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This study aims to using Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy and scanning electron microscope/energy dispersive Xray spectrometer to identify different automotive coatings for forensic purpose.

Two four-layered samples in a hit-and-run case were compared layer by layer with three different methods. FTIR spectroscopy was used to primarily identify the organic and inorganic compositions. Raman spectrum and scanning electron microscope/energy dispersive Xray spectrometer (SEM-EDS) were further used to complement the FTIR results.

Two weak and tiny peaks in one layer found between two samples by FTIR, Raman microscope and SEM-EDS verified the result of differences. The study used the three instruments in combination and found it’s effective in sensing coatings, especially in the inorganic additives.

Using these three instruments in combination is more accurate than individually in multilayered coating analysis for forensic purpose.

The three different instruments all present unique information on the composition, and provided similar and mutually verifiable results on the two samples.

With this method, scientists could identify and discriminate important coating evidences with tiny but characteristic differences.

Laser powder bed fusion (LPBF) in-situ alloying is a recently developed technology that provides a facile approach to optimizing the microstructural and compositional characteristics of the components for high performance goals. However, the complex mass and heat transfer behavior of the molten pool results in an inhomogeneous composition distribution within the samples fabricated by LPBF in-situ alloying. The study aims to investigate the heat and mass transfer behavior of an in-situ alloyed molten pool by developing a three-dimensional transient thermal-flow model that couples the metallurgical behavior of the alloy, thereby revealing the formation mechanism of composition inhomogeneity.

A multispecies multiphase computational fluid dynamic model was developed with thermodynamic factors derived from the phase diagram of the selected alloy system. The characteristics of the Al/Cu powder bed in-situ alloying process were investigated as a benchmark. The metallurgical behaviors including powder melting, thermal-flow, element transfer and solidification were investigated.

The Peclet number indicates that the mass transfer in the molten pool is dominated by convection. The large variation in material properties and temperature results in the presence of partially melted Cu-powder and pre-solidified particles in the molten pool, which further hinder the convection mixing. The study of simulation and experiment indicates that optimizing the laser energy input is beneficial for element homogenization. The effective time and driving force of the convection stirring can be improved by increasing the volume energy density.

This study provides an in-depth understanding of the formation mechanism of composition inhomogeneity in alloy fabricated by LPBF in-situ alloying.

The purpose of this paper is to study the high temperature oxidation behavior of Ti and C-added FeCoCrNiMn high entropy alloys (HEAs).

Cyclic oxidation method was used to obtain the oxidation kinetic profile and oxidation rate. The microstructures of the surface and cross section of the samples after oxidation were characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM).

The results show that the microstructure of the alloy mainly consisted of FCC (Face-centered Cubic Structure) main phase and carbides (M₇C₃, M₂₃C₆ and TiC). With the increase of Ti and C content, the microhardness, strength and oxidation resistance of the alloy were effectively improved. After oxidation at a constant temperature of 800 °C for 100 h, the preferential oxidation of chromium in the chromium carbide determined the early formation of dense chromium oxide layers compared to the HEAs substrate, resulting in the optimal oxidation resistance of the TC30 alloy.

More precipitated CrC can preferentially oxidize and rapidly form a dense Cr₂O₃ layer early in the oxidation, which will slow down the further oxidation of the alloy.

This study aims to better inform environmental management at universities by applying and validating the policy integration processes theory through a case study of Manchester Metropolitan University.

Social network analyses were used to identify, differentiate and categorise working networks of individuals and departments and the interconnections between them.

In an organisation, networks can be developed and active at departmental level but not at individual level. High numbers of departments can be doing work related to sustainable development whilst having low and medium levels of interconnections between departments. Influence of stakeholders throughout the network suggests levels of sustainable development policy integration at individual and departmental.

New insights provide evidence for universities’ environmental managers of the need of developing and implementing strategies that involve individuals’ work between departments by providing incentives, supporting capacity building and staff empowerment.

This paper applied and validated the theory of policy integration processes, showing that work at individual level and between departments needs more attention.

Considering the network externalities of online selling, this paper builds three different online direct selling models: manufacturer direct selling (MN model), network platform direct selling (NN model) and retailer direct selling (RN model). The optimal advertising and pricing decision and corporate profits under each selling model are investigated.

Combining the characteristics of online direct selling, this paper formulates direct selling models that are dominated by different companies as Stackelberg game models. Numerical analyses are carried out, along with the comparison of the equilibrium solutions for each model.

The authors' research shows that increasing network externalities is conducive to the development of enterprises. The network platform's profit is the lowest in the RN model and the highest in the NN one. The comparison of manufacturers' profits between the MN model and the NN model primarily depends on consumers' sensitivities for sales price and advertising promotion level. The manufacturer does not benefit from the RN model due to the lowest efficiency.

