Classical Error Research Articles

Machine-learning descriptor search on the density of states profile of bimetallic alloy systems and comparison with the d-band center theory.

In this study, the electronic density of states (DOSs) calculated with density functional theory (DFT) were analyzed by the machine-learning techniques. More than 400 pure metal and bimetallic alloy systems were calculated with DFT, and obtained the surface DOSs and the CH3 adsorption energy (Ead). By fitting the Gaussian functions to the DOS, multiple descriptors, such as the Gaussian peak positions, heights, and widths were extracted. Several regression methods, such as the least absolute shrinkage of selection operator (LASSO), random-forest, gradient-boosting, and extra-tree were used to find the relationship between these descriptors and the Ead. The results show that the energy position of the peaks in the d-projected DOS is the most important descriptor, in agreement with the previously known d-band center theory. It was also shown that the peak position in d-projected DOS improves the regression model in addition to the d-band center, since it reduces the regression error.

Privacy Protection Based on Federated Learning

With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

A novel approach for quantitatively distinguishing between anthropogenic and natural effects on paleovegetation.

How to distinguish and quantify past human impacts on vegetation is a significant challenge in paleoecology. Here, we propose a novel method, the error inflection point-discriminant technique. It finds out the inflection points (IPs) of the regression errors of pollen-climate transfer functions using modern pollen spectra from vegetation with different values of the Human Influence Index (HII), which represent the HII threshold values of native/secondary and secondary/artificial vegetation systems. Our results show that the HII value at the native/secondary vegetation IPs is approximately 22 and globally uniform, whereas it varies regionally for the secondary/artificial vegetation IPs. In a case study of the Liangzhu archaeological site in the lower Yangtze River, discriminant functions for pollen spectra from three vegetation types and pollen-climate transfer functions of the native vegetation were established to reconstruct paleovegetation and paleoclimate over the past 6,600 years. Our study demonstrates this method's feasibility for quantitatively distinguishing human impacts on paleovegetation and assessing quantitative paleoclimate reconstructions using pollen data.

Towards Fine-Grained HBOE with Rendered Orientation Set and Laplace Smoothing

Human body orientation estimation (HBOE) aims to estimate the orientation of a human body relative to the camera’s frontal view. Despite recent advancements in this field, there still exist limitations in achieving fine-grained results. We identify certain defects and propose corresponding approaches as follows: 1). Existing datasets suffer from non-uniform angle distributions, resulting in sparse image data for certain angles. To provide comprehensive and high-quality data, we introduce RMOS (Rendered Model Orientation Set), a rendered dataset comprising 150K accurately labeled human instances with a wide range of orientations. 2). Directly using one-hot vector as labels may overlook the similarity between angle labels, leading to poor supervision. And converting the predictions from radians to degrees enlarges the regression error. To enhance supervision, we employ Laplace smoothing to vectorize the label, which contains more information. For fine-grained predictions, we adopt weighted Smooth-L1-loss to align predictions with the smoothed-label, thus providing robust supervision. 3). Previous works ignore body-part-specific information, resulting in coarse predictions. By employing local-window self-attention, our model could utilize different body part information for more precise orientation estimations. We validate the effectiveness of our method in the benchmarks with extensive experiments and show that our method outperforms state-of-the-art. Project is available at: https://github.com/Whalesong-zrs/Towards-Fine-grained-HBOE.

A Panel-corrected Standard Error (PCSE) Framework to Estimate Capital Structure and Banking Performance within the Tunisian Context

The objective of this article is to empirically examine the effect of financing structure on the market share of banks, and their performance in Tunisian banks. To this end, we gathered financial statements of ten commercial banks over the period 2012-2019, and we employed the panel-corrected standard error (PCSE) regression. The empirical results show that the bank capital structure measured by the equity to total assets ratio negatively affects bank performance while the debt to total assets ratio can be a robust and positive driver of bank performance. Through this research, we recommend to Tunisian commercial banks to reduce their operating costs through a better management of their resources, and to find cheaper sources of financing such as increasing equity. We also recommend to Tunisian commercial banks to diversify further their revenues in order to enhance their performance and to generate more profits.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Trajectory Tracking and Disturbance Rejection Performance Analysis of Classical and Advanced Controllers for a SCORBOT Robot

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement.

