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The purpose of this paper is to present an optimisation of four-point star-shaped structures produced through additive manufacturing (AM) polylactic acid (PLA). The study also aims to investigate the compression failure mechanism of the structure.

A Taguchi L₉ orthogonal array design of the experiment is adopted in which the input parameters are resolution (0.06, 0.15 and 0.30 mm), print speed (60, 70 and 80 mm/s) and bed temperature (55°C, 60°C, 65°C). The response parameters considered were printing time, material usage, compression yield strength, compression modulus and dimensional stability. Empirical observations during compression tests were used to evaluate the load–response mechanism of the structures.

The printing resolution is the most significant input parameter. Material length is not influenced by the printing speed and bed temperature. The compression stress–strain curve exhibits elastic, plateau and densification regions. All the samples exhibit negative Poisson’s ratio values within the elastic and plateau regions. At the beginning of densification, the Poisson’s ratios change to positive values. The metamaterial printed at a resolution of 0.3 mm, 80 mm/s and 60°C exhibits the best mechanical properties (yield strength and modulus of 2.02 and 58.87 MPa, respectively). The failure of the structure occurs through bending and torsion of the unit cells.

The optimisation study is significant for decision-making during the 3D printing and the empirical failure model shall complement the existing techniques for the mechanical analysis of the metamaterials.

To the best of the authors’ knowledge, for the first time, a new empirical model, based on the uniaxial load response and “static truss concept”, for failure mechanisms of the unit cell is presented.

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

This study aims to assess the knowledge and attitudes toward recovering citizenship (RC)/5 Rs and mental illness of people aged ≥18 years in Hong Kong using a telephone survey approach.

A questionnaire comprised the Mental Health Knowledge Schedule (MAKS), Short Form-Community Attitudes Toward Mental Illness (SF-CAMI) and questions on attitudes toward RC/5 Rs, was administered on the phone.

A total of 1,009 respondents completed the telephone survey. A high mean score of MAKS (4.37 ± 1.08) was found with 68%–94% answering the knowledge items correctly. The mean score of SF-CAMI was 46.50 ± 8.74 with the most positive attitude toward fear and exclusion. Approximately half had heard about a similar concept of RC and 79%–94.3% agreed with people in recovery to possess the 5 Rs. Those with greater knowledge or more positive toward mental illness, or knowing someone in recovery were more supportive toward 5 Rs. Those aged 18–44 years, attained a post-secondary education, were employed, and received a monthly income of US$3,861–6,434 were significantly more positive toward 5 Rs.

To the best of the authors’ knowledge, this is the first study assessing the views of RC of people in the community. The sample had a good knowledge of mental illness but recognition of recovery from mental illness and a sympathetic view toward people in recovery can be further improved. Besides promotion programs, dissemination of the concept of RC and having people in recovery take up valued roles in the community could potentially facilitate the acceptance of social inclusion and acceptance in the community.

This study aims at providing exploratory insights into the initiative and capabilities of Chinese SMEs to develop and utilize diverse networks to support internationalization. Such network development and utilization efforts are fundamental to the analysis and explanation of Chinese firms’ internationalization patterns and outcomes. Extending from the existing network studies in the Chinese context that generally put emphasis on strong‐tie and ethnic‐oriented networks, this paper investigates and explains explicitly the use and effects of both strong‐ and weak‐tie networks in the international development of Chinese SMEs. Indepth case studies on four rapidly internationalized Chinese SMEs are conducted. The case findings demonstrate that weak‐tie networks are essential to the firms’ business development in foreign markets; and were proactively developed and utilized in the course of the firms’ development. The cases also provide alternative perspectives to the beliefs and values underpinning strong‐tie networks presumed in existing literature. The findings draw attention to the changing business values and approaches of the Chinese firms aiming at developing internationally. Managerial implications concerning the significant influence of effective networking on internationalization are pinpointed.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.