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The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

Research focusing on psychological capital (PsyCap) has been mainly conducted at the individual level. However, recent research has expanded investigations to the collective level with a greater focus on team-level PsyCap. Although, as demonstrated by recent systematic reviews and meta-analyses, the relationships between individual-level PsyCap and the desirable/undesirable outcomes are fairly established in the literature, less is known about such relationships for team-level PsyCap. One of these important, yet least investigated, research areas is the research stream that focuses on the relationship between team-level PsyCap and the outcomes of health, Well-Being, and safety. This chapter aims to highlight the role of individual-level PsyCap as an important predictor of employees’ health, Well-Being, and safety outcomes, but also to go beyond that to provide insights into the potential role of team-level PsyCap in predicting such outcomes at both individual and team levels. To do so, the chapter first draws upon relevant theories to discuss the empirical research findings focusing on the relationship between individual-level PsyCap and the outcomes of health, Well-Being, and safety. It then focuses on team-level PsyCap from theoretical, conceptualization, and operationalization perspectives and provides insights into how team-level PsyCap might be related to health, Well-Being, and safety outcomes at both individual and team levels. Thus, this chapter proposes new research directions in an area of PsyCap that has been left unexplored.

The purpose of this paper is to systematically synthesize the extant literature of knowledge sharing (KS) and knowledge transfer (KT) in the small and medium enterprise (SME) context and to contribute with predictions of emerging themes.

Applied is a systematic literature review using three bibliometric techniques: (1) textual analysis for keywords and abstracts to identify the research hotspots, (2) co-citation analysis of references to identify the evolution of KS and KT in SME and (3) bibliographic coupling analysis of documents to synthesize antecedents and outcomes.

A conceptual map emerges from the review to reveal the antecedents of KS and KT at the individual, group and organizational levels. The analysis shows the strategic importance of KS and KT for the SME context. Specific findings include: (1) KS and KT are involved in enhancing SMEs strategic focus for human resources, including organizational learning, customer relations, creativity, higher profit and positive effects on operational processes and decision-making. (2) Innovation, trust and performance are identified as central human factors linked to KS and KT in SMEs. (3) Human resource (HR) management research could contribute to KS and KT in the SME domain by exploring KS- and KT-based practices, linking the emergence of innovation and innovative behaviors to these practices, leading to a better understanding of strategies that enable the long-term storage and retrieval of tacit and explicit knowledge as organizational memory in the SME context.

This paper is one of the first to systematically review KS and KT in SMEs and propose a concept map. The research adds value to the growing literature of KS and KT and exposes the need for more specific activities to support SME managers, as well as HR managers, who need to facilitate KS and KT in SMEs.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.

Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm.

In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods.

The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06.

The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.

The purpose of this paper is to investigate the fatigue performance of a welded detail from a composite steel-concrete railway twin girder bridge caused by a passenger train circulating at varying speeds, by identifying the dynamic amplification scenarios induced by resonance. For this purpose, the hotspot stress method is used, instead of the traditional nominal stress methods.

This paper assesses the fatigue behavior of a welded connection considering critical stress concentration locations (hotspot). Finite element analysis (FEA) is applied, utilizing both a global and a local submodel, made compatible by displacements field interpolation. The dynamic response is obtained through the modal superposition method. Stress cycles are extracted with the rainflow counting method and the fatigue damage is calculated with Palmgren-Miner’s rule. The feasibility of five submodels with different mesh densities, i.e. 1, 2, 4, 8 and 20 mm is verified.

An increase in the fatigue damage due to the resonance effect was found for the train traveling at a speed of 225 km/h. A good agreement between the computed fatigue damage for the submodels is achieved. However, a non-monotonic hotspot stress/fatigue damage vs mesh density convergence was observed with a peak observed for the 4 mm model, which endorses the mesh sensitivity that could occur when using the surface stress extrapolation detailed rules specified in the standards for the hotspot stress method.

Advanced dynamic analyses are proposed to obtain local stresses in order to apply a local method for the fatigue assessment of a bridge’s structure subjected to high-speed railway traffic on the basis of the mode superposition technique resulting in much less computing times.

The teaching–research nexus (TRN) has assumed a prominent role in global higher education systems. However, the connection between the two domains has been subject to diverse interpretations within well-developed higher education systems. Little is known about translating TRN into policy and practice in diverse higher education spaces. In this regard, this article examines the recent trajectory and challenges associated with integrating research and teaching in Cambodia.

To analyse the current development of TRN and its associated challenges, this article consolidates two sets of data: recent research papers and government policy documents on TRN in the last 10 years or so.