Design of Bayesian stochastic networks for numerical treatment of Williamson fluid stretching flow model with mixed convected heat generation

In the presented article, a stochastic network paradigm through Bayesian Regularization backpropagation neural network (BRB-NN) is designed to interpret the dynamics of the Williamson fluid stretching flow model with mixed convected heat generation (WF-SFM). The governing nonlinear PDEs system of WF-SFM is reduced to a set of ODEs by incorporating appropriate transformations. The reference datasets for the anticipated BRB-NN approach are created with Adams solver for numerical solutions of WF-SFM by varying material variable W e , buoyancy factor λ , temperature characteristic parameter ε , thermal relaxation variable γ , heat source factor δ , and Prandtl numbers P r . The knacks of artificial intelligence (AI) based BRB-NN procedure is then employed on the generated dataset for WF-SFM. The bias training, biased testing, and validation of BRB-NN are conducted to compute the approximate numerical results for WF-SFM for sundry scenarios, and outputs are in good agreement in reference data that validate the worthy performance of the proposed BRB-NN which is further justified by results of absolute error, mean squared error, error histogram illustrations and regression measures. The viable performance in terms of mean square error (MSE) is achieved at levels ranging from E − 11 to E − 13 , consistently for all scenarios of WF-SFM. The accuracy and the justification of performance are effectively proven by the low level of MSE, to optimal regression metric index as well as distribution of instances error on histograms with negligible magnitude.

An efficient and easy-to-implement recovery-based a posteriori error estimator for isogeometric analysis of the Stokes equation

In this article, we present an efficient recovery-based a posteriori error estimator for adaptive isogeometric analysis (IGA) of the Stokes equation, which is straightforward to implement. The increased regularity of splines in IGA versus Lagrange polynomials used in the classical finite element method plays a significant role for the success of the presented recovery technique. We consider LR B-splines (Johannessen et al., 2014) for the adaptive mesh refinement, and the isogeometric Taylor–Hood element, Sub-Grid element (Buffa et al., 2011), and Raviart–Thomas element (divergence-conforming) (Buffa et al., 2010; Evans and Hughes, 2013; Johannessen et al., 2015) for the mixed discretization of the Stokes equation. Three benchmark problems with analytical solutions are tested, and they demonstrate clearly the great performance of the recovery-based error estimator compared to classical residual-based error estimator.

Design automation of sustainable self-compacting concrete containing fly ash via data driven performance prediction

Self-compacting concrete (SCC) is a highly flowable and segregation-resistant material, effectively facilitating proper filling and ensuring exceptional structural performance in confined spaces. Incorporating fly ash as a supplementary cementitious material in SCC mixtures yields numerous benefits, including enhanced cost-effectiveness in construction and the advancement of environmental sustainability. Nevertheless, the addition of fly ash in SCC poses significant challenges in modelling and predicting the properties of SCC due to lack of understanding of its influence on material rheology and bonding. It is therefore desirable to develop more appropriate machine learning approach to compliment the large scale and costly laboratory-based experiments. This paper presents four well trained supervised machine learning models for the prediction of fresh and hardened properties of SCC containing fly ash: support vector machine (SVM), decision tree, random forest, and artificial neural network (ANN). Training datasets gathered from publicly available existing relevant literature, were analysed and processed prior to shape the required machine learning models. Optimization strategies of hyperparameters were also implemented for each model. To evaluate the performance of these machine learning models and to compare their accuracy, regression error characteristic curves and Taylor diagrams were utilized. The findings reveal that all models demonstrate promising results, with the random forest model outperforming the others in predicting SCC properties with higher accuracy. This underscores the potential of random forest algorithms in accurately modelling and predicting the properties of fly ash-infused SCC. Finally, a data driven implementation framework has been developed, thereby offering robust and logical strategy for experimental designs and guidance for developing sustainable SCC.

Influence of the scanning path on the accuracy of intraoral scanners in the implanted edentulous patient: an in vitro study.

The aim of this in vitro study was to investigate the influence of scan paths on the accuracy (trueness and precision) of intra-oral scanning of an implant impression on an edentulous patient. An epoxy resin maxillary model was made with 6 bone level implants (NobelParallel Conical Connection RP, NobelBiocare®). The implants were placed at the spot of the first incisor, the canine and the first molar. The trans gingival component (Multi-unit, NobelBiocare®) was screwed onto the implants. The scanbodies (IO 2C-A, Elos Accurate®) were then screwed onto the multi-units. The model was run through a coordinate measurement machine to obtain a control cast. Then, five different scanning paths, respectively the zigzag technique (ZZT), the zigzag technique with palatal (ZZTP), the wrap technique (WT), the wrap technique with palatal (WTP), and the big zigzag technique (BZZT), were applied by a single operator. Finally, each scan was compared to the control model. Results were assessed by one-way ANOVA and linear mixed effects models at P<0.05. The study showed that scan paths ZZT and ZZTP had significantly lower absolute positioning errors and residual mean square errors than the others (P<0.0001). For distances between consecutive implant axes and for absolute vertical errors, their superiority was borderline (P<0.10). Overall, techniques ZZT and ZZTP were equally performant and proved to be the most accurate. This in vitro experimental study demonstrates that the scan path can have an influence on the accuracy of the optical impression for full arch rehabilitation on implants.

