Dataset Research Articles

The effect of natural disasters on food security in Sub-Saharan Africa

Purpose This study aims to assess the effects of natural disasters on food security in a sample of 40 sub-Saharan African countries. First, the authors assess the effects of natural disasters on the four dimensions of food security and second, the authors disaggregate natural disaster using the two dimensions that are most representative, namely, hydrological and biological disasters. Design/methodology/approach The regressions are based on the generalised method of moments on a data set covering the period 2005–2020. Natural disasters are measured by the total number of people affected and food security by its characteristics: access, availability, use and sustainability. Findings The results show that natural disasters increase the prevalence of undernourishment but reduce dependence on cereal imports. An increase in natural disasters by 1% increases the prevalence of undernourishment by the same proportion. As for import dependency, a 1% increase in natural disasters reduces dependency by 2.2%. The disaggregated effects show that hydrological disasters are more significant than biological disasters in impacting food security. Floods reduce the average energy supply adequacy but also dependence on cereal imports. Policy implications are discussed. Originality/value The study complements the extant literature by assessing the effects of natural disasters on food security in a region where food insecurity is one of the worst in the world.

Does financial statement comparability reduce cost of equity? Evidence in emerging market

Purpose The research aims to provide empirical evidence on the relationship between financial statement comparability (FSC) and cost of equity (COE) in an emerging market. Design/methodology/approach Specifically, this study examines the relationship between FSC and COE of Vietnamese listed firms. The research uses the System Generalized Method of Moments regression techniques for a panel data set of 454 companies for the period 2015–2022. Findings The authors find that firms with high comparability of financial statements have lower COE. To confirm the research findings, the authors conduct the robustness test by using different proxies for the cost of equity. Consistent results are found. Originality/value The study contributes to the overall understanding of the relationship between FSC and COE, and suggests policy implications for relevant stakeholders such as managers, regulatory bodies and investors. Especially, regarding policymakers, this study could provide more insight into how the accounting convergence process impacts the effectiveness of a firm’s capital allocation.

Understanding sociodemographic characteristics shaping the choice of large homes: lessons from Turkish metropolises

Purpose In metropolitan areas experiencing rapid urbanization and housing production, the size of housing units emerges as a crucial factor to consider in housing policy formulation. This study aims to focus on Turkey, a developing country undergoing rapid urbanization and a construction boom in recent years, to examine households’ housing size preferences. Through a detailed analysis, this research delves into the causal relationships between income, education and housing size preferences. Design/methodology/approach This study uses the Family Structure Survey in Turkey 2016 data set collected nationwide by the Turkish Statistical Institute (TurkStat). To address potential endogeneity issues related to income and education levels in households’ choice of house size, an extended regression model is used. In addition, survey weights are applied to the statistical model to generalize the results of the study. Findings The study demonstrates that household income correlates with an increase in house size, while household education is associated with a decrease in house size. Variables such as household age, composition and vehicle ownership are found to impact the choice of house size. Particularly, one-person and couple-only households tend to prefer smaller dwellings compared to others. Lastly, the results reveal that the influence of household composition on dwelling size varies according to household age. Originality/value This study presents a comprehensive analysis of the determinants influencing households’ housing size preferences within the framework of a developing country context, focusing on Turkey. It specifically offers insights into the causal impact of education and income levels on housing size preferences, as well as the intricate interplay between household characteristics in shaping these preferences.

Beyond terroir: a comparative analysis of Pinot Noir producers and business strategies in Burgundy, New Zealand and South Africa

Purpose This paper aims to examine the convergence and divergence of business and production practices in the global wine industry, particularly focusing on Pinot Noir producers in Burgundy, New Zealand and South Africa (SA). This study explores the interplay between firm-specific factors and regional contexts to identify competitive advantage drivers among Pinot Noir producers. Design/methodology/approach This research uses a comparative analysis approach, using data from a comprehensive winery level survey. This study applies methodologies akin to value chain analysis to unravel the configuration of productive and technology/knowledge creation activities within wineries across three regions. Findings This analysis reveals both convergence and divergence in business and production practices among Pinot Noir producers in Burgundy, New Zealand and South Africa. Although there is a degree of convergence in marketing, distribution and competition strategies, differences exist in production practices and firms’ capabilities. Burgundy emphasizes tradition and terroir expression, contrasting with the modernization and innovation focus observed in New Zealand and South Africa. However, all regions share a commitment to quality as a competitive advantage. Research limitations/implications This study acknowledges limitations such as the focus on a specific grape variety and regions, the absence of performance impact analysis and the need for additional variables like environmental, institutional and cultural factors and consumer preferences to provide a comprehensive understanding of industry dynamics. Practical implications The insights from this study offer practical implications for winemakers, industry stakeholders and policymakers. Producers can optimize production and marketing strategies based on regional contexts and market segments, whereas stakeholders can identify emerging trends and opportunities in the global wine market. Policymakers can develop targeted policies supporting innovation, sustainability and competitiveness. Originality/value This paper provides a unique contribution by conducting a comparative firm-level analysis across distinct wine-producing regions, shedding light on the nuanced interplay of factors shaping competitive advantage among Pinot Noir producers. This study’s comprehensive data set and methodological approach enhance understanding and offer valuable insights for industry stakeholders and policymakers.

