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This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an inspection mechanism and a reputation system in deterring opportunistic claims, considering an employer with limited inspection accuracy and a contractor, which can be either reputation-concerned or opportunistic.

This paper applies a signaling game to investigate the complementary effect between the employer's inspection and a reputation system in deterring the contractor's possible opportunistic claim, considering the information-flow influence of claiming prices.

This study finds that in the exogenous-inspection-accuracy case, the employer does not always inspect the claim. A more stringent reputation system complements a less accurate inspection only when the inspection cost is lower than a threshold, but may decline the employer's surplus or social welfare. In the optimal-inspection-accuracy case, the employer always inspects the claim. However, only a sufficiently stringent reputation system can guarantee the effectiveness of an optimal inspection in curbing opportunistic claims. A more stringent reputation system has a value-stepping effect on the employer's surplus but may unexpectedly impair social welfare, whereas a higher inspection cost efficiency always reduces social welfare.

This article contributes to the project management literature by combing the signaling game theory with the reputation theory and thus embeds the problem of inspection mechanism design into a broader socio-economic framework.

The prevalence of exposure to adversity is elevated in autistic populations, compared to neurotypical peers. Despite this, the frequency and nature of early adverse experiences are not well understood in autistic adults, with several underlying methodological limitations in the available literature. The purpose of this study is to systematically synthesise and analyse the prevalence of childhood adversity in this marginalised population, in accordance with the adverse childhood experiences (ACEs) framework.

Peer-reviewed empirical research articles were systematically searched for from electronic databases and screened against established inclusion criteria. Pooled prevalence rates for individual ACE types were calculated.

Four papers were included (N = 732), all of which used a predominantly or exclusively female sample. Only sexual abuse was reported in all papers, with a pooled prevalence rate of 38%. Physical abuse and emotional abuse were less frequently explored, with two papers reporting on these ACEs, though obtained comparable and higher pooled prevalence rates (39% and 49%, respectively). Pooled prevalence rates could be calculated for neither neglect nor “household” ACEs because of insufficient data. The limited state of the evidence, in conjunction with high levels of heterogeneity and poor sample representativeness found, positions the ACEs of autistic adults as a critical research priority.

To the best of the authors’ knowledge, this study is the first to systematically synthesise the prevalence of early childhood adversities, as conceptualised in accordance with the ACEs framework, in adults with autistic traits.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

The purpose of this paper is to provide a comprehensive framework that identifies thematic clusters and their interconnections within Global Talent Management (GTM), global careers and talent management (TM).

In this paper, this study conducted a co-citation analysis using bibliographic data to unveil the intellectual connections and relationships among thematic articles related to GTM sourced from the Web of Science.

This review highlights three key research themes: experiences working abroad, TM approaches and the complex nature of GTM as a living system.

The main limitation of this research is the sample itself. Content analysis based on the co-citation method resulted in some more recent releases being omitted.

The practical implications of the paper include providing a structured framework for understanding the complexities of GTM.

Research into the academic literature in this area is divided into various clusters, empirically demonstrating how GTM and global mobility are intertwined, revealing the need for us to more thoroughly comprehend the social ramifications of GTM practices and activities and the need to further analyse the influencing social aspects in a GTM strategy, like diversity, increased mobility and virtual reality.

The analysis revealed the emergence of three distinct thematic groups: (1) global work experiences, (2) TM approaches and (3) GTM.

The purpose of this paper is to measure the relationship of technology acceptance model (TAM) variables (PEOU and PU) with behavioral intention (BI) and attitude in mobile and digital libraries context. This study also examines the relationship of external variables (information quality and system quality) with TAM variables (PEOU and PU) in mobile and digital libraries context.

This meta-analysis was performed through PRISMA-P guidelines. Four databases (Google Scholar, Web of Science, Scopus and LISTA) were used for searching, and the search was conducted according to defined criteria.

Findings of this study revealed a large effect size of PU and PEOU with BI. There was also a large effect size of PU and PEOU with attitude. A medium effect size was found between SysQ → PU, InfoQ → PU and SysQ → PEOU. However, there was a small effect size between InfoQ and PEOU.

To the best of the authors’ knowledge, there was no study published till the time of conducting this meta-analysis. Hence, this study fills the literature gap. This study also confirms that TAM is a valid model in the acceptance and use of technology in mobile and digital libraries context. Thus, the findings of the present study are helpful for developers and designers in designing and developing mobile library apps. It will also be beneficial for library authorities and system librarians in designing and developing digital libraries in academic settings.

This study aims to examine the relationship between self-perceived talent status (SPTS) and positive employee outcomes (work engagement and organisational commitment), mediated by organisational justice (distributive and procedural justice). The authors define SPTS as employees’ self-conceptualisation of talent, formed by inferring the organisation’s initiatives regarding training and development opportunities and through informal recognition by others.

The authors measured SPTS using eight items on a five-point scale. Through an internet survey company, the authors initially surveyed 1,207 full-time employees from 300 Japanese companies with ≥ 300 employees. In the second round of the survey, conducted after approximately two weeks, 876 (82.9%) responses were collected from the initial 1,207 respondents, which were used for the final analysis.

SPTS was directly and positively related to work engagement, organisational commitment, distributive justice and procedural justice. In learning organisations, SPTS was positively but indirectly related to work engagement and organisational commitment, mediated by distributive justice. In non-learning organisations, SPTS was positively but indirectly related to work engagement and organisational commitment, mediated by procedural justice.

Given SPTS’s positive impact on employee outcomes, to eliminate the information asymmetry between organisations and talent due to strategic ambiguity, organisations should increase SPTS by helping talents perceive the plethora of development opportunities in the talent pool.

The results demonstrate the utility of SPTS for improving employee outcomes based on strategic talent management (TM) mechanisms including talent rewards, talent development opportunities and promotions. Furthermore, the results demonstrate that distributive justice plays an important role in the build-based TM context of learning organisations.