Damage identification of wind turbine blades based on deep learning and ultrasonic testing

ABSTRACT Ultrasonic testing (UT) is an effective method for detecting internal damage in wind turbine blades. However, manual interpretation is time-consuming and subjective. To automate UT in the wind turbine industry, an image detection model based on deep learning called UCD-YOLO (Ultrasonic C-scan image Detection You Only Look Once) is proposed to detect internal damage such as stratified and lack of glue in wind turbine blades. In order to enhance the feature extraction ability under complex damage forms, the use of deformable convolutional networks and context augmentation module are proposed. Designing a lightweight cross stage partial spatial pyramid pool fast (LCSP_SPPF) module, which allows the model to learn more information and improve detection accuracy. The introduction of wise intersection over union (WIoU) improves the convergence speed while reducing the regression error. Using UT devices to collect damage images of wind turbine blades and conducting comprehensive evaluation experiments on the detection performance of the proposed method. Results show that compared to the YOLOv5, UCD-YOLO is not only lighter but also achieves a 7.5% increase in mean average precision (mAP50). And compared with other detection methods, further verifying that the proposed method can quickly and effectively detect wind turbine blade internal damage, improving power generation efficiency.

A Machine Learning approach for enhancing permittivity mixing rules of binary liquids with a Gaussian modification and a new interaction factor estimation

The microstructure and solvation mechanics of binary liquids are key for predicting mixture permittivity. However, since traditional mixing rules do not consider this complexity, they must be modified to address the mixture characteristics through an interaction factor (kint). This paper evaluates this parameter for several mixing rules, applying Support Vector Regressor models trained with glycerin-water reflective signals acquired with a Dielectric Resonator sensor. The regression error of these models indicates both the optimal interaction factor and the mixing rule that fits the most with experimental permittivity values. Kraszewski and Hashin-Shtrikman mixing rules achieved the best performance with an RMSE of around 1. In addition, this paper suggests that the interaction factor can be estimated through the molar volume and the dielectric contrast between liquids (kint=2.67) without acquiring experimental data. Moreover, after analyzing the physical limitations of a linear modification formula, this paper proposes an alternative based on a Gaussian function that avoids unrealistic volume fractions. Both contributions enhance mixing rule accuracy and improve the flexibility to model mixture dielectric behavior.

Examining Volatility in Bank Stock Prices: A Comparative Exploration of Dividend Policies, Macroeconomic Influences, and Company-Specific Factors

This study aims to ascertain and compare factors influencing stock price volatility in terms of dividend policy, macroeconomic and company specific aspects for selected subset of listed banks in Bangladesh. Eleven years of secondary data (from 2011 to 2021) for fifteen commercial banks (representing 61.5% market capitalization of the banking sector) was acquired from diverse sources. The dependent variable chosen is stock price volatility, whereas the dividend policy is initially represented by dividend payout ratio (PRt &amp; PRt-1), dividend yield (DYt &amp; DYt-1). Several firm and country-specific macroeconomic indicators are used as control variables. A methodical approach followed by panel data analysis has been employed to identify a suitable model that can yield more accurate estimators. Consequently, some diagnostic tests are also performed to account for diagnosed problems: contemporaneous cross-sectional correlation, group-wise heteroskedasticity and autocorrelation; Driscoll-Kraay standard error regression model is finally applied. The study not only found significant negative impact of dividend payout ratio but also spotted a significant influence of inflation, EPS, and firm size on stock price volatility. Price volatility influenced by “No dividend policy” can be reinvestigated from the aspect of behavioral finance in the future. Furthermore, this study also sheds light on to what extent firm-specific and macroeconomic influence impact stock price volatility in Bangladesh's banking sector.

