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Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the industrial internet. However, a gap persists in the existing research regarding the strategies and methods available to small and medium-sized enterprises (SMEs) for executing business model innovation. Therefore, this paper aims to explore the connotation, characteristics and logic of business model innovation for SMEs in the industrial internet era.

To explore the business model innovation logic of small and medium-sized enterprises in the era of industrial internet, the paper adopts a longitudinal single-case study approach, with PAYA, a medium-sized enterprise in the electromechanical industry, serving as the subject of research. It systematically analyzes PAYA’s business model innovation, centering on four key elements of the business model: value proposition, value creation, value delivery and value capture.

The study proposes two types of business model innovation, namely, “Migration” and “Expansion”, and explains the logic of business model innovation for SMEs in the industrial internet era: faced with a rapidly changing market environment, entrepreneurs put forward the value proposition through the insight of the market environment, then enterprises conduct technological innovation to support the value creation by their own unique experience and knowledge, and then improve the legitimacy of the market by expanding the influence of market acceptance of the new business model to promote the value delivery, and finally capture the economic value and ecological value.

The types and logic of business model innovation proposed in this paper contribute to supplementing and developing the theory of business model innovation and meanwhile have important reference value for SMEs in the industrial internet era.

In a typical problem of allocating a fixed resource across a set of decision-making units (DMUs), the decision maker will decide how much resource to allocate to each DMU. Real-world decision-making contexts frequently pose challenges in precisely assessing each DMU’s performance on distinct parameters and in ascertaining the relative importance of these parameters, since resource allocation often precedes the decision-making process. Conventional decision-making approaches fall short in effectively addressing such complexities. In this paper, the authors propose a stochastic multi-attribute acceptability analysis (SMAA)-based model that can deal with the resource allocation problem with uncertain attribute values and unknown attribute weights.

The SMAA-based model first regards the resource to be allocated as an evaluation attribute and then proposes a fair resource allocation solution that minimizes the gap between the holistic acceptability of each DMU. To address the optimization of the SMAA-based model, two heuristic strategies were devised: a modified particle swarm optimization (PSO) approach and an adaptive algorithm, both designed to yield acceptable outcomes within a constrained time frame.

The resource allocation model based on SMAA can provide an effective analytical framework for the resource allocation problem of different DMUs in uncertain environments. Meanwhile, the PSO algorithm and adjustment algorithm proposed in this paper can help decision-makers quickly find a fair resource allocation plan.

Primarily, this study introduces a novel approach rooted in SMAA for tackling resource allocation dilemmas where attribute values are uncertain and weight information is absent. Secondly, a fairness-centric strategy that minimizes the disparity in overall acceptability among various DMUs is presented. Thirdly, two heuristic algorithms, namely, PSO algorithm and adjustment algorithm, are developed to solve the problem.

Service robots with advanced artificial intelligence (AI) can collect data on customer preferences, understand complex requests, improve services, and tailor marketing strategies. This study examined how perceived relatedness, perceived warmth, and customer–AI-assisted exchanges (CAIX) of service robots affect customer service competencies and brand love through service-robot intimacy.

A brand love model was developed based on the AI device using acceptance and an emotional perspective. Data were collected from customers who had dined in robot restaurants; 415 questionnaires were completed, and partial least squares analysis was adapted to the proposed model.

The results demonstrate that the perceived relatedness, perceived warmth and CAIX of service robots affect the intimacy of robot restaurants. Customers who feel friendly and satisfied with a restaurant’s service robot will recommend it to their friends.

This study draws on theory and existing literature to identify principal factors in robot restaurant service capabilities. Future research can include service robot data analysis capabilities and adoption process factors as the direction of customer relationship management research while also exploring the influence of AI computing on restaurant supply chains. Likewise, the agility of service robots in the stages of innovation can be discussed in future research based on different theories, which will bridge unique insights.

The findings of this study emphasize the relationship between service robots and restaurant brand love and propose specific practice areas for restaurants.

