CNKI from inception to March 2024. Following the application of predetermined inclusion and exclusion criteria to screen the available studies, main outcome measures were rigorously extracted. RevMan v5.4 software was utilized to evaluate the overall recurrence rate, employing a random-effects model to calculate pooled odds ratios with the Mantel-Haenszel estimation method. Inter-study heterogeneity was assessed using the Cochran Q (Chi-square) test and I2 statistics. Funnel plots were used to evaluate publication bias.Results: Among the 48 articles initially screened for citation, eight were ultimately selected for inclusion in the study. The results of our network meta-analysis indicate that patients with newly diagnosed Chronic subdural hematoma experienced a significantly reduced recurrence rate when treated with Goreisan compared to standard neurosurgical treatment (OR: 0.72; 95% CI 0.61–0.86; p = 0.00003). There was no statistically significant difference in the incidence rates of complications, including general fatigue, allergic reactions, hepatic dysfunction, and interstitial pneumonia (OR: 7.21; 95% CI 0.37–141.29; p = 0.19).Conclusion: Traditional medicine Goreisan was effective in reducing CDSH recurrence rates. For clinical treatment, it provides a high level of evidence-based medicine. It is also necessary to conduct multicenter randomized controlled trials with dose adjustments to determine whether Goreisan interventions improve neurological function or prognosis.