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This paper aims to explore the ambiguity and limitations of measuring firm-level multinationality (FLM) using theoretical and empirical comparisons of existing methods. The paper puts forward a list of five key aspects that collectively serve as a tool for researchers to select the most appropriate method for future research and as a basis for the future development of methods.

Firstly, the author reviews existing methods of measuring FLM and consolidates findings into five key aspects. Secondly, the author uses the aspects to compare existing methods theoretically, and subsequently, the author groups them into three distinct streams. Thirdly, the author compares existing methods across a sample of the 35 largest European MNEs by sales in 2020 to identify and demonstrate the ambiguity and limitations of these methods.

The author identifies the five key aspects of measuring FLM: framework, aggregation, segmentation, metrics and indicators. Using empirical comparison, the author empirically confirms the limitations highlighted in the literature and shows the differences and inconsistencies among methods, which cause confusion rather than clarity in the extant literature. Additionally, the author emphasises that three distinct streams further drive the debate on the regional/global nature and present further limitations of methods not mentioned in the literature to date.

This paper provides the most comprehensive review of the existing literature on FLM, resulting in five novel aspects of measuring FLM. The analysis of a sample of 35 European firms demonstrates and identifies the ambiguity and limitations of FLM-measuring methods.

This paper aims to derive a reduced-order model for the heat transfer across the interface between a millimetric thermocapillary liquid bridge from silicone oil and the surrounding ambient gas.

Numerical solutions for the two-fluid model are computed covering a wide parametric space, making a total of 2,800 numerical flow simulations. Based on the computed data, a reduced single-fluid model for the liquid phase is devised, in which the heat transfer between the liquid and the gas is modeled by Newton’s heat transfer law, albeit with a space-dependent Biot function Bi(z), instead of a constant Biot number Bi.

An explicit robust fit of Bi(z) is obtained covering the whole range of parameters considered. The single-fluid model together with the Biot function derived yields very accurate results at much lesser computational cost than the corresponding two-phase fully-coupled simulation required for the two-fluid model.

Using this novel Biot function approach instead of a constant Biot number, the critical Reynolds number can be predicted much more accurately within single-phase linear stability solvers.

The Biot function for thermocapillary liquid bridges is derived from the full multiphase problem by a robust multi-stage fit procedure. The derived Biot function reproduces very well the theoretical boundary layer scalings.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This study aims to investigate the behavior of different types of stone columns, including the short and floating columns, as well as the ordinary and the geosynthetic encased stone columns (OSC and GESC). The effectiveness of the geosynthetic encasement and the impact of the installation using the lateral expansion method on the column performance is evaluated through a three-dimensional (3D) unit cell numerical analysis.

A full 3D numerical analysis is carried out using the explicit finite element code PLAXIS 3D to examine the installation influence on settlement reduction (ß), lateral displacement (U_x) and vertical displacement (U_z) relative to different values of lateral expansion of the column (0% to 15%).

The findings demonstrate the superior performance of GESC, particularly short columns outperforming floating counterparts. This enhanced performance is attributed to the combined effects of geosynthetic encasement and increased lateral expansion. Notably, these strategies contribute significantly to decreasing lateral displacement (U_x) at the column’s edge and reducing vertical displacement (U_z) under the rigid footing.

In contrast to previous studies that examined the installation effect of OSC contexts, this paper presents a comprehensive investigation into the effect of geosynthetic encasement and the installation effects using the lateral expansion method in very soft soil, using 3D numerical simulation. The study emphasizes the significance of the consideration of geosynthetic encasement and lateral expansion of the column during the design process to enhance column performance.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

This paper addresses a significant research gap in the study of Rayleigh surface wave propagation within a piezoelectric medium characterized by piezoelectric properties, thermal effects and voids. Previous research has often overlooked the crucial aspects related to voids. This study aims to provide analytical solutions for Rayleigh waves propagating through a medium consisting of a nonlocal piezo-thermo-elastic material with voids under the Moore–Gibson–Thompson thermo-elasticity theory with memory dependencies.

