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With the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience strategies.

The stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the decision-maker.

Firstly, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover conveniently.

The findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among distributors.

This study establishes blockchain as a pro-resilience technology. It advocates that organisations focus on investing in blockchain to enhance their visibility and recovery, as it effectively reduces absolute shortages and feelings of shortage unfairness while improving recovery and visibility.

To the best of the authors’ knowledge, this is a unique supply chain model study that integrates a technology such as blockchain directly as a binary variable in the model constraint equations while also focusing on resilience strategies, costs, risk aversion and shortage unfairness.

The purpose of this study is to develop a novel approach that addresses time-cost tradeoffs in repetitive construction projects while considering the uncertainty in activity durations and the risk preferences of planners.

Our study involves work in three aspects. Firstly, it employs triangular fuzzy numbers to represent activity durations in different units, which facilitates the management of scenarios characterized by limited historical data or the presence of ambiguous information. Secondly, it introduces a fuzzy chance-constrained programming model, which is aimed at minimizing the project budget while ensuring that the risks associated with cost overruns and schedule delays are confined to specified limits. Thirdly, it advances an enhanced genetic algorithm, integrating an electromagnetism-like mechanism and a scheduling repair process, to improve the efficiency and effectiveness of the optimization process.

A real-life street renovation project was analyzed to demonstrate the applicability of the proposed algorithm. The analysis explored three common types of risk preferences: risk-averse, risk-neutral and risk-loving. The results indicate that the proposed algorithm surpasses existing fuzzy repetitive scheduling methods in terms of risk management. It effectively generates schedules that align with the risk preferences of planners and provides worst-case estimates of project performance.

This research makes a significant contribution to the field by developing a fuzzy chance-constrained programming model and an associated optimization algorithm that is specifically designed for time-cost tradeoffs in repetitive construction projects. A key distinction is that this study considers the risk preferences of planners, which sets it apart from previously developed models. As a result, it provides a practical approach for effective risk management.

This study aims to solve the problems of large training sample size, low data sample quality, low efficiency of the currently used classical model, high computational complexity of the existing concern mechanism, and high graphics processing unit (GPU) occupancy in the current visualization software defect prediction, proposing a method for software defect prediction termed recurrent criss-cross attention for weighted activation functions of recurrent SE-ResNet (RCCA-WRSR). First, following code visualization, the activation functions of the SE-ResNet model are replaced with a weighted combination of Relu and Elu to enhance model convergence. Additionally, an SE module is added before it to filter feature information, eliminating low-weight features to generate an improved residual network model, WRSR. To focus more on contextual information and establish connections between a pixel and those not in the same cross-path, the visualized red as integer, green as integer, blue as integer images are inputted into a model incorporating a fused RCCA module for defect prediction.

Software defect prediction based on code visualization is a new software defect prediction technology, which mainly realizes the defect prediction of code by visualizing code as image, and then applying attention mechanism to extract the features of image. However, the challenges of current visualization software defect prediction mainly include the large training sample size and low sample quality of the data, and the classical models used today are not efficient, and the existing attention mechanisms have high computational complexity and high GPU occupancy.

Experimental evaluation using ten open-source Java data sets from PROMISE and five existing methods demonstrates that the proposed approach achieves an F-measure value of 0.637 in predicting 16 cross-version projects, representing a 6.1% improvement.

RCCA-WRSR is a new visual software defect prediction based on recurrent criss-cross attention and improved residual network. This method effectively enhances the performance of software defect prediction.

Purpose: This chapter identifies performance factors that have the strongest impact on companies’ sustainable outcomes and compares the obtained results across different sectors.

Methodology: About 3,384 observations were gathered from 2015 to 2022 from companies in communication services, energy, financials, real estate, and utilities sectors that comprise the ‘STOXX Global ESG Leaders Select 50’ index. The multiple regression model is constructed with companies’ ESG scores as dependent variables and independent variables representing operational, financial, and market performance.

