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This paper aims to introduce a new target tracking method based on risk theory in a 2-D discrete environment. After that, the related sensor scheduling method is proposed. This can make up the blank of target tracking and sensor management in the 2-D discrete environment.

The definition of risk is proposed based on risk decision theory firstly. Then the target tracking model in a two-dimensional discrete environment is built. The motion state updating and estimation method of target’s motion state based on Bayes theory is given. Thirdly, the method of computing sensor emission interception risk is provided. Afterwards, the optimization rule of obtaining the minimum risk is followed to model the sensor scheduling objective function. The lion algorithm is adjusted and improved combined with Chaos theory to generate the optimal sensor management projects.

The risk-based sensor target tracking method and sensor management method are both effective in a 2-D discrete environment.

To the best of the authors’ knowledge, this paper is the first to study the target tracking method and sensor scheduling method in a 2-D environment. Furthermore, the lion algorithm is improved combined with Chaos theory to show a better optimization performance.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

There has been a substantial realization of the importance of community engagement (CE) for social enterprise (SE) missions. However, the emerging literature on CE in social enterprises is riddled with theoretical inconsistencies and there is universal lack of studies in this direction. For the sake of advancing the field, the purpose of this paper is to identify and analyze the elements of CE in SEs. Further, a comprehensive preliminary model is developed integrating the five fundamental dimensions of CE in SE setting.

The paper is based on the critical analysis of the extant literature across disciplines to identify the relevant themes that can affect the CE in SEs.

The paper develops a preliminary social enterprise community engagement model (SE-CEM), which results from a broad literature analysis. The proposed model attempts to offer a comprehensive perspective outlining the institutional, organizational, individual, community and process dimensions likely to predict engagement success in SE context.

This paper pioneers in presenting a holistic approach to understanding the dimensions that constitute CE in SEs. The SE-CEM incorporates the determinants of CE in social entrepreneurship (S-ENT) context to direct a future research agenda. The preliminary model can further be developed and empirically tested by researchers. Moreover, practitioners can also gain benefits from the model and promote S-ENT.

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.

Ecologically fit built form and settlement is an outcome of harmonious modes of interaction between the man-made and the natural environment. It is also the prerequisite of a sustainable planning and design process. In building terms, an ecologically fit building involves the existence of appropriate schemes of enclosures and exposures of indoor spaces which is achieved through fundamental building features like relations with the ground, properties of building mass, treatment of the building envelop and roof performance.

While the debate on the conflicting aspects of the two extremes, namely the international style and regionalism, continues to occupy architectural media, a new paradigm of place dependent ecological architecture has to be formulated. In this connection it is not surprising to notice that vernacular built form based on building traditions are ecologically fit and may well constitute a sound source of information. This study aims at deciphering relevant clues to be utilized as design guidelines through the analysis of fundamental building features of vernacular built environments.

The purpose of this paper is to discuss the reliability of board level Sn‐Ag‐Cu (SAC) solder joints in terms of both thermal cycling and drop impact loading conditions, and further modification of the characteristics of low Ag‐content SAC solder joints using minor alloying elements to withstand both thermal cycle and drop impact loads.

The thermal cycling and drop impact reliability of different Ag‐content SAC bulk solder will be discussed from the viewpoints of mechanical and micro‐structural properties.

The best SAC composition for drop performance is not necessarily the best composition for optimum thermal cycling reliability. The content level of silver in SAC solder alloys can be an advantage or a disadvantage depending on the application, package and reliability requirements. The low Ag‐content SAC alloys with different minor alloying elements such as Mn, Ce, Bi, Ni and Ti display good performance in terms of both thermal cycling and drop impact loading conditions.

The paper details the mechanical and micro‐structural properties requirements to design a robust bulk SAC solder joint. These properties provide design and manufacturing engineers with the necessary information when deciding on a solder alloy for their specific application.

The International Energy Agency states that the global energy intensity must reduce by 2.9% yearly before attaining Sustainable Development Goal 7.3 by 2030. However, the European Union (EU) seeking to attain a climate-neutral EU by 2050 shall require a substantial rate of reducing energy intensity. Consequently, this study aims to investigate how (clean) renewable energy, income, trade openness, technological innovation and nonrenewable energy consumption impact energy intensity for the EU countries.

The quantile regression, augmented mean group and causality techniques were used for analyses. Panel data for 26 EU nations over the 1990 and 2019 period was used.

The empirical evidence indicates that the variables have long-run equilibrium relationships. However, the analysis revealed that clean energy and income reduce energy intensity whiles trade, technological innovation and nonrenewable energy consumption increase energy intensity. An interactive term analysis shows that renewable energy and trade interact to reduce further, the negative effect of income on energy intensity. Causality results revealed a feedback connection between energy intensity and clean energy, income, trade liberalization as well as the interaction between income and trade liberalization. A one-way causality was obtained between energy intensity and technological innovation, nonrenewable energy consumption and the interaction between clean energy and income.