Coupled with three different online direct selling models and detailed analyses of the optimal solutions, this study has enriched the theoretical foundation of online direct selling. Moreover, this study extends the research of network externalities to the field of e-commerce, revealing the network externalities' influence on the decision-making of the e-supply chain.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on users' expressions can provide accurate and reliable information for event processing. Hence, 2,003,814 expressions on a major malignant emergency event were mined from multiple dimensions in this paper.

This paper conducted finer-grained analysis on users' online expressions in an emergency event. Specifically, the authors firstly selected a major emergency event as the research object and collected the event-related user expressions that lasted nearly two years to describe the dynamic evolution trend of the event. Then, users' expression preferences were identified by detecting anomic expressions, classifying sentiment tendencies and extracting topics in expressions. Finally, the authors measured the explicit and implicit impacts of different expression preferences and obtained relations between the differential expression preferences.

Experimental results showed that users have both short- and long-term attention to emergency events. Their enthusiasm for discussing the event will be quickly dispelled and easily aroused. Meanwhile, most users prefer to make rational and normative expressions of events, and the expression topics are diversified. In addition, compared with anomic negative expressions, anomic expressions in positive sentiments are more common. In conclusion, the integration of multi-dimensional analysis results of users' expression preferences (including discussion heat, preference impacts and preference relations) is an effective means to support emergency event processing.

To the best of the authors' knowledge, it is the first research to conduct in-depth and fine-grained analysis of user expression in emergencies, so as to get in-detail and multi-dimensional characteristics of users' online expressions for supporting event processing.

This study aims to establish a centralized decision-making game model and manufacturer-led Stackelberg game model based on factors of risk aversion of supply chain members and product greenness. The research aims to study whether the introduction of the “cost + risk sharing” contract affects coordination of this type of green supply by calculating the optimal decision of each mode.

This research designs a supply chain model under centralized and decentralized decision-making. This model uses the Stackelberg game to calculate the optimal decision under decentralized decision-making to evaluate the effect of a green supply chain and then analyze the “cost + risk sharing” contract and the degree of coordination of the supply chain. A sensitivity analysis is conducted on the centralized mode for the impact of variables on the supply chain.

This research finds a double marginalization effect in decentralized decision-making, and the risk aversion coefficient plays a decisive role in the utility of supply chain members. The specific range of risk- and cost-sharing factors allows supply chain members to achieve Pareto improvements and provides decision-making based on the corresponding management strategies according to each other’s risk preference degree.

The influence of each variable on the green supply chain in the centralized mode is studied by MATLAB numerical simulation. It provides reference for green supply chain members to formulate corresponding management strategies according to each other's risk preference degree.

This research innovatively considers manufacturers and retailers to explore the market demand for product greenness. It introduces a novel “cost + risk sharing” contract to coordinate the green supply chain.

This paper aims to examine the opportunities and challenges of using ChatGPT in higher education. Furthermore, it is also discuss the potential risks and plunders of these tools.

The paper discuss the use of artificial intelligence (AI) in academia and explores the opportunities and challenges of using ChatGPT in higher education. It also highlights the difficulties of detecting and preventing academic dishonesty and suggests strategies that universities can adopt to ensure ethical and useful use of these tools.

The paper concludes that while the use of AI tools, ChatGPT in higher education presents both opportunities and challenges. The universities can effectively address these concerns by taking a proactive and ethical approach to the use of these tools. This paper further suggests that universities should develop policies and procedures, provide training and support, to detect and prevent cheating intentions.

The paper provides insights into the opportunities and challenges of using ChatGPT in higher education, as well as strategies for addressing concerns related to academic dishonesty. The paper further adds importance to the discussion on the ethical and responsible use of AI tools in higher education.

This paper aims to investigate the impact of corporate culture on stock price crash risk and explore the underlying mechanisms.

This paper uses a novel firm-level culture measure of Li et al. (2020), which evaluates corporate culture from the perspectives of integrity, teamwork, innovation, respect and quality. Using a sample of 4,017 US firms from 2001 to 2018, this paper uses panel data regressions to explore the impact of corporate culture on stock price crash risk.

This paper finds that among five cultural dimensions, integrity reduces crash risk and quality increases crash risk. The mitigating effect of integrity culture on crash risk is concentrated among firms with a strong incentive or ability to hoard bad news. The exacerbating effect of quality culture on crash risk is concentrated among firms with low managerial flexibility.

This paper helps investors and regulators to understand the determinants of stock price crash risk, which facilitates investors’ wealth management and stabilizes social welfare.

To the best of the authors’ knowledge, this is the first study that uses time-varying firm-level measure of corporate culture to investigate its impact on stock price crash risk, contributing to the literature on the determinants of crash risk. Besides, this is the first study that explores the possible mechanism of managerial flexibility in influencing stock price crash risk.