Robust Analysis Research Articles

Measuring robustness in uncertain topologies: a study of on-demand bus networks

Ensuring the robustness of bus networks is essential for delivering reliable and efficient mobility services to passengers. This paper addresses the challenge of assessing the robustness of on-demand bus networks, which are characterised by uncertain topology. We propose a framework based on a random multilayer bus network, from which we develop four topological metrics to quantify network robustness. Additionally, we conduct network attack simulations to derive simulation-based robustness indicators, which are acknowledged as golden rules in robustness measurements. Correlation analysis between the proposed metrics reveals a positive relationship between the topological properties and network robustness, validating the effectiveness of the topological metrics in assessing the robustness of networks with uncertain topology. This study fills a gap in the existing literature by providing a robustness analysis framework specifically tailored to on-demand bus networks, contributing to the design of more resilient on-demand bus systems.

The environmental impact of buying groceries online/offline pre and during COVID-19. Any changes?

COVID-19 has represented a watershed in people's lifestyle. One important consequence has been the change in how people buy groceries online/offline that represent the most frequent shopping-related trip any household performs. Since this has substantially different transport and environmental implications, it is crucial to investigate what are the COVID-19-induced variations. This paper estimates both travel and environmental impacts grocery channel choice implies by comparing, in a diachronic fashion, the pre and during COVID-19 situation in Norway, considering alternative scenarios describing different service configurations for each channel considered. Stated preferences are used to estimate market shares for grocery purchase distinguishing between different channels: in-store, home delivery and click&pick. The robust comparative analysis between the two time periods is guaranteed by the use of the same methodological approach in the same geographical context. Results show that total emissions are lower during the pandemic and adopting a free delivery strategy not only increases home delivery market shares but also produces the lowest level of CO2 emissions regardless of COVID-19. The results obtained are particularly relevant in the "new normal" the world is experiencing both from a public policymaking perspective, when promoting transport-related interventions to curb emissions, and from a private standpoint, when customising market strategies to increase sales and profits.

A frequency-Hankel transform method to extract multimodal Rayleigh wave dispersion spectra from active and passive source surface wave data

Rayleigh wave dispersion energy spectra have been widely used to extract dispersion curves and invert underground S-wave velocity structures for engineering geophysics and seismology. We develop a frequency-Hankel (F-H) transform method to extract high-quality multimodal Rayleigh wave dispersion energy spectra from active and passive source Rayleigh wave data. The F-H transform method is inspired by the frequency-Bessel (F-J) transform method and considers the physical meaning of Green’s functions for Rayleigh wave dispersion analysis. The F-H transform method can naturally avoid crossed artifacts caused by converging waves on F-J spectrograms and obtain more multimodal dispersion spectra of the same quality with fewer Rayleigh wave data than the F-J transform method. Synthetic and field Rayleigh wave data from active and passive sources for near-surface exploration and ambient noise tomography are used to demonstrate the validity, accuracy, and applicability of the F-H transform method. The F-H transform method unifies the formulas of the F-J transform method and its modifications for active and passive sources of Rayleigh wave data. The F-H transform method is a robust and efficient multimodal Rayleigh wave dispersion analysis method for active and passive source Rayleigh wave data.

Caffeinated beverages intake and risk of deep vein thrombosis: A Mendelian randomization study.

This study aimed to explore the potential link between coffee and tea consumption and the risk of deep vein thrombosis (DVT) through Mendelian randomization (MR) analysis. Employing the MR, we identified 33 single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for coffee intake and 38 SNPs for tea intake. The investigation employed the inverse-variance weighted (IVW) method to evaluate the causal impact of beverage consumption on DVT risk. Additionally, MR-Egger and MR-PRESSO tests were conducted to assess pleiotropy, while Cochran's Q test gauged heterogeneity. Robustness analysis was performed through a leave-one-out approach. The MR analysis uncovered a significant association between coffee intake and an increased risk of DVT (odds ratio [OR] 1.008, 95% confidence interval [CI] = 1.001-1.015, P = 0.025). Conversely, no substantial causal effect of tea consumption on DVT was observed (OR 1.001, 95% CI = 0.995-1.007, P = 0.735). Importantly, no significant levels of heterogeneity, pleiotropy, or bias were detected in the instrumental variables used. In summary, our findings suggest a modestly heightened risk of DVT associated with coffee intake, while tea consumption did not exhibit a significant impact on DVT risk.