The analytical solutions are derived using a wave-mode method, and roots are computed from the characteristic equation using the Durand–Kerner method. These roots are then filtered based on the decay condition of surface waves. The analysis pertains to a medium subjected to stress-free and isothermal boundary conditions.

Computational simulations are performed to determine the attenuation coefficient and phase velocity of Rayleigh waves. This investigation goes beyond mere calculations and examines particle motion to gain deeper insights into Rayleigh wave propagation. Furthermore, this investigates how kernel function and nonlocal parameters influence these wave phenomena.

The results of this study reveal several unique cases that significantly contribute to the understanding of Rayleigh wave propagation within this intricate material system, particularly in the presence of voids.

This investigation provides valuable insights into the synergistic dynamics among piezoelectric constituents, void structures and Rayleigh wave propagation, enabling advancements in sensor technology, augmented energy harvesting methodologies and pioneering seismic monitoring approaches.

This study formulates a novel governing equation for a nonlocal piezo-thermo-elastic medium with voids, highlighting the significance of Rayleigh waves and investigating the impact of memory.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

This paper aims to identify the effect of environmental management systems (EMSs), commitment to stakeholders and gender diversity on corporate environmental performance (CEP) and the extent to which an economic crisis moderates these relationships.

A regression analysis was conducted on a sample of 14,217 observations from 1,933 firms from 26 countries from 2002 to 2010. The estimator used is ordinary least squares with heteroscedastic panel-corrected standard errors (PCSEs), which allows us to obtain consistent results in the presence of heteroscedasticity and autocorrelation.

The results show that EMSs and stakeholder engagement are mechanisms that drive CEP but lose their effectiveness in times of crisis. However, the presence of women on boards has a positive effect on CEP that is not affected by an economic crisis.

The study has some limitations that could be addressed in the future. We present board gender diversity as a governance mechanism because its role is strongly related to non-financial performance. Future studies could focus on other corporate governance mechanisms, such as the presence of institutional or long-term investors. In addition, other mechanisms could be found that can counteract poor environmental performance in times of crisis. Finally, it might be useful to contrast these results with the crisis generated by the coronavirus pandemic.

The results obtained have important practical implications at the corporate and institutional levels. At the corporate level, they highlight, as essential contributions, that environmental management systems and stakeholder orientation are not effective in times of economic crisis, except for with the presence of women on the board.

Following the crisis, the European Commission has promoted gender diversity on boards as a mechanism to improve the governance of entities – improving, among other aspects, sustainability. In this sense, another one of the practical implications of the study is support for the policies that the European Union has implemented over the last two decades.

The paper analyses how a crisis affects the moral and cultural institutional mechanisms that promote CEP. Gender diversity on the board of directors not only promotes environmental performance but also appears to be a governance mechanism that ensures this performance in times of crisis when the other mechanisms lose their effectiveness. The study proposes specific policies that help maintain environmental performance in an economic crisis.

This study’s main objective is to explore the ISO 9001 implementation model and identify a future research agenda. This is important because not all organizations find it easy to implement ISO 9001, and not all organizations get positive benefits after implementing it.

The paper presents a comprehensive review of the literature on ISO 9001 implementation models using the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on ISO 9001 implementation models. Relevant studies published from 2003 to early 2023 are explored to reveal the research landscape, gaps and trends.

Many ISO 9001 implementation methods have been developed for actual implementation in organizations, including models, frameworks, special variable considerations, application uses and integration. These methods were developed and applied to cover gaps regarding constraints, unbeneficial, special conditions, implementation objectives and organization types in ISO 9001 implementation. Current issues and future research on ISO 9001 implementation models were found, namely ISO 9001 implementation models specific to SMEs, ISO 9001 implementation levels, ISO 9001 implementation models that are agile to change, and affordable certification models.

Only a few researchers have systematically reviewed the literature or taken a bibliometric approach in their analyses to provide an overview of the current trends and links to ISO 9001 implementation models. The ISO 9001 standard is a general standard and can be applied by all organizations with the implementation method left to the implementer. Many implementation methods have been developed, but several implementation obstacles and disadvantages are still found. It is important to know the extent of current research and discover future research gaps regarding methods of implementing the ISO 9001 standard.