Findings: Companies that tend to have higher operational and financial performance in the financial sector are more likely to have higher ESG performance. The financial performance results of companies showed the strongest statistically significant relationship with environmental and the weakest with governance scores.

Implications: Results benefit private and institutional investors aiming to create more sustainable portfolios. The obtained results indicate that these investors should focus on companies operating in the financial and energy sectors with higher performance results. Better ROE, ROA, and Tobin’s Q may have a negative impact on sustainable outcomes for companies operating in the real estate and utility sectors.

Limitations: Firstly, not all ESG index providers disclose information about their index constituents. Secondly, within the chosen ‘STOXX Global ESG Leaders Select 50’ index, not all constituents had complete ESG data available on the Bloomberg platform. When selecting the analysis period, it was observed that the accessible ESG data on Bloomberg covers a relatively short time span, only from 2015 onwards.

Future research: A larger number of companies by choosing a more comprehensive available ESG index.

There is a dearth of research in the field of social marketing that attempts to understand why consumers prefer to use plastic packages when using online food delivery services. In addressing this issue, this study aims to investigate the role of moral disengagement, myopia and environmental apathy in the young generations' intentions to use plastic bags while ordering food online. It also examines the mediating role of moral disengagement and the moderating role of guilt in the context of the online food delivery service industry in China.

An online survey was designed to collect data, which yielded 256 usable responses. The partial least squares structural equation modelling (PLS-SEM) technique (SmartPLS 4.0) was used to test the study hypotheses.

The results indicate that environmental apathy, myopia and moral disengagement exert significant negative effects on consumer intention to use plastic. In addition, moral disengagement was able to mediate the links between “environmental apathy”, “myopia” and “plastic usage intention”. Lastly, consumers’ guilt was found to be a significant moderator in the link between moral disengagement and plastic usage intention.

This research holds significant importance for social marketers in the online food delivery service industry. Particularly, by understanding consumers' negative behavioural aspects, social marketers can implement marketing strategies that emphasise green practices for environmental well-being.

This is a pioneer study that focuses on the negative aspects of consumer behaviour, such as myopia, environmental apathy and moral disengagement, to understand what drives young consumers to use plastic. Additionally, this study investigates several new relationships in the social marketing field, such as the mediating effect of moral disengagement between myopia, environmental apathy and plastic usage intention. It also tests the moderating effect of guilt on the link between moral disengagement and use intention.

This research focuses on the controlling irreversibilities in a radiative, chemically reactive electromagnetohydrodynamics (EMHD) flow of a nanofluid toward a stagnation point. Key considerations include the presence of Ohmic dissipation, linear thermal radiation, second-order chemical reaction with the multiple slips. With these factors, this study aims to provide insights for practical applications where thermal management and energy efficiency are paramount.

Lie group transformation is used to revert the leading partial differential equations into nonlinear ODE form. Hence, the solutions are attained analytically through differential transformation method-Padé and numerically using the Runge–Kutta–Fehlberg method with shooting procedure, to ensure the precise and reliable determination of the solution. This dual approach highlights the robustness and versatility of the methods.

The system’s entropy generation is enhanced by incrementing the magnetic field parameter (M), while the electric field (E) and velocity slip parameters (ξ) control its growth. Mass transportation irreversibility and the Bejan number (Be) are significantly increased by the chemical reaction rate (C_r). In addition, there is a boost in the rate of heat transportation by 3.66% while 0.05⩽ξ⩽0.2; meanwhile for 0.2⩽ξ⩽1.1, the rate of mass transportation gets enhanced by 12.87%.

This paper presents a novel approach to analyzing the entropy optimization in a radiative, chemically reactive EMHD nanofluid flow near a stagnation point. Moreover, this research represents a significant advancement in the application of analytical techniques, complemented by numerical approaches to study boundary layer equations.