A pathway to coordinated regional development: Energy utilization efficiency and green development - Evidence from China's major national strategic zones

According to the new economic geography hypothesis, this study establishes an analytical framework to assess the efficiency of energy use and the progress of green development. The framework is based on panel data collected from 254 cities at the prefecture level in China. The spatial Durbin model is employed to examine the influence and spatial spillover effect of energy utilization efficiency on green development in major national strategic zones. The results show that: improving energy utilization efficiency promotes green development in major national strategic regions, with a stronger intra-regional transfer effect than inter-regional. This is confirmed by robustness and endogeneity analyses. The spillover intensity of energy utilization efficiency on green development varies across regions. It shows an inverted U-shape curve in the Yangtze River Economic Belt, a U-shape curve in the Yellow River Basin and Yangtze River Delta region, a diminishing margin in the Beijing-Tianjin-Hebei region, and a growing margin in the Guangdong-Hong Kong-Macao Greater Bay Area. There is significant spatial heterogeneity within these regions. Different mediating mechanisms affect green development in different regions. The knowledge spillover effect mediates energy utilization efficiency's impact on green development in the Guangdong-Hong Kong-Macao Greater Bay Area, the industry linkage effect in the Yangtze River Economic Belt and Beijing-Tianjin-Hebei region, the environmental improvement effect in the Yellow River Basin, and the market linkage effect in the Yangtze River Delta region. To promote regional coordinated development with energy utilization efficiency, the country should not only consider differences between national strategic areas but also focus on the effects of different paths to realize the complementarity between energy utilization efficiency and green development.

Enhancing the accuracy of China's electricity consumption forecasting through economic cycle division: An MSAR-OPLS scenario analysis

Accurate electricity consumption (EC) forecasting is vital for ensuring efficient energy supply and underpinning long-term economic stability. Prior research has often overlooked the intricate interplay between the nuanced dynamics of economic growth and EC. In contrast, this study innovatively incorporates the economic cycle into the analytical framework of EC, providing a more comprehensive perspective. Firstly, we construct the MSAR-OPLS two-step model to discern the factors influencing EC across various cycles. We select the most fitting stabilization cycle as the foundation for forecasting. By integrating both dynamic and static scenario analyses, we offer a comprehensive and precise evaluation of future EC trends. This study ensures both the reliability and accuracy of our forecasts. Projections suggest that China's EC will escalate to 11,780 billion kWh by 2030, reflecting an average annual growth rate of 3.96% from 2023 to 2030. Furthermore, through rigorous robustness and sensitivity analyses, the study validates the forecast's accuracy. Beyond emphasizing the role of economic phases in ensuring energy security, this research furnishes policymakers with insights on embedding foresightful elements into strategic economic and energy planning.

Improved quaternion robust principal component analysis for color image recovery

Numerous studies have demonstrated the potential of robust principal component analysis (RPCA) in image recovery. However, conventional RPCA methods for color image recovery apply RPCA independently to each color channel, which ignores the correlation information of the red, green and blue channels. To improve the performance of RPCA-based methods and draw inspiration from the success of quaternion representation in color image processing, we propose an improved quaternion RPCA (IQRPCA) method for color image recovery. The IQRPCA method treats all color channels holistically and considers the correlation information of different color channels naturally. In addition, we have developed a quaternion nuclear norm known as improved quaternion Cauchy nuclear norm, which produces an even more effective and robust approach to color image recovery task. Compared to the RPCA method in the quaternion setting, the IQRPCA method treats the singular values differently, which shows better recovery performance than competing methods. Furthermore, we provide a convergence analysis of the proposed method. Our experiments confirm the effectiveness of IQRPCA in the application of color image recovery.

