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Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

This study aims to explore the influence of corporate sustainability on organizational value, specifically focusing on companies ranked in the Just Capital Market ranking. The aim is to establish whether higher sustainability rankings are associated with increased firm value and to investigate how corporate social responsibility (CSR) activities affect both financial and non-financial outcomes.

This study uses the Ohlson model to assess the value-generation potential of the top and bottom ten companies in the Just Capital Market ranking from 2013 to 2018. The analysis involves evaluating stock prices and other financial metrics and incorporating non-financial indicators related to CSR activities to gain a comprehensive understanding of their impact on firm valuation.

The results indicate a strong connection between high sustainability rankings and increased market value. Companies such as Microsoft, Intel and Alphabet, which have robust CSR initiatives, have shown significant improvements in market performance due to greater stakeholder engagement and detailed non-financial disclosures. On the other hand, companies with low sustainability ratings have demonstrated weaker market performance, which indicates the financial risks associated with neglecting CSR activities. This study underscores the critical importance of integrating CSR into fundamental business strategies to create sustainable value.

This study addresses the limitations of traditional financial indicators by incorporating non-financial factors into the valuation process. The study offers a more comprehensive assessment of firm value, reflecting modern business practices and the evolving global economy landscape. Integrating nonfinancial indicators enhances valuation accuracy and provides a holistic view of company performance, enabling stakeholders to make informed decisions based on a broader range of factors. This innovative method may reshape firm valuations, leading to more accurate and reliable assessments in contemporary business contexts.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

With the global market for advanced technology-driven customer service set to soar, understanding the complicated relationship between advanced technology and customer purchase behaviour is paramount. While prior research has touched upon the impact of technology on purchase processes in some aspects, this study investigates the specific features of advanced technology that shape customer purchase intention in greater depth. By investigating when and under what conditions customers choose advanced technology-based purchases, this research sheds light on the evolving landscape of consumer decision-making and it seeks to quantify the transformative power of advanced technology in driving customer purchase intentions.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

There is consensus among scholars on how political institutional imprinting interprets the unique management and practice phenomenon of Chinese enterprises. However, little scholarly attention has been given to the different political institutional imprints that shape firms’ internationalization. Therefore, this study aims to investigate how communist and market logic political institutional imprintings influence firms’ initial ownership strategies in outward foreign direct investment.

Based on the propensity score matching difference in difference method and a sample of 464 foreign investments from 2009 to 2020 for 310 Chinese private firms.

The results show that private firms with market logic political institutional imprintings tend to adopt higher ownership and vice versa. As institutional differences increase, private firms with market logic imprintings are more risk-taking and adopt higher ownership, whereas private firms with communist imprintings are more conservative and choose lower ownership. When diplomatic relations are friendlier, private firms with market logic imprintings prefer higher ownership to grasp business opportunities and vice versa.

This study not only identifies the net effect of political institutional imprinting on private firms’ initial ownership strategy but also investigates the different moderating effects of current institutional forces to respond to the call for research on bringing history back into international business research and the fit between imprinting and the environment.

Artificial intelligence (AI) carries the risk of widening gender inequalities due to the digital divide, while simultaneously promising to equalise the situation for women through the gender digital dividend. The conflicting findings from previous studies justify the need to investigate the gendered aspects of Artificial Intelligence (AI) diffusion. Specifically, the aim of this chapter is to understand the relationship between female entrepreneurship and the adoption of AI technologies within business contexts at the macroeconomic level. To achieve this, cluster analyses are conducted for the European Union (EU) countries. The results indicate an inverted U-shaped pattern in the relationship between the level of female entrepreneurship and the diffusion of AI technology in business. In the EU countries belonging to clusters with the highest level of AI diffusion, female entrepreneurship is at a moderate level, while in the EU countries with the lowest level of intelligent transformation, both extremes are observed: the highest and the lowest levels of female entrepreneurship. The variety of patterns in female entrepreneurship and AI technology spread in the EU countries implies the complex and multidimensional nature of the interrelationship, and, thus, it indicates the need for diverse, country-specific policies and practices to reach the intelligent transformation with respect to more equal society.

The paper presents the findings of a study assessing the progress of implementing the European Green Deal (EGD) strategy goals across the EU-27 countries. The research aimed to evaluate individual countries' implementation of the strategy, considering its multidimensional nature.

A research methodology was devised, incorporating 18 indicators that characterize various dimensions pertinent to the EGD strategy. Evaluation of the strategy’s goals relied on the European Green Deal Index (EGDI), determined using the combined compromise solution (CoCoSo) method and a hybrid approach to weigh the indicators. Three analytical methods – criteria importance through intercriteria correlation (CRITIC), statistical variance, equal weights – and the Laplace criterion were utilized to ascertain the final weights of these indicators. The EGDI values for the years under scrutiny (2019–2021) served as the basis for assessing the EU-27 countries' progress towards the goals of the EGD.

The survey results indicate that from 2019 to 2021, the highest EGDI values – exceeding 2 – were achieved by Sweden, Denmark and the Netherlands. Austria also recorded very strong results. In contrast, the “new EU-13” countries generally exhibited lower levels of implementation of the EGD, as reflected in their EGDI values. Bulgaria and Cyprus, in particular, had the weakest results over the study period, with EGDI values below 1.5. Consequently, the “old EU-14” countries performed significantly better in implementing the EGD compared to the “new EU-13” countries. Among the “old EU-14” countries, Ireland recorded the weakest performance.

The originality of the research is highlighted by several key factors. Firstly, it addresses a significant research gap by assessing the initial positions and efforts of EU countries toward the EGD goals, providing a benchmark for effectiveness and strategy development. Secondly, it pioneers an authoritative and universal multi-criteria evaluation approach through the Green Deal Index (GDI), offering a robust methodology for assessing EGD implementation. Lastly, the study’s holistic approach incorporates energy, environmental and socioeconomic dimensions, significantly expanding knowledge and contributing to informed decision-making and policy formulation.

This study aims to tackle control challenges in soft robots by proposing a visually-guided reinforcement learning approach. Precise tip trajectory tracking is achieved for a soft arm manipulator.

A closed-loop control strategy uses deep learning-powered perception and model-free reinforcement learning. Visual feedback detects the arm’s tip while efficient policy search is conducted via interactive sample collection.

Physical experiments demonstrate a soft arm successfully transporting objects by learning coordinated actuation policies guided by visual observations, without analytical models.

Constraints potentially include simulator gaps and dynamical variations. Future work will focus on enhancing adaptation capabilities.

By eliminating assumptions on precise analytical models or instrumentation requirements, the proposed data-driven framework offers a practical solution for real-world control challenges in soft systems.

This research provides an effective methodology integrating robust machine perception and learning for intelligent autonomous control of soft robots with complex morphologies.