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It is expected that two-dimensional (2D) metal nitrides (MNs) consisting of the 13th group elements of the periodic table and nitrogen, namely aluminium nitride (AlN), gallium nitride (GaN), indium nitride (InN) and thallium nitride (TlN), have enhanced physical and mechanical properties due to the honeycomb, graphene-like atomic arrangement characteristic of these compounds. The basis for the correct design and improved performance of nanodevices and complex structures based on 2D MNs from the 13th group is an understanding of the mechanical response of their components. In this context, a comparative study to determine the elastic properties of metal nitride nanosheets was carried out making use of the nanoscale continuum modelling (or molecular structural mechanics) method. The differences in the elastic properties (surface shear and Young's moduli and Poisson's ratio) found for the 2D 13th group MNs are attributed to the bond length of the respective hexagonal lattice of their diatomic nanostructure. The outcomes obtained contribute to a benchmark in the evaluation of the mechanical properties of AlN, GaN, InN and TlN monolayers using analytical and numerical approaches. [ABSTRACT FROM AUTHOR]

Most humanitarian logistics research focuses on immediate response efforts, leaving a gap regarding the long-term socio-economic impacts of post-tragedy financial aid. Our research investigates the Brumadinho landslide tragedy in Minas Gerais, Brazil, analyzing the effectiveness of financial aid in fostering sustainable recovery and resilience in affected communities. We employ a hybrid multi-criteria decision-making (MCDM) and machine learning model to quantitatively assess the socio-economic impact on affected municipalities. Using social responsibility indices from official state government datasets and data from the PTR transparency initiative—a financial aid program determined by the Judicial Agreement for Full Reparation and operationalized by FGV Projetos, which allocates USD 840 million for the reparation of damages, negative impacts, and socio-environmental and socio-economic losses—our analysis covers all municipalities in Minas Gerais over 14 years (10 years before and 4 years after the tragedy). We determine a final socio-economic performance score using the max entropy hierarchical index (MEHI). Additionally, we assess the efficiency of the PTR financial aid in affected municipalities through examining MEHI changes before and after the transfers using a difference-in-differences (DiD) approach. Our findings reveal both direct and indirect impacts of the tragedy, the efficacy of financial aid distribution, and the interplay of various socio-economic factors influencing each municipality's financial health. We propose policy recommendations for targeted and sustainable support for regions still coping with the long-term repercussions of the Brumadinho landslide. [ABSTRACT FROM AUTHOR]

Accepted by: Prof. Ali Emrouznejad Non-parametric data envelopment analysis (DEA) is susceptible to the curse of dimensionality, a challenge that can be mitigated through the use of the multi-criteria decision-making (MCDM) method. Conversely, DEA can overcome the limitations of the MCDM method by defining the weights of the decision-making unit to calculate the data envelop. This study addresses this issue by introducing a novel model, the Trigonometric Envelopment Analysis for Ideal Solutions (TEA-IS). TEA-IS combines DEA and the Technique for Order Performance by Similarity to Ideal Solution approaches. The proposed method is employed to assess the efficiency and performance of 367 Chinese banks over a 19-year period using various financial variables. The TEA-IS model leverages machine learning techniques to predict positive or negative outcomes for Chinese banks, taking into account various influencing factors. Our results indicate that TEA-IS scores demonstrate superior discriminatory power and reliability compared with non-parametric and MCDM methods. Furthermore, our findings reveal the presence of synergy amongst Chinese banks and illustrate a pattern of volatility in the Chinese banking industry's performance. Notably, performance improved from 2000 to 2005, declined during the period from 2006 to 2013 and subsequently experienced a recovery until 2018. The majority of Chinese banks in the sample are categorized as medium performers with lower synergy levels. Additionally, the study underscores the positive impact of bank listing and age on bank performance, suggesting that regional banks outperform domestic ones. [ABSTRACT FROM AUTHOR]