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. This paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241Am, 60Co, 137Cs, 192Ir, 239Pu, and 235U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

Application of improved adaptive method of CEEMD and data-drive in fault diagnosis

Purpose The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis. Design/methodology/approach This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis. Findings According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate. Research limitations/implications In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied. Originality/value Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches

To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). The cohort included all multiparous women who delivered a singleton birth at 20–42 weeks’ gestation in an Ontario hospital between April 1, 2012 and March 31, 2014. The primary outcome was PTB < 37 weeks, with spontaneous PTB analyzed as a secondary outcome. Stepwise logistic regression and the Boruta machine learning were used to select the important variables during the first and second trimester. For building prediction models, the whole data set were divided for the two independent parts: two-third for training the classifiers (Logistic regression, random forests, decision trees, and artificial neural networks) and one-third for model validation. Then, the training data set were balanced by random over sampling technique. The best hyper parameters were obtained by the tenfold cross validation. The performance of all models was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristics (AUC). The cohort included 145,846 births, of which 8125 (5.57%) were preterm. In first-trimester models, the strongest predictors of PTB were previous PTB, preexisting diabetes, and abnormal pregnancy‐associated plasma protein-A. In the testing data set, the highest predictive ability was seen for artificial neural networks, with an area under the receiver operating characteristic curve (AUC) of 68.8% (95% CI 67.6–70.1%). In second-trimester models, addition of infant sex, attendance at first-trimester appointment, medication exposure, and abnormal alpha-fetoprotein concentrations increased the AUC to 72.1% (95% CI 71.1–73.1%) with logistic regression. With the inclusion of the variable complications during pregnancy, the AUC increased to 80.5% (95% CI 79.6–81.5%) using logistic regression. For both overall and spontaneous PTB, during both the first and second trimesters, models yielded negative predictive values of 97%. Overall, machine learning and logistic regression produced similar performance for prediction of PTB. For overall and spontaneous PTB, both first- and second-trimester models provided negative predictive values of ~ 97%, higher than that of fetal fibronectin.

Why are healthcare professionals leaving NHS roles? A secondary analysis of routinely collected data

BackgroundMuch policy attention focuses on increasing the supply of workers in the English NHS but there has been less attention paid to the rise in leavers. This paper seeks to explore how existing data sets can illuminate the decision-making of leavers and inform actions that could mitigate this.MethodSecondary analysis of routinely collected data from 79 workforce projects in the UK (n = 46 339 participants) over a 4-year (2019–2023) period was undertaken. Free text data we extracted and analysed using content analysis, sentiment analysis and text mining. Inclusion criteria were those who stated they had resigned, had confirmed retirement date, and had secured employment elsewhere either within or without the sector but had not yet resigned. Exclusion criteria were those who had not indicated they were leaving or indicated intention to leave. These findings were then compared with themes from Herzberg’s work hygiene theory and Hoffat and Woods’s professional practice environment theory.ResultsMultiple reasons were given for leaving. Findings were congruent with Herzberg’s two factor work hygiene theory and demonstrate that leavers are driven by the inability to meet their intrinsic motivation to practice according to their professional standards as much as by terms and conditions. Leavers describe suboptimal professional practice environments which produce obstacles to achieving their work objectives and leaving their intrinsic motivation frustrated.ConclusionWhilst reasons for leaving differ between people, there is a relationship between intrinsic motivation (why they want to do the job) and the conditions in which they try to do the job. This study suggests that looking beyond the primary reason for leaving given in the national dataset could identify how the practice environment influences the decision.

Deep learning–based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer

A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile distributions of planning dose plan using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask (BM) and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site. The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm/2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets. Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.

