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As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to explore the associations linking employees’ challenge-hindrance appraisals toward artificial intelligence (AI) to service performance while considering the dual mediating roles of job crafting and job insecurity, as well as the moderating role of AI knowledge.

A survey was administered to a sample of 297 service industry employees. This study examined all the hypotheses with Mplus 8.0.

This study confirms that challenge appraisal toward AI has an indirect positive influence on service performance via job crafting (motivation process), whereas hindrance appraisal toward AI has an indirect negative influence on service performance via job insecurity (strain process). Meanwhile, AI knowledge, serving as a key personal resource, could strengthen the positive impacts of challenge appraisal toward AI on job crafting and of hindrance appraisal toward AI on job insecurity.

Organizational decision-makers should first survey employees’ appraisals toward AI and then adopt targeted managerial strategies. From the perspective of service industry employees, employees should adopt proactive coping strategies and enrich their knowledge of AI to meet the challenges brought by this technology.

The primary contribution of this study is that we enrich the literature on AI by exploring the dual mediators (i.e. job crafting and job insecurity) through which AI awareness affects service performance. Moreover, this study advances our understanding of when appraisals toward AI influence job outcomes by identifying the moderating role of AI knowledge.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Among the proposed radio access strategies for improving system execution in 5G networks, the non-orthogonal multiple access (NOMA) scheme is the prominent one.

Among the most fundamental NOMA methods, power-domain NOMA is the one where at the transmitter, superposition coding is used, and at the receiver, successive interference cancellation (SIC) is used. The importance of power allocation (PA) in achieving appreciable SIC and high system throughput cannot be overstated.

This research focuses on an outage probability analysis of NOMA downlink system under various channel conditions like Rayleigh, Rician and Nakagami-m fading channel. The system design's objectives, techniques and constraints for NOMA-based 5G networks' PA strategies are comprehensively studied.

From the results of this study, it is found that the outage probability performance of downlink ordered NOMA under Rayleigh, Rician and Nakagami-m fading channel was good.

Outage probability analysis of downlink ordered NOMA under various channel conditions like Rayleigh, Rician and Nakagami-m fading channels were employed. Though the performance of Nakagami-m fading channel is lesser compared to Rayleigh channel, the performance for user 1 and user 2 are good.

This study aims to investigate the impact of the source of the content in social media communication and the content distribution intensity on consumer-based brand equity (CBBE) dimensions and how the study will eventually impact purchase intention.

A total of 521 samples were collected using an online survey questionnaire. The respondents' validity was verified using purposive sampling techniques, and the responses were analysed using SmartPLS 3.0.

The authors outlined the fundamental mechanisms of what makes social media communication effective and discovered that emotional-based brand equity dimensions (brand association and brand loyalty) remained significant in influencing purchase intention. However, attribution-based brand equity dimensions (perceived quality, brand trust and brand awareness) are found to have no impact.

This study decomposed social media communication into three different dimensions, and the authors' result showed that the dimensions do not impact CBBE to the same extent. The authors concluded that some CBBE dimensions, which appear to be a rigour determinant of purchase intention over time, have a feeble effect during the pandemic. The existing relationship between the CBBE dimensions with purchase intention might not hold in the pandemic context. The authors suggested that anxiety or pandemic fear could alter the normal consumer buying process and make some well-established relationships not hold. As research indicates that pandemics are reoccurring events, the authors' study contributes to the global effort to dampen some of the pandemic-related effects on business and marketing.

Decentralised autonomous organisations (DAOs) are internet-native self-governing enterprises where individual groups, communities, agencies, consumers and providers work together using blockchain-led smart contracts (SCs). This study aims to examine the role of DAO marketplaces in technology-led autonomous organisation design for enterprise technology sourcing industries, with algorithmic trust and governance.

The authors examined the importance of an enterprise marketplace governance platform for technology sourcing using DAO as a decentralised/democratised business model. A total of 98 DAO products/services are evaluated across 11 industries that envisage DAO as a strategic choice for the governance of decentralised marketplace platforms.