Utilizing Multi-Criteria Decision Analysis (MCDA) methods based on environmental, social, and governance (ESG) factors to rank countries according to these criteria aims to evaluate and prioritize countries based on their performance in environmental, social, and governance aspects. The contemporary world is influenced by a multitude of factors, which consequently impact our lives. Various models are devised to assess company performance, with the intention of enhancing quality of life. An exemplary case is the ESG framework, encompassing environmental, social, and governmental dimensions. Implementing this framework is intricate, and many nations are keen on understanding their global ranking and avenues for enhancement. Different statistical and mathematical methods have been employed to represent these rankings. This research endeavors to examine both types of methods to ascertain the one yielding the optimal outcome. The ESG model comprises eleven factors, each contributing to its efficacy. We employ the Performance Contribution Analysis (PCA), Clifford algebra method, and entropy weight technique to rank these factors, aiming to identify the most influential factor in countries' ESG-based rankings. Based on prioritization results, political stability (PSAV) and the voice of accountability (VA) emerge as pivotal elements. In light of the ESG model and MCDA methods, the following countries exhibit significant societal impact: Sweden, Finland, New Zealand, Luxembourg, Switzerland, Denmark, India, Norway, Canada, Germany, Austria, and Australia. This research contributes in two distinct dimensions, considering the global context and MCDA methods employed. Undoubtedly, a research gap is identified, necessitating the development of a novel model for the comparative evaluation of countries in relation to prior studies. [ABSTRACT FROM AUTHOR]

In recent years, tubular nanostructures have been related to immense advances in various fields of science and technology. Considerable research efforts have been centred on the theoretical prediction and manufacturing of non-carbon nanotubes (NTs), which meet modern requirements for the development of novel devices and systems. In this context, diatomic inorganic nanotubes formed by atoms of elements from the 13th group of the periodic table (B, Al, Ga, In, Tl) and nitrogen (N) have received much research attention. In this study, the elastic properties of single-walled boron nitride, aluminium nitride, gallium nitride, indium nitride, and thallium nitride nanotubes were assessed numerically using the nanoscale continuum modelling approach (also called molecular structural mechanics). The elastic properties (rigidities, surface Young's and shear moduli, and Poisson's ratio) of nitride nanotubes are discussed with respect to the bond length of the corresponding diatomic hexagonal lattice. The results obtained contribute to a better understanding of the mechanical response of nitride compound-based nanotubes, covering a broad range, from the well-studied boron nitride NTs to the hypothetical thallium nitride NTs. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018. Design/methodology/approach: This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores. Findings: The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity. Originality/value: To the best of the authors' knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it. [ABSTRACT FROM AUTHOR]

This paper examines the performance evaluation and lockdown drivers of the UK's NHS (National Health Service) during the COVID-19 pandemic. The study aims to enhance the NHS's response to future health crises and guide government lockdown decisions. Lockdown drivers encompass vital resources like beds, ventilators, patients, and staff. A three-stage MCDM (Multiple-Criteria Decision Making) approach is employed to analyze performance scores. First, partial utility functions or partial distances are computed using COPRAS (Complex Proportional Assessment) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), respectively. Second, the Latent Vagueness and Randomness Components (LAVRA) method filters unbiased performance scores from uncertain components. Third, a bootstrapped neural network regression classifies lockdown drivers based on performance, deaths, and geographic regions. Crucial drivers relate to ventilated bed availability, while less critical ones include staff absence due to COVID-19 and a high admission rate of elderly inpatients. The results indicate performance scores range from 0.65 to 0.75 using TOPSIS, while COPRAS analysis significantly reduces the scores. Lockdown decisions are influenced by geographic regions, death tolls, and unbiased hospital performance scores. [ABSTRACT FROM AUTHOR]

For meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two‐stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi‐activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre‐process and industry have a significant and positive impact on energy efficiency in China. [ABSTRACT FROM AUTHOR]