Causes of Poverty among Genders in Micro and Small Scale Enterprises in Jalingo, Nigeria

Ending poverty is a key issue of discussion in the world and the first goal of the seventeen sustainable development goals. Nigeria is richly endowed with natural resources, yet her people are severely improvised, trapped in vicious cycle of poverty. High levels of poverty among genders have been found to be detrimental to people’s well-being and economic growth. Previous studies on microcredit were mostly in relation to economic growth with little attention on poverty and genders. This study fills this gap by investigating the relationship between micro-credit and Poverty among genders of micro and small-scale entrepreneurs. The simple unifying neoclassical scarcity-driven poverty theoretical framework and cross-sectional survey research design were adopted. Data analysis was based on the questionnaires retrieved from 186 and 726 small and micro entrepreneurs, a total of 912 obtained from the 229 and 794 samples determined by the Cochran’s simple random sampling formula. The Probit and regression models were estimated using the Maximum Likelihood and Ordinary Least Square techniques respectively. Descriptive analysis was conducted to examine the demographic features of the respondents. The heteroskedasticity robust standard analysis was done and statistical significance at p? 0.05. The mean age of the respondents was 40 years and 63% were with formal education. The FGT poverty rate, depth and severity are 61.95%, 24.67% and 12.92% respectively, while the SST poverty rate is 32.68% and the Gini coefficient is 35.63%, indicating high poverty level of Poverty among the entrepreneurs. Probit results show that respondents who obtain micro-credit from deposit money banks (DMBs), microfinance banks, and relatives are less likely to be poor, with 16.9%, 11.8%, and 6.8% probabilities respectively. Also, respondents with monthly (43.88%), quarterly (48.56%), and yearly (37.08%) access to microcredi

A Review of E-Government Practices in the Age of Digitalization

In the era of digitalization, this study comprehensively reviews e-government practices, focusing on the European Union member states, with a particular emphasis on Slovakia and the Czech Republic. The purpose of this research is to analyze the impact of public administration digitalization on corruption, utilizing a comparative and correlative research analysis. The study also investigates the clustering of e-government practices in the EU using Gaussian Mixture Models. The methodology employed includes an extensive review of existing literature on e-government maturity models, benchmark frameworks, and national concepts of public administration informatization. Data from authoritative sources such as the Digital Economy and Society Index (DESI), E-Government Benchmark reports, and national reports, including Slovakia's National Concept of Public Administration Informatization, contribute to a robust analysis. Results reveal the nuanced relationship between public administration digitalization and corruption levels in EU member states. Clustering based on Gaussian Mixture Models provides insights into the diverse landscape of e-government practices. Furthermore, the study compares the digitalization progress of Slovakia and the Czech Republic, shedding light on the challenges and successes of these nations in implementing e-government services. In conclusion, the findings underscore the significance of e-government in shaping transparent and efficient public administrations. Recommendations include fostering international collaboration to share best practices and addressing shortcomings in e-government policy implementation. The research implications extend to policymakers, researchers, and practitioners, offering valuable insights for advancing e-government development. This study contributes to the ongoing discourse on e-government, aligning with the global effort towards sustainable development and digital governance. As governments strive to provide citizen-centric digital services, understanding the intricacies of e-government practices becomes imperative for informed decision-making and policy formulation.

Robust Approaches to the Quantitative Analysis of Genome Formula Variation in Multipartite and Segmented Viruses.

When viruses have segmented genomes, the set of frequencies describing the abundance of segments is called the genome formula. The genome formula is often unbalanced and highly variable for both segmented and multipartite viruses. A growing number of studies are quantifying the genome formula to measure its effects on infection and to consider its ecological and evolutionary implications. Different approaches have been reported for analyzing genome formula data, including qualitative description, applying standard statistical tests such as ANOVA, and customized analyses. However, these approaches have different shortcomings, and test assumptions are often unmet, potentially leading to erroneous conclusions. Here, we address these challenges, leading to a threefold contribution. First, we propose a simple metric for analyzing genome formula variation: the genome formula distance. We describe the properties of this metric and provide a framework for understanding metric values. Second, we explain how this metric can be applied for different purposes, including testing for genome-formula differences and comparing observations to a reference genome formula value. Third, we re-analyze published data to illustrate the applications and weigh the evidence for previous conclusions. Our re-analysis of published datasets confirms many previous results but also provides evidence that the genome formula can be carried over from the inoculum to the virus population in a host. The simple procedures we propose contribute to the robust and accessible analysis of genome-formula data.

