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The purpose of this study was to investigate the effects of aging time on the microstructure, mechanical properties and fracture morphology of Cu/Zn160%SAC0307/Al solder joints produced through solid-state bonding.

Zn particles with a size of 1 µm and Sn-0.3Ag-0.7Cu (SAC0307) particles ranging from 20 to 38 µm were used to achieve Cu/Al micro-connections using ultrasonic assistance at a temperature of 180 °C, followed by aging treatment at 150 °C to enhance the quality of Cu/Al joints. Scanning electron microscopy was used for observing and analyzing the solder seam, interface microstructure, and fracture morphology. The structural composition was determined using energy dispersive spectroscopy, while a PTR-1102 bonding tester was used to measure the average shear strength.

The results indicated that the intermetallic compounds formed at the interface between Cu substrates and solder metal primarily consisted of smooth Cu5Zn8. The Al-side interface mainly comprises an Al-Sn-Zn solid solution, with Zn-Sn-Cu phases forming between SAC0307 particles at 180 °C. During the aging process, atomic diffusion was accelerated, leading to improved connection quality. The shear strength of the joints initially increased before decreasing as aging time progressed; it peaked at 32.92 MPa after 24 h – an increase of 76.8% compared to as-received joints. After reaching stability at 96 h, there was still a notable increase in shear strength by 48.4% relative to as-received joints.

This study further explores the strengthening mechanisms associated with solid-state bonded Cu/SACZ/Al joints through aging processes. Joints created via solid-state bonding demonstrate superior reliability compared to traditional soldered connections. It is anticipated that insights gained from this research will contribute valuable knowledge toward developing low-temperature soldering methodologies for heterogeneous materials.

As information technology advances, the prevalence of AI chatbot products is on the rise. Despite optimistic market projections, consumer skepticism towards these agents persists. This paper aims to expand the scope of the technology acceptance model by integrating the aspect of appearance. It examines the influence of different attributes of AI chatbot, such as usefulness, ease of use and appearance, individually and in combination, on consumers' intentions to share and purchase.

Using an exploratory study of Web Texts, a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) mixed design and a 2 (usefulness: high vs low) × 2 (ease of use: high vs low) × 2 (anthropomorphism appearance: humanoid vs cartoon) for between-subjects designs and the price level (high vs low) for within-subjects designs. The hypotheses were tested by Octoparse and SPSS 22.0.

The research highlights the significant role of usefulness, ease of use and anthropomorphic appearance in shaping consumer attitudes towards AI chatbots, thus influencing their intentions to share information and make purchases. Grouped regression analysis reveals that lower prices exert a more pronounced positive influence on consumers' inclinations to both share and purchase, compared to higher prices. Moreover, novelty-seeking behavior moderates the effect of perceived usefulness or ease of use on attitude. Specifically, heightened novelty-seeking tendencies mitigate the impact of low perceived usefulness or ease of use, leading to sustained positive attitudes towards AI chatbots among consumers.

This study innovatively incorporates product appearance into the Technology Acceptance Model (TAM), considering both the functional attributes and appearance of AI chatbot and their impact on consumers. It offers valuable insights for marketing strategies, extends the scope of TAM application and holds significant practical implications for enhancing enterprise product planning.

随着信息技术的进步, AI聊天机器人产品的普及正在增长。尽管市场对这些代理人持乐观态度, 但消费者对这些代理人的怀疑仍然存在。本文旨在通过整合外观方面来扩展技术接受模型的范围。它考察了AI聊天机器人的不同属性（如有用性、易用性和外观）对消费者分享和购买意图的影响, 单独以及组合。

使用Web文本的探索性研究, 一个2（有用性：高vs低）× 2（易用性：高vs低）的混合设计和一个2（有用性：高vs低）× 2（易用性：高vs低）× 2（人格化外观：类人形vs卡通）用于受试者间设计和价格水平（高vs低）用于受试者内设计。通过 Octoparse 和 SPSS 22.0 测试假设。

研究突出了有用性、易用性和拟人化外观在塑造消费者对AI聊天机器人态度方面的重要作用, 从而影响了他们分享信息和购买的意图。分组回归分析显示, 相对于高价格, 低价格对消费者分享和购买的倾向产生了更为显著的正面影响。此外, 新奇寻求行为调节了感知有用性或易用性对态度的影响。具体来说, 增强的新奇寻求倾向减轻了对低感知有用性或易用性的影响, 导致消费者对AI 聊天机器人持续保持积极态度。

本研究将产品外观创新地纳入技术接受模型（TAM）中, 考虑了AI 聊天机器人的功能属性和外观以及它们对消费者的影响。它为营销策略提供了有价值的见解, 拓展了TAM的应用范围, 并对增强企业产品规划产生了重要的实际影响。

This research aims to focus on the problem of optimizing the layout of charging stations.

The authors estimate the total number of charging stations by the number of existing gas stations using clustering by fast search-and-find of density peaks (CFSFDP) algorithm.

This paper reveals the relationship between the distance and the population. Priority should be given to the short-distance and high-frequency travel charging needs in the city, followed by low frequency and travel, which can improve the use efficiency of the whole charging network.

First, the total number of charging stations is estimated by the number of existing gas stations. This method can fully consider the growth potential of charging vehicles instead of the traditional cars. Besides, the cost-benefit method is proposed to explore the marginal coverage distance of the city’s charging network and can be used to reveal the relationship between the distance and the population.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

Marking and inspecting are key steps in hull structure construction. However, currently most marking and inspecting operations are still carried out manually, which leads to low assembly efficiency and quality. This paper aims to solve these problems through the application of digital technology: the optical projection and machine vision.