An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation

BackgroundThe accurate measurement of Low-density lipoprotein cholesterol (LDL-C) is critical in the decision to utilize the new lipid-lowering therapies like PCSK9-inhibitors (PCSK9i) for high-risk cardiovascular disease patients that do not achieve sufficiently low LDL-C on statin therapy.ObjectiveTo improve the estimation of low LDL-C by developing a new equation that includes apolipoprotein B (apoB) as an independent variable, along with the standard lipid panel test results.MethodsUsing β-quantification (BQ) as the reference method, which was performed on a large dyslipidemic population (N = 24,406), the following enhanced Sampson-NIH equation (eS LDL-C) was developed by least-square regression analysis:eSLDL-C=TC1.15-HDL-C1.25-TG6.99-TG×NonHDL-C1120+TG28910+TG×ApoB1240+ApoB4.54-4.73\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$eS\\,LDL-C= \\frac{TC}{1.15}-\\frac{HDL-C}{1.25}-\\frac{TG}{6.99}-\\frac{\\left(TG\ imes NonHDL-C\\right)}{1120}+\\frac{{TG}^{2}}{8910}+\\frac{\\left(TG\ imes ApoB\\right)}{1240}+\\frac{ApoB}{4.54}-4.73$$\\end{document}ResultsThe eS LDL-C equation was the most accurate equation for a broad range of LDL-C values based on regression related parameters and the mean absolute difference (mg/dL) from the BQ reference method (eS LDL-C: 4.51, Sampson-NIH equation [S LDL-C]: 6.07; extended Martin equation [eM LDL-C]: 6.64; Friedewald equation [F LDL-C]: 8.3). It also had the best area-under-the-curve accuracy score by Regression Error Characteristic plots for LDL-C < 100 mg/dL (eS LDL-C: 0.953; S LDL-C: 0.920; eM LDL-C: 0.915; F LDL-C: 0.874) and was the best equation for categorizing patients as being below or above the 70 mg/dL LDL-C treatment threshold for adding new lipid-lowering drugs by kappa score analysis when compared to BQ LDL-C for TG < 800 mg/dL (eS LDL-C: 0.870 (0.853–0.887); S LDL-C:0.763 (0.749–0.776); eM LDL-C:0.706 (0.690–0.722); F LDL-C:0.687 (0.672–0.701). Approximately a third of patients with an F LDL-C < 70 mg/dL had falsely low test results, but about 80% were correctly reclassified as higher (≥ 70 mg/dL) by the eS LDL-C equation, making them potentially eligible for PCSK9i treatment. The M LDL-C and S LDL-C equations had less false low results below 70 mg/dL than the F LDL-C equation but reclassification by the eS LDL-C equation still also increased the net number of patients correctly classified.ConclusionsThe use of the eS LDL-C equation as a confirmatory test improves the identification of high-risk cardiovascular disease patients, who could benefit from new lipid-lowering therapies but have falsely low LDL-C, as determined by the standard LDL-C equations used in current practice.

Movement efficiency in survivors of childhood acute lymphoblastic leukemia: a report from the St. Jude lifetime cohort study.

Movement efficiency, a measure of neuromuscular biomechanics, may be modified by physical activity. We aimed to assess the risk of and risk factors for low movement efficiency in survivors of childhood acute lymphoblastic leukemia (ALL). Participants underwent an assessment of activity energy expenditure (AEE) with actigraphy, and the gold standard doubly labeled water, where the differences between elimination rates of oxygen and hydrogen from body water are evaluated over a week. Movement efficiency was assessed using the raw residuals of a linear regression between AEEs from accelerometers and doubly labeled water. Elastic-net logistic regressions were used to identify demographic, treatment, and functional variables associated with movement efficiency. The study cohort included 256 non-cancer controls and 302 ALL survivors (48% female), categorized as efficient (N = 24), normal (N = 245), or inefficient (N = 33) based on their movement efficiency. There was no difference in the odds for poor movement efficiency between survivors (n = 33, 10.9%) compared to controls (n = 23, 9.0%, odds ratio [OR]: 1.19, 95% confidence interval [CI]: 0.67, 2.10; p = 0.55). In survivors, neuropathy was associated with a higher risk of being inefficient compared to efficient (OR 4.30, 95% CI 1.03-17.96), while obesity (≥ 30kg/m2) had a protective association (OR 0.18, 95% CI 0.04-0.87). Neuropathy was associated with a higher risk of poor movement efficiency in survivors of childhood ALL. These results further highlight impairments associated with treatment-induced neuropathy in survivors of childhood ALL.

A residual‐based nonparametric variance ratio no‐cointegration test

It is prominently stated in the literature that local asymptotic power properties serve as a useful indicator for the performance of residual‐based no‐cointegration tests in finite samples. However, this article comes to an opposing conclusion. In particular, we show that Breitung's (2002, Journal of Econometrics 108, 343–363) nonparameteric variance ratio unit root test applied to regression residuals serves as a no‐cointegration test but is inferior to its competitors from a local asymptotic power perspective. Nevertheless, in finite samples, the variance ratio test has good size properties, competitive power, and the convenience of being tuning parameter free. In general, we find that short‐run dynamics in the error process can have considerably larger detrimental effects on the performance of residual‐based no‐cointegration tests in finite samples than changes in the only nuisance parameter affecting local asymptotic power of the tests. The results serve as a warning for practitioners and lead to interesting directions for future research.