Activating the SERS features of screen-printed electrodes with thiocyanate for sensitive and robust EC-SERS analysis

This study presents a novel electrochemical (EC) methodology for activating the SERS features of disposable silver screen-printed electrodes (AgSPEs) using a pseudohalide as a precipitating agent to guide the assembly of a nano-filamentous silver network responsible for a strong SERS enhancement. To the best of our knowledge, the use of thiocyanate for SPEs activation for quantitative purposes has not been reported before. In order to better study this complex system, time-resolved (TR) operando spectroelectrochemistry (SEC) measurements were performed by SERS and UV-Vis and correlated with SEM imaging of the electrode surface at different steps. Moreover, to significantly increase the reproducibility of the assays, a rapid EC protocol was proposed for the first time to obtain a reliable Ag/AgSCN reference electrode (RE) and a comprehensive optimization process was conducted to determine the critical parameters. Additionally, propranolol, as an emerging pharmaceutical pollutant has been selected as a model molecule to demonstrate the applicability of the method for rapid (3 min) quantitative analysis in the nM range from real river and tap water samples.

The Influence of Risk Management Practices on Financial Market Stability: Insights from Lebanon

This research investigates the relationship between various risk management practices, including diversification, hedging, and contingency planning, and their impact on the dependent variable, financial market volatility. The study is based on a sample size of 127 observations in the context of Lebanon's financial markets. The primary objective of this research is to analyze how risk management practices employed by financial institutions and market participants affect the stability of Lebanon's financial markets. To achieve this, the study employs a quantitative approach, utilizing statistical analysis and regression modeling to assess the relationships between the independent variables (risk management practices) and the dependent variable (financial market volatility). The independent variables in this study encompass risk management practices, namely diversification, hedging, and contingency planning. These practices are crucial components of financial risk management strategies employed by institutions and investors. By examining their influence on financial market stability, this research aims to provide insights into effective risk mitigation strategies within the Lebanese financial context. The decision of Lebanon as the study's context is significant due to the unique economic and geopolitical challenges faced by the country. The financial markets in Lebanon have been subject to various external and internal pressures, making the analysis of risk management practices particularly relevant. The sample size of 127 observations allows for robust statistical analysis, ensuring the validity of the findings. The research findings are expected to contribute to the existing body of knowledge on risk management and financial market stability, with potential implications for policymakers, financial institutions, and investors operating in Lebanon and similar emerging market economies.

Enhancing SAR Multipath Ghost Image Suppression for Complex Structures through Multi-Aspect Observation

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image.

A gene regulatory network model that recovers the abaxial-adaxial polarity in Arabidopsis thaliana leaf primordium

Megaphylls, present in the majority of vascular plants, show in many plant lineages an abaxial-adaxial polarity in their dorsoventral axis. This polarity commonly translates into different tissues developing on each side of the leaf blade. This is important because it promotes better photosynthetic efficiency as related to light absorption and gas exchange. Many researchers have studied the molecular bases of the emergence of leaf abaxial-adaxial polarity, showing that it is produced by the interaction and differential expression of particular genes and other molecules. However, until now, it is still unclear if the molecular components documented thus far are sufficient to explain the emergence of leaf polarity. In this work, we integrated the available experimental data to construct a graph of the Gene Regulatory Network (GRN) involved in the formation of abaxial-adaxial polarity in the leaf primordium of Arabidopsis thaliana. This graph consisted of 21 nodes and 47 regulations. We extracted the main components of the graph to obtain a Minimum Network consisting of six genes and 22 possible regulations. Then, we used the Boolean network (BN) formalism to describe the dynamics of this Minimum Network. We identified 1905 distinct BNs that comprised the regulations of the Minimum Network and exclusively generated the two attractors representing the abaxial and adaxial cell types. This highlights the fact that most graphs, including our network, can describe experimentally observed behaviors with many BN dynamics. By performing mutant simulations and robustness analysis, we found that two of the 1905 BNs better reproduce experimentally available information. To produce the expected attractors, both BNs predict the same missing regulations, which we propose should be experimentally analyzed to confirm their existence. Interestingly, these two BNs have low robustness to perturbations compared with previously analyzed GRNs. This was an unexpected result since abaxial-adaxial polarity is a robust biological trait, which suggests more components or regulations of the network are missing.