This study provides a bibliometric analysis of business literature on the interlinks of fintech, climate risks, and sustainable finance. Fintech growth promotes national environmental efficiency and green finance by decreasing carbon intensity toward the net-zero target. National fintech growth moderates the impact of environmental, social, and governance investment on bank efficiency. Fintech mitigates the loan bankruptcy risk imposed by climate risks with strict mortgage lending decisions due to climate concerns. Fintech applications in banking systems optimize financing costs and increase the accessibility of money for firms, decreasing corporate greenwashing behaviors and promoting green innovation. The existing literature leaves room for future studies on fintech to promote climate finance with important policies for climate action toward Sustainable Development Goals.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

As a result of environmentally conscious production requirements in the world, the concept of disassembly has been a focus of interest by researchers and practitioners over the last two decades. Disassembly is an important process in circular economy to recover and reuse of parts and materials. End-of-life and large-sized products such as minibuses and trucks may be disassembled on two-sided lines. The ability of using both right and left sides of two-sided lines may increase line efficiency and reduce space requirements across the line. This paper aims to address a two-sided disassembly line balancing problem (TSDLBP), which deals with assigning disassembly tasks, various equipments and assistants to the workstations to maximize total net recovery profit of the line.

A detailed explanation of the TSDLBP is first presented in the paper. A new 0–1 integer linear programming model is then proposed for the TSDLBP, aiming at maximizing total net recovery profit from disassembly of products. A set of test problems is generated, and an experimental analysis is conducted to make a comparison between traditional one-sided disassembly lines (TOSDL) and two-sided disassembly lines by means of performance improvement rate.

Optimal results are obtained in 132 (81.48%) out of 162 the TOSDL balancing problems, while 92 (56.79%) out of 162 the TSDLBP using the proposed model. Total net recovery profits are compared on 88 problems for which optimal solutions are obtained in both the TOSDL and the TSDLBP. Results showed that implementing two-sided disassembly lines provides 29.18% increment in total net recovery profit compared to the TOSDL. Furthermore, the effects of different parameter levels on the net recovery profit are analyzed using two-way analysis of variance. According to the results, implementing two-sided disassembly line configuration increases total net recovery profit of the line significantly compared to traditional disassembly line configuration.

The use of disassembly lines has become essential because of increasing consumption that results in a huge number of end-of-life products in the world. Two-sided disassembly lines may be preferred for dismantling large-sized products due to their high disassembly capacity and fewer space requirements. This paper proposes a new mathematical model for disassembly line balancing problem. The proposed model differs from the existing models by means of efficiently assigning limited disassembly resources as well as assigning disassembly tasks to the workstations to maximize total net recovery profit of the production system. The model allows decision-makers to consider several resource limitations when balancing their disassembly lines. The paper also provides a comprehensive experimental study to compare traditional and two-sided disassembly lines by means of profitability of disassembly processes.

This paper aims to investigate the problem of vision based autonomous laparoscope control, which can serve as the primary function for semi-autonomous minimally invasive surgical robot system. Providing the surgical gesture recognition information is a fundamental key component for enabling intelligent context-aware assistance in autonomous laparoscope control task. While significant advances have been made in recent years, how to effectively carry out the efficient integration of surgical gesture recognition and autonomous laparoscope control algorithms for robotic assisted minimally invasive surgical robot system is still an open and challenging topic.

The authors demonstrate a novel surgeon in-loop semi-autonomous robotic-assisted minimally invasive surgery framework by integrating the surgical gesture recognition and autonomous laparoscope control tasks. Specifically, they explore using a transformer-based deep convolutional neural network to effectively recognize the current surgical gesture. Next, they propose an autonomous laparoscope control model to provide optimal field of view which is in line with surgeon intra-operation preferences.

The effectiveness of this surgical gesture recognition methodology is demonstrated on the public JIGSAWS and Cholec80 data sets, outperforming the comparable state-of-the-art methods. Furthermore, the authors have validated the effectiveness of the proposed semi-autonomous framework on the developed HUAQUE surgical robot platforms.

This study demonstrates the feasibility to perform cognitive assistant human–robot shared control for semi-autonomous robotic-assisted minimally invasive surgery, contributing to the reference for further surgical intelligence in computer-assisted intervention systems.