Association between skeletal muscle and left ventricular mass in patients with hyperthyroidism.

This study aims to investigate the relationship between skeletal muscle and left ventricular mass (LVM) in patients with hyperthyroidism, providing theoretical and data-based foundations for further research on the interaction between secondary muscle atrophy and cardiac remodeling. A retrospective data collection was conducted, including 136 patients with hyperthyroidism (Study group) and 50 healthy participants (control group). The Study group was further divided into Group A (high LVM) and Group B (low LVM) based on LVM size. Multiple linear regression analysis was performed to examine the correlation between skeletal muscle and LVM, with model evaluation. Based on the results, further nonlinear regression analysis was conducted to explore the detailed relationship between skeletal muscle and LVM. Compared to the control group, the Study group exhibited significantly lower LVM, skeletal muscle mass index (SMI), and skeletal muscle mass (SMM) (P<0.05). Within the subgroups, Group A had significantly higher SMI, SMM, and hand grip strength compared to Group B (P<0.05). The results of the multiple linear regression showed a certain correlation between SMI (β=0.60, P=0.042, 95% CI=0.02~1.17) and hand grip strength (β=0.34, P=0.045, 95% CI=0.01~0.67) with LVM. However, the residuals of the multiple regression did not follow a normal distribution (K-S=2.50, P<0.01). Further results from a generalized linear model and structural equation modeling regression also demonstrated a correlation between SMI (β=0.60, P=0.040, 95% CI=0.03~1.17) (β=0.60, P=0.042, 95% CI=0.02~1.17) and hand grip strength (β=0.34, P=0.043, 95% CI=0.01~0.67) (β=0.34, P=0.045, 95% CI=0.01~0.67) with LVM. Patients with hyperthyroidism may exhibit simultaneous decreases in LVM, SMM, and SMI. The LVM in patients is correlated with SMM and hand grip strength, highlighting the need for further exploration of the causal relationship and underlying mechanisms. These findings provide a basis for the prevention and treatment of secondary sarcopenia and cardiac pathology in patients with hyperthyroidism.

Robust hybrid learning approach for adaptive neuro-fuzzy inference systems

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a regression model that uses fuzzy logic and neural networks, making it suitable for modeling the uncertainty of regression problems. However, the non-robust loss function in ANFIS's hybrid learning algorithm can make it susceptible to the direct effects of noise and outliers. This paper introduces a new procedure that uses robust loss functions to enhance the hybrid learning performance against noise and outliers. In addition, a new robust loss function is devised that can completely ignore outliers. Furthermore, a set of robust loss functions with mathematical relations are suggested. The proposed approach is evaluated on real-world problems, including weather forecasting and stock market prediction. Results suggested that the proposed model can reduce the Mean Square Error (MSE) in regression. Moreover, the new procedure enables utilizing different loss functions based on the application.

Exploring the influence of gender diversity and women's empowerment on family entrepreneurship performance: the moderating impact of firm characteristic

PurposeThis paper aims to examine how gender diversity and women’s empowerment influence the performance of family entrepreneurships and explores the role of firm characteristics as a moderating factor.Design/methodology/approachThe study used a structured questionnaire as the survey tool to collect data from 91 women managers working in family entrepreneurships, which originated from entrepreneurial initiatives, located in various Lebanese regions. The validity of the construct was assumed using the fitness of extracted index, incremental fit-index, non-normal fit-index, root mean square of residuals and standard root mean square residual. Composite reliability, Cronbach's alpha and value confirmatory factor analysis were used to measure the internal consistency. Data were analyzed using the structural equation modeling method.FindingsThis study reveals that gender equality, education level and family support significantly affect women's empowerment while an insignificant association was found between empowerment and earning social status and achieving financial independence. This paper also showed a significant interaction between women’s empowerment and the performance of family entrepreneurships. Additionally, the results showed that women holding managerial positions in family entrepreneurships is positively associated with firm performance. Finally, it was concluded that the location of the family firm moderates the relationship between gender diversity and firm performance.Originality/valueThis research contributes to theory and practice regarding the role of women in family entrepreneurships and sheds light on gender differences influencing family entrepreneurships and women empowerment issues.