Unmanned Helicopter Airborne Fourier Transform Infrared Spectrometer Remote Sensing System for Hazardous Vapors Detection

The rapid development of unmanned aerial vehicles (UAVs) provides a new application mode for gas remote sensing. Compared with fixed observation and vehicle-mounted platforms, a Fourier transform infrared spectrometer (FTIR) integrated in the UAV can monitor chemical gases across a large area, can collect data from multiple angles in three-dimensional space, and can operate in contaminated or hazardous environments. The unmanned helicopter has a larger payload and longer endurance than the rotary-wing drone, which relaxes the weight, size and power consumption limitations of the spectrometer. A FTIR remote sensing system integrated in an unmanned helicopter was developed. In order to solve the data acquisition and analysis problem caused by vibration and attitude instability of the unmanned helicopter, a dual-channel parallel oscillating mirror was designed to improve the stability of the interferometer module, and a robust principal component analysis algorithm based on kernel function was used to separate background spectrum and gas features. The flight experiment of sulfur hexafluoride gas detection was carried out. The results show that the system operates stably and can collect and identify the target spectrum in real time under the motion and hovering modes of an unmanned helicopter, which has broad application prospects.

Charting an Economic Sustainability Path: Quantile Regression Analysis of Green Finance and Financial Development in Newly Industrialized Economies

Sustainability, once a fringe concept, has become a central theme in global discourse, commanding the attention of governments, industries, and individuals alike. Newly industrialized countries (NICs) stand as exemplars of economies in transition, teetering on the precipice of prosperity and vulnerability. The present study aimed to chart economic sustainability through green finance and financial development in NICs. The study uses Panel Quantile Regression (PQR) and robustness analysis using annual data from 1998 to 2022. Overall, the study results indicate heterogeneity in the impacts of green finance, financial development, and foreign direct investment on the economic sustainability of NICs. The findings from the PQR unveiled that green finance contributes to economic sustainability in lower and middle quantiles (15th, 25th and 50th quantiles) while showing negative but insignificant effects in higher quantiles (75th and 95th quantiles). Financial development is proxied through three distinct variables; however, the heterogeneity in the results of all the quantiles can be witnessed. In addition, the positive and significant impacts of FDI were validated approximately across all quartiles (15–75th), but positive and insignificant effects are endorsed in the 95th quantile. Compared heterogeneous panel estimators support analogous results; however, the amplitude of their parameters varied substantially. Finally, quantile slope equality and symmetric quantiles test as robustness analysis confirmed the authenticity of the results. Therefore, this study’s empirical findings can assist policymakers in comprehending how green finance, financial development, and FDI can achieve economic sustainability and address sustainability issues. JEL Classification F43, F65, O11, Q20

Algorithm-assisted diagnosis of Hirschsprung’s disease – evaluation of robustness and comparative image analysis on data from various labs and slide scanners

BackgroundDifferences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.MethodsThe decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.ResultsThe algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.ConclusionsThe results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development – Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Unravelling the Impact of Financial Literacy on the Intention of Indian Retail Investors to Adopt Socially Responsible Investments: The Interplay of Social Self-Efficacy as a Mediating Mechanism