The analysis shows that the connection between teaching and research appears to be patchy in practice, as evidenced in previous studies. The recent government policies and interventions, although potentially fostering progress in research activities, pose dilemmas regarding the orientation and feasibility of linking these two domains.

This article focuses on enhancing TRN in Cambodia, one of the developing higher education contexts, thereby offering insights into the strategies and concerns within such environments. Policymakers in similar higher education contexts might benefit from considering the concerns and solutions highlighted in this article.

Research on policies and strategies for strengthening TRN in developing higher education contexts remains limited in the literature. This article contributes to our collective knowledge of TRN by looking at Cambodia's contemporary reality and strategies.

The purpose of this paper is to explore two identified knowledge gaps: first, the identification and analysis of online searching trends for Financial Technology (FinTech)-related jobs and education information in UK, and second to assess the current strength of the FinTech-related job distribution in terms of job titles and locations in UK, job market in UK and what is required to help it to grow.

Two sets of data were used in this study in order to fill the two identified knowledge gaps. First, six years’ worth of data, for the period from September 2012 to August 2018 was collected from Google Trends. This was in the form of search term keyword text. The hypothesis was designed correspondingly, and the results were reviewed and evaluated using a relevant statistical tool. Second, relevant data were extracted from the “Indeed” website (www.indeed.co.uk) by means of a simple VBA programme written in Excel. In total, the textual data for 500 job advertisements, including the keyword “FinTech”, were downloaded from that website.

The authors found that there was a continuously increasing trend in the use of the keyword “fintech” under the category “Jobs and Education” in online searching from September 2012 to August 2018. The authors demonstrated that this trend was statistically significant. In contrast, the trends for searches using both “finance” and “accounting” were slightly decreased over the same period. Furthermore, the authors identified the geographic distribution of the fintech-related jobs in the UK. In regard to job titles, the authors discovered that “manager” was the most frequently searched term, followed by “developer” and “engineer”.

Educators could use this research as a reference in the development of the portfolio of their courses. In addition, the findings from this study could also enable potential participators to reflect on their career development. It is worth noting that the motivations for carrying out an internet search are complex, and each of these needs to be understood. There are many factors that would affect how an information seeker would behave with the obtained information. More work is still needed in order to encourage more people to enter to the FinTech sector.

In the planning stage prior to launching a new course educators often need to justify the market need: this analysis could provide a supporting rationale and enable a new course to launch more quickly. Consequently, the pipeline of talent supply to the sector would also be benefitted. The authors believe this is the first time that a study like this had been conducted to explore specifically the availability and opportunities for FinTech education and retraining in UK. The authors anticipate that this study will become the primary reference for researchers, educators and policy makers engaged in future research or practical applications on related topics.

Highlights in academic papers serve as condensed summaries of the author’s key work, allowing readers to quickly grasp the paper’s focus. However, many journals do not currently offer highlights for their articles. To address this gap, some scholars have explored using supervised learning methods to extract highlights from academic papers. A significant challenge in this approach is the need for substantial amounts of training data.

This study examines the effectiveness of prompt-based learning for generating highlights. We develop task-specific prompt templates, populate them with paper abstracts and use them as input for language models. We employ both locally inferable pre-trained models, such as GPT-2 and T5, and the ChatGPT model accessed via API.

By evaluating the model’s performance across three datasets, we find that the ChatGPT model performed comparably to traditional supervised learning methods, even in the absence of training samples. Introducing a small number of training samples further enhanced the model’s performance. We also investigate the impact of prompt template content on model performance, revealing that ChatGPT’s effectiveness on specific tasks is highly contingent on the information embedded in the prompts.

This study advances the field of automatic highlights generation by pioneering the application of prompt learning. We employ several mainstream pre-trained language models, including the widely used ChatGPT, to facilitate text generation. A key advantage of our method is its ability to generate highlights without the need for training on domain-specific corpora, thereby broadening its applicability.

Given the rapid growth of the Chinese cruise market, several Chinese cities are attempting to become a hub for cruise ships. The aim of this study is to evaluate the competitiveness of those cities with regard to hosting cruises. It employs a cloud model that transforms the qualitative description of competitive indicators into quantitative measurement and assesses the competitive advantage and specialization of the top homeports in China. Questionnaires are delivered to experts from eight cruise ports in China and the cloud model based on 14 evaluation factors is used to analyze the competitiveness of the ports under investigation. The result shows that the city’s scale, population, and development have been the main factors for assessing the competitiveness of port cities. The research provides insight about the Chinese cruise port cities’ competitiveness, which is particularly useful for future development decision and marketing strategies of ports and prospective destinations.