Examining the combined effect of antenatal care visits and iron-folic acid supplementation on low birth weight: a pooled analysis of two national data sets from Nepal

BackgroundThe prevalence of low birth weight (LBW) has stagnated at approximately 12% for the past 15 years in Nepal, significantly impacting newborn survival. While antenatal care (ANC) visits and iron-folic acid supplementation are recognised as important interventions to reduce LBW, there is a lack of evidence regarding their combined effect. This study aimed to explore the potential synergistic impact of ANC and iron-folic acid supplementation on LBW in Nepal by analyzing data from two national surveys.MethodsThe nationally representative Nepal Demographic and Health Surveys of 2016 and 2022 were used, and the pooled dataset was analysed. Birth weight and the prevalence of LBW (i.e. birthweight < 2500 g) were reported using descriptive statistics. The associations among LBW, ANC visits, and iron-folic acid supplementation were examined using logistic regression analyses.ResultsThe mean birth weight was 3011 g, with an LBW prevalence of 11.2%. Not attending ANC (Adjusted Odds Ratio (AOR): 1.49; 95% Confidence Interval (CI): 1.14, 1.95) and not consuming iron-folic acid supplements (AOR: 1.43; 95% CI: 1.11, 1.84) were independently associated with a higher likelihood of having LBW. Furthermore, when considering both factors together, mothers who attended less than four ANC visits and consumed iron-folic acid for ≤ 90 days had the higher likelihood of having LBW (AOR: 1.99; 95% CI: 1.35, 2.60) compared to those who did not.ConclusionsThis study highlights that the individual and joint influence of ANC visits and iron-folic acid supplementation on having LBW. These findings underscore the significance of ANC attendance and iron-folic acid supplementation in preventing LBW. Traditionally, these two interventions were primarily considered as maternal survival strategies. However, our findings indicate that these existing interventions could be utilised further for both maternal and newborn survival. Given that these services are offered free of cost and are available near people’s homes through the National Safe Motherhood Programme in Nepal, efforts to increase the uptake of these services should be strengthened while emphasising their role in preventing LBW.

Research on Topology Optimization of Building Digital Supply Chain Based on Genetic Neural Network

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most.

Does corporate green innovation unlock financial doors? The mediating role of environmental performance

Purpose This study aims to respond to the latest research calls to precisely revisit the nexus between corporate green innovation (CGI) and financial decisions through deeply investigating the mediating effect of corporate environmental performance measured by the effectiveness of emission reduction. Design/methodology/approach This study analyzes nonfinancial-listed firms on the Australian Securities Exchange from 2002 to 2019 using multiple regression analysis on a panel data set. Initially, different static panel data approaches were used. To account for the potential endogeneity issue and generate robust outcomes, the authors apply the one-step system generalized method of moment, two-stage least squares and lagged model approaches. Findings The results provide a clear indication that the practices of green innovation can favorably contribute to the level of environmental performance, which in turn affect the firm’s ability in opening the new financial doors and shape solid capital structure. In this context, the effective environmental performance fully mediates the nexus between CGI and capital structure of a firm. More importantly, the outcomes are robust and coherent across different estimation techniques. Originality/value The originality of this study lies in its utilization of mediation analysis to explore the relationship between CGI and a firm's financial structure. This approach distinguishes it from previous research by offering a thorough and nuanced understanding of how green innovation practices influence the financing decisions of a firm.

Global Inference and Test for Eigensystems of Imaging Data Over Complicated Domains

A nonparametric approach for analyzing eigensystems of image data over a complex domain is novelly developed. The proposed estimators, which are based on bivariate splines, have both oracle efficiency of O p ( n − 1 / 2 ) , meaning they are asymptotically indistinguishable from estimators computed with true trajectories, and computational efficiency with more convenient spectrum decomposition forms. Under mild conditions, we derive consistency and weak convergence of our proposed estimators, which can be used to construct confidence intervals and simultaneous confidence corridors for any individual eigenvalue and eigenfunction, as well as design uniform inference procedures for eigensystems with a diverging number of components. In addition, we extend our method to two-sample test problems. Extensive simulation results provide strong support for the asymptotic theory, and the procedures are applied to two potential seawater temperature data sets to demonstrate its validity.

Personalized assist-as-needed dressing assistance robot without human modeling using Rowat-Selverston CPG controller

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the ‘Assist-As-Needed’ (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology.

A hybrid PCA acceleration method for rapid real-time 2D MRI

Abstract Objective: To develop a 2D MR acceleration method utilizing Principal Component Analysis (PCA) in a hybrid fashion for rapid real-time applications. &amp;#xD;Approach: Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, Principal Components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting. &amp;#xD;Main Results: It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ≥ 0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ≥ 0.89 even at 8x accelerations which is on par with or better than intra-observer variation.&amp;#xD;Significance: This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ~50 ms/frame on Intel i5 CPU). &amp;#xD;

Forecasting solar irradiance for the strategic integration of hybrid hydro and solar photovoltaic systems in rural Indian regions