The results imply that EU countries stand to gain if more resources are committed to encouraging the production and consumption of cleaner/renewable energy. Advancement in policies that support renewable energy and facilitate green growth will help reduce energy intensity for the region. Trade policies that promote lower energy consumption should be strengthened.

The effect of renewable energy on energy intensity is assessed. The moderating impact of renewable energy and trade openness on the income–energy intensity relationship for the EU countries is examined. Moreover, this study uses the quantile estimation technique to assess the nonlinear effect of the explanatory variables on energy intensity.

The study aims to map the links between Industry 4.0 (I-4.0) technologies and circular economy (CE) for sustainable operations and their role to achieving the selected number of sustainable development goals (SDGs).

The study adopts a systematic literature review method to identify 76 primary studies that were published between January 2010 and December 2020. The authors synthesized the existing literature using Scopus database to investigate I-4.0 technologies and CE to select SDGs.

The findings of the study bridge the gap in the literature at the intersection between I-4.0 and sustainable operations in line with the regenerate, share, optimize, loop, virtualize and exchange (ReSOLVE) framework leading to CE practices. Further, the study also depicts the CE practices leading to the select SDGs (“SDG 6: Clean Water and Sanitation,” “SDG 7: Affordable and Clean Energy,” “SDG 9: Industry, Innovation and Infrastructure,” “SDG 12: Responsible Consumption and Production” and “SDG 13: Climate Action”). The study proposes a conceptual framework based on the linkages above, which can help organizations to realign their management practices, thereby achieving specific SDGs.

The originality of the study is substantiated by a unique I-4.0-sustainable operations-CE-SDGs (ISOCES) framework that integrates I-4.0 and CE for sustainable development. The framework is unique, as it is based on an in-depth and systematic review of the literature that maps the links between I-4.0, CE and sustainability.

This paper aims to analyze the effect of cerium addition on the microstructure and the mechanical properties of Tin-Silver-Copper (SAC) alloy. The mechanical properties and refined microstructure of a solder joint are vital for the reliability and performance of electronics. SAC305 alloys are potential choices to use as lead-free solders because of their good properties as compared to the conventional Tin-Lead solder alloys. However, the presence of bulk intermetallic compounds (IMCs) in the microstructure of SAC305 alloys affects their overall performance. Therefore, addition of cerium restrains the growth of IMCs and refines the microstructure, hence improving the mechanical performance.

SAC305 alloy is doped with various composition of xCerium (x = 0.15, 0.35, 0.55, 0.75, 0.95) % by weight. Pure elements in powdered form were melted in the presence of argon with periodic stirring to ensure a uniform melted alloy. The molten alloy is then poured into a pre-heated die to obtain a tensile specimen. The yield strength and universal tensile strength were determined using a fixed strain rate of 10 mm per minute or 0.1667 mm s^(−1). The IMCs are identified using X-ray diffraction, whereas the elemental phase composition and microstructure evolution are, respectively, examined by using electron dispersive spectroscopy and scanning electron microscopy.

Improvement in the microstructure and mechanical properties is observed with 0.15% of cerium additions. The tensile test also showed that SAC305-0.15% cerium exhibits more stress-bearing capacity than other compositions. The 0.75% cerium doped alloy indicated some improvement because of a decrease in fracture dislocation regions, but microstructure refinement and the arrangement of IMCs are not those of 0.15% Ce. Different phases of Cu_6 Sn_5, Ag_3 Sn and CeSn_3 and ß-Sn are identified. Therefore, the addition of cerium in lower concentrations and presence of Ce-Sn IMCs improved the grain boundary structure and resulted refinement in the microstructure of the alloy, as well as an enhancement in the mechanical properties.

Characterization of microstructure and evaluation of mechanical properties are carried out to investigate the different composition of SAC305-xCerium alloys. Finally, an optimized cerium composition is selected for solder joint in electronics.

The application of circular economy (CE) has received wide coverage in the built environment, including public-private partnership (PPP) infrastructure projects, in recent times. However, current studies and practical implementation of CE are largely associated with construction demolition, waste and recycling management. Few studies exist on circular models and success factors of public infrastructures developed within the PPP contracts. Thus, the main objective of this article is to identify the models and key success factors associated with CE implementation in PPP infrastructure projects.

A systematic review of the literature was undertaken in this study using forty-two (42) peer-reviewed journal articles from Scopus, Web of Science, Google Scholar and PubMed.

The results show that environmental factors, sustainable economic growth, effective stakeholder management, sufficient funding, utilization of low-carbon materials, effective supply chain and procurement strategies facilitate the implementation of CE in PPP infrastructure projects. Key CE business models are centered around the extension of project life cycle value, circular inputs and recycling and reuse of projects.

Although the study presents relevant findings and gaps for further investigations, it has a limited sample size of 42 papers, which is expected to increase as CE gain more prominence in PPP infrastructure management in future.

The findings are relevant for decision-making by PPP practitioners to attain the social, economic and environmental benefits of transitioning to circular infrastructure management.

This study contributes to articulating the key models and measures toward sustainable CE in public infrastructure development.