First, the assembly process model of hull construction is established in 3D design environment. Second, the process information is presented to workers in a virtual form through optical projector, which provides accurate guidance for the manual operation. On this basis, the workers can complete welding and assembly operations readily. Finally, the machine vision method is used to check the assembly results, which can decrease the subjective errors.

A rapid and accurate assembly positioning for hull structure construction is realized based on optical projection, which can avoid the pollution caused by the marking machine and the error caused by human.

This paper combines the advantages of optical projection and machine vision to the field of shipbuilding. The shortcomings of the traditional marking and inspection methods is effectively solved, which may provide a new way for enhancing the assembly efficiency and quality.

The purpose of this study is to identify a comprehensive list of main enablers of successful implementation of green manufacturing (GM) practices in small and medium-sized enterprises (SMEs). Based on the inputs from Irish SMEs' experts, a validated conceptual hierarchy model of the identified enablers is developed to analyse and prioritise the most dominant ones.

Data were collected using a questionnaire survey responded by various professionals in Irish manufacturing SMEs. Experts' opinions were analysed using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to distinguish the mutual influences amongst the nine identified enablers.

The findings revealed that from the Irish SMEs' perspective, strong green supply chain relations are the outcome of successfully implementing GM and not a driver of the other enablers. Furthermore, lower manufacturing costs are found to arise as a result of GM practices in SMEs.

It is discussed that emulating the actions of larger companies will not necessarily yield the same results for SMEs. Innovation will become a cornerstone of remaining competitive in a sustainable environment, which is facilitated by closer green supply chain relations.

The reported findings in this article contribute to theory and practice of GM and SMEs research domains by systematically identified, classified and ranked enablers in a conceptual hierarchal model from the perspective of Irish SMEs. This research lays the foundations for further analysis of the GM practices enablers in a time when UN mandates and EU regulations are increasingly impacting SMEs all around Europe and especially Ireland.

This systematic review aims to comprehensively explore the existing literature on the use of corporate communication within the realm of social media.

A total of 136 peer-reviewed journal articles are explored and analysed using both performance and bibliometric analysis.

This review identifies five main findings: (1) trends in corporate social media research that highlight the growth trajectory of research on social media use for corporate disclosure, (2) geographical coverage of studies indicating the concentration of research in certain regions, such as the USA, followed by China and the UK, with notable gaps in others, such as developing countries, (3) theoretical frameworks employed demonstrate that various theoretical frameworks are utilized, although a significant portion of the studies do not specify any theoretical underpinning, (4) social media platforms studied, confirming Twitter to be the most studied channel followed by Facebook and (5) thematic analysis of articles on disclosure type that categorized the articles using bibliometric analysis into five themes of disclosure: general disclosure, corporate social responsibility-related information, financial information, CEO announcements and strategic news communication. A subsequent cross-theme analysis classifies disclosure determinants and consequences of corporate social media usage.

Through a comprehensive and systematic analysis of existing research, this review offers novel insights into the current state of corporate communication on social media. It consolidates current knowledge, highlights under-explored areas in the existing literature and proposes new directions and potential avenues for future research.

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.

Circular economy denotes future sustainability that allows optimum utilization of resources. In the present era of technology, plenty of innovations are happening across the world, and digital manufacturing is one of such innovations. However, there are several barriers which are impeding adoption of digital manufacturing in circular economy environment. The study explores the barriers of digital manufacturing initiatives in a circular economy and develops a methodological model to prioritize the identified challenges for automotive parts manufacturing industry.

Seven categories of challenges namely process, human resources, financial, collaboration, technological, security and leadership challenges were identified from literature and further validated with subsequent discussions with experts from the industry. The study is conducted in two phases, where in the first phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is used to define the priority and importance of seven categories of challenges. In second phase, the barriers are ranked using a Fuzzy Performance Important Index (FPII), taking into account contextual factors associated with the challenges and linked barriers, to determine the extent to which they impede the adoption of digital manufacturing in the sample automotive parts manufacturing company.

The “risk of data security and information privacy in connection with use of external data and protecting customer data” appeared as the most significant barrier to digital manufacturing in circular economy. Furthermore, technological challenges emerged as the most significant category of challenges followed by financial challenges in adoption of digital manufacturing in circular economy.

Identification of the identified barriers and understanding the interrelationships will lead to easier adoption of digital manufacturing in circular economy.

Despite all the potential benefits of implementing Industry 4.0 technologies in manufacturing industries, the adoption thereof is still in nascent phase with significant challenges yet to be overcome to accelerate the pace of adoption. Hence, this study explores the barriers preventing companies from adopting and benefiting from digital manufacturing initiatives and further develops a methodological model.

The purpose of this study is to explore the green innovation evolution in coal enterprises in China and the various driving mechanisms influenced by government regulations, enterprise resource capability and supply chain integration.

This paper uses the innovation data of coal enterprises in China for constructing a model to infer how enterprises achieve green innovation (the “hybrid drive effect model”).

This paper shows that various driving modes of green innovation in an enterprise are influenced by government regulations, enterprise resource and capability, supply chain integration and the interaction effects between any two actors.

By examining the evolution process of green innovation in a specific industry from the perspectives of exploitative and exploratory innovation, the paper touches a relatively novel view on green innovation.