ABSTRACT Conversion of the conventional electrical grid into a smart and sustainable grid involves several considerations. The primary factors, however, are renewable energy penetration, associated storage systems, and energy generation costs. This research endeavors to conduct a thorough survey and analysis of the solar irradiance on various hydropower locations in India, including Run-of-River (RoR), Run-of-River with Pondage (RoRP), Reservoir Storage (S), Multi-Purpose Storage (MP) and Pumped Storage Systems (PSS). The hydroelectric projects in rural Indian regions have been the subject of the proposed case study. As a preliminary study, the probabilistic variables like minimum, maximum, and mean solar irradiance are calculated for 252 High-Scale Hydropower Plant locations (HSHPs) using the past 40 years day ahead solar radiation data to identify the high-irradiance hydropower plant location in each state of India. This study concludes that the maximum mean solar irradiance location in each state as these sites are well suited for hybrid PV-hydro systems. The identified high-irradiance locations 40 years day ahead data sets are analyzed employing 8 machine learning models and 2 deep learning models. This analysis aims to forecast solar irradiance, serving as a crucial foundation for the initial phase of the implementation of hybrid PV-hydro.

Facilitators and challenges of implementing a digital patient education programme for rheumatoid arthritis into clinical practice

BackgroundThe integration of telehealth interventions into clinical practice is frequently delayed, hindering the full adoption. Previously, we developed a digital patient education (PE) programme for self-management in rheumatoid arthritis (RA). While the programme design considered crucial factors to ensure the likelihood of success in clinical practice, there is a need for a systematic evaluation of implementation perspectives. The purpose of this study was to explore perspectives crucial to implementation of a digital PE programme in clinical practice.MethodsThe non-adoption, abandonment, scale-up, spread and sustainability (NASSS) framework was used to evaluate the successes and challenges of implementing the digital PE programme. We included a data set consisting of qualitative focus group discussions involving study nurses, rheumatologists, and leaders from rheumatology departments. Data analysis was guided by a deductive content analysis approach. Further we included data from earlier studies pertaining to the programme’s implementation, comprising the programme development process, a randomized controlled trial evaluating the programme’s effectiveness, and a qualitative study exploring patients’ perspectives of the programme.ResultsFacilitators and challenges of importance to implementation of digital PE were identified. While a wide range of patients could benefit from using digital PE, future implementation should aim for an even broader group than those studied. Both patients and healthcare providers embraced the technology, and the fact that it did not require specific technical skills enhances its potential for success. However, offering digital PE should be based on individual assessments, and expanding its use will require organizational adjustments. An adaptable structure is needed to accommodate unforeseen care needs that may arise following the use of digital PE at home. There was indication of some reluctance among healthcare providers toward the programme shown by concerns about changing roles, which could impact the adoption of the program.ConclusionsThe design and ease of use of the technology, the program’s effectiveness, its availability, and the potential to release healthcare resources may encourage the implementation of digital patient education. Challenges associated with implementing this mode of care pertains to the condition and the patient population, user adoption of the technology, and the organization of patient education.Trial registrationThe study is registered by the Central Denmark Region Scientific Committee (no. 1-16-02-52-19).

Litigation Risk, Ownership Structure and Payout Policy: An Agency Theory Perspective

The study aims to investigate the relationship between litigation risk, ownership structure and payout policy in the light of agency theory. The study analyzed a data set of 690 non-financial Chinese firms spanning 2013–2021, by employing two-stage dynamic panel data methodology. The results reveal that litigation risk adversely impacts dividend payouts, while managerial ownership has a similar negative effect. Conversely, institutional and concentrated ownership exhibit positive associations with dividend yield. Furthermore, managerial and institutional ownership moderates the link between litigation risk and dividend payout ratio, while concentrated ownership strengthens this link. These findings confirm the presence of agency theory dynamics in emerging economies, providing valuable insights for corporate decision-makers, regulators and investors. By considering the moderating role of ownership structure, this research enhances our understanding of the multifaceted relationship between litigation risk and payout policy, facilitating better-informed strategic decisions and policy formulation. The study provides valuable implications for corporate managers and individual investors for constructing a diversified portfolio in the context of emerging economies.

Cultural heritage preservation in the digital age, harnessing artificial intelligence for the future: a bibliometric analysis

Purpose This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage. Design/methodology/approach This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials. Findings The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites. Practical implications The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration. Social implications This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage. Originality/value Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

A data‐assisted physics‐informed neural network (DA‐PINN) for fretting fatigue lifetime prediction

AbstractIn this study, we present for the first time the application of physics‐informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of fretting fatigue. We propose a data‐assisted PINN (DA‐PINN) for predicting fretting fatigue crack initiation lifetime. Unlike traditional PINN that solves partial differential equations for specific problems, DA‐PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy. The DA‐PINN method, employed in this study, consists of two main steps. First, damage parameters are obtained from the finite element method by using critical plane method, which generates a data set used to train an artificial neural network (ANN) for predicting damage parameters in other cases. Second, the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA‐PINN models, which predict fretting fatigue lifetime. The results demonstrate that DA‐PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed. Additionally, the choice of loss‐function methods in DA‐PINN models plays a crucial role in determining its performance.