In the context of the escalating socio-economic disparities and environmental concerns, this study delves into the intricate interplay among financial literacy (FL), social self-efficacy (SSE) and intentions for socially responsible investment (SRI). Grounded firmly in the theory of planned behaviour, a survey of 235 Indian participants was conducted employing a meticulously structured questionnaire and leveraging Partial Least Squares Structural Equation Modelling software for robust data analysis. The research addresses pivotal questions, examining the impact of FL on fostering positive SRI attitudes, appraising the influence of subjective norms (SNs) on the intentions of Indian investors regarding SRI, and exploring the mediating role of SSE between FL and SRI intentions. The findings underscore the substantive role of FL in shaping positive attitudes towards SRI, exerting a direct influence on SRI propensities. Concurrently, SNs exert a comparable impact on SRI intentions. Notably, the study unravels a dual mediation paradigm, elucidating the sequential mediation of SRI attitude and SSE. The study yields pragmatic implications for fund managers and policymakers. It accentuates the strategic imperative of effective communication of investment benefits, necessitating tailored approaches attuned to cognitive and psychosocial orientations. In essence, the study augments our understanding of the nuanced dynamics guiding sustainable investment decisions, thereby advancing the comprehension of investor behaviour in the domain of responsible investing.

Sustainable products and audit fees: empirical evidence from western European countries

Purpose This study aims to examine whether firms that appear to exhibit high sustainable outputs are more likely to pay higher audit fees than firms without such outputs. Design/methodology/approach The sustainability outputs are measured using a sustainable product portfolio consisting of four products: clean energy products, eco-design products (EDP), environmental products (EP) and sustainable building projects (SBP). The audit fee variable is measured by the natural logarithm of the total amount of audit fees. The study tests two models of the association between these outputs and audit fees; Model 1 tests this association in the absence of the moderating variable (sustainability committee), and Model 2 tests the association in the presence of the moderating variable. Findings An analysis of data on 261 European firms from the Refinitiv Eikon database from 2010 to 2019 shows that high sustainability outputs are significantly and positively associated with audit fees. More importantly, this association is moderated by the presence of a board-level sustainability committee, suggesting that this type of committee reflects a factor considered by auditors in their audit risk assessment practices. The findings indicate that in Model 1, one (EP) out of four variables has a significant and positive association with audit fees, while in Model 2 and in the presence of sustainability committee, two variables (EP and EDP) have a significant and negative association with audit fees. However, the robust analysis shows that three variables (EP, EDP and SBP) have significant and negative associations with audit fees. Practical implications The study findings have important implications for policymakers, auditors and firms’ managers. For policymakers, the findings provide support for the argument that sustainable attitudes incentivise firms to manage sustainable product profiles more effectively. As such, policymakers should incentivise firms to establish a sustainability committee and regulate its role and responsibilities. Auditors should coordinate with the sustainability committee to facilitate audit efforts and reduce audit fees. Social implications Understanding the relationship between sustainable products and audit fees will allow firms to improve their portfolio of sustainable products. In addition, other social implications of this study relate to improving relationships with society by establishing a sustainability committee that is responsible to communicate with that society. Originality/value The results support the argument that firms should manage sustainable product portfolios more effectively. In addition, the results of the study highlight the importance of a new variable as a moderator, the sustainability committee, which has not been examined before.

Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

AbstractQuality is an indispensable requirement in medical laboratories, where controls are performed at different phases of the laboratory processes. In the following, we consider the internal quality control activities during the analytical phase of medical laboratories for verifying the test results prior to their release. To monitor the measurement process for errors in the test results, one approach that is considerably attracting the health professionals is patient‐based real‐time quality control. In this respect, real‐time patient test results are monitored, commonly by implementing the Moving Average (MA) control chart with truncation limits. A usual practice is the use of truncation limits to exclude abnormal patient test results from the MA calculations. However, an open issue in the literature is how to determine the width of the truncation limits and the window size in the MA calculations. Furthermore, the distributions of the test results are often nonnormal and difficult to model by personnel having insufficient training in statistics. Consequently, robustness of the performance of the MA control chart to non‐normal distributions of test results is another open topic in the literature. In this study, a detailed robustness analysis is performed by considering various distributions having different shapes. Performance of several MA control chart designs constructed by altering the truncation limits and window sizes are investigated. Based on our simulation results with selected distributions, we conclude that the truncation limits should not be used and a window size of 20 is recommended. Under such a setting, the MA control chart performs similar to its intended performance in terms of false alarms and in detecting systematic errors, hence being robust to non‐normality of the test results.