This study expands the main issue of current brand love research from traditional restaurant operations to the novel field of humanoid service robot restaurants. It enriches our understanding of how consumers’ emotional fondness for a brand affects their behavioural intentions.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

Air pollution poses a significant global threat to both human health and environmental stability, acknowledged by the World Health Organization as a leading cause of non-communicable diseases (NCDs) and a notable contributor to climate change. This chapter offers a comprehensive review of the impacts of air pollution on health, highlighting the complex interactions with genetic predispositions and epigenetic mechanisms. The consequences of air pollution to health are extensive, spanning respiratory diseases, cardiovascular disorders, adverse pregnancy outcomes, neurodevelopmental disorders, and heightened mortality rates. Genetic factors play a pivotal role in shaping individual responses to air pollution, influencing susceptibility to respiratory illnesses and the severity of symptoms. Additionally, epigenetic changes triggered by exposure to pollutants have been linked to respiratory health issues, cancer development and progression, and even transgenerational effects spanning multiple generations. As countries, including the UK, pursue ambitious targets for reducing emissions, ongoing research into the complex interplay of air pollution, genetics, and epigenetics is essential. By unravelling the underlying mechanisms and advancing preventive and therapeutic strategies, we can protect public health and promote sustainable environmental practices in the face of this pervasive global challenge.

The purpose of this paper is to study the anodic dissolution processes of alloy 690 in NaCl + Na₂S₂O₃ solutions by using digital holography.

The digital holography technique was used to in situ observe the dynamic processes occurring at the electrode|electrolyte interface during the anodic dissolution of alloy 690 in NaCl + Na₂S₂O₃ solutions, both in the presence and absence of a magnetic field (MF).

In 3.5% NaCl + 0.01 M Na₂S₂O₃ solutions, MF inhibited intergranular corrosion (IGC) because it increased the defects in the oxide film and facilitated the uniform adsorption of low concentration of S on these defects due to its stirring effects, which resulted in a weakened adsorption of S at the grain boundaries. Conversely, in 3.5% NaCl + 0.1 M Na₂S₂O₃ solutions, MF promoted IGC by increasing the number of defects in the oxide film, with lots of S species preferentially adsorbing at the grain boundaries. The resultant salt films formed more readily, inhibiting the formation of the oxide film at the grain boundaries.

Through the use of digital holography, it was possible to in situ observe the initiation of IGC at a single grain boundary and its progression to adjacent grain boundaries, regardless of the presence or absence of MF.

This study investigates the impact and role of digital twin technology in building automation (DTBA) from a sustainability viewpoint. It aims to enhance the understanding of how DTBA can boost efficiency, optimize quality and support sustainable practices in contemporary construction. By exploring the integration of DTBA with sustainable practices, the study seeks to demonstrate how DT can revolutionize building management and operations, leading to significant improvements in resource efficiency, environmental impact and overall operational excellence.

This research employs a bibliographic analysis and systematic review of 176 publications from the past five years (January 1, 2019 to December 31, 2023), focusing on the application and development of DTBA. The study methodically analyzes current trends, identifies research gaps and suggests future directions by synthesizing data from various studies, offering a comprehensive overview of the current state of DTBA research. The approach combines quantitative and qualitative analyses to provide robust insights into the advancements and challenges in the field.

The review identifies key development areas in DTBA, such as energy and environmental management, resource utilization within a circular economy and technology integration and interoperability. It highlights the necessity for further research to maximize DTBA’s potential in sustainable building automation. The findings suggest that while significant progress has been made, there is a critical need for innovations in data interoperability, predictive analytics and the integration of renewable energy sources to fully realize the benefits of DTBA in enhancing building sustainability.

This paper provides a thorough review of DTBA from a sustainability perspective, offering valuable insights into its current applications and future development potential. It serves as a crucial resource for researchers and practitioners looking to advance sustainable practices in the construction sector using DT technology. By bridging the gap between theoretical research and practical applications, the paper underscores the transformative potential of DTBA in driving sustainable development and provides a roadmap for future research and innovation in the field.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

This study aims to explore how perceived anthropomorphism, perceived warmth, and customer–artificial intelligence (AI) assisted exchange (CAIX) of service robots affect customers’ satisfaction via digital marketing innovation.

A customer satisfaction model was formulated based on the perspective of parasocial relationships and hybrid intelligence; 236 completed questionnaires were returned by partial least squares structural equation modeling analysis.

This study demonstrates that perceived anthropomorphism, perceived warmth and CAIX's impact on digital marketing innovation were supported, and customer satisfaction impacted the continued intention to use service robots.

Restaurants that leverage service robots differentiate themselves from competitors by offering innovative and technologically advanced dining experiences. Integrating AI capabilities sets these restaurants apart and attracts tech-savvy customers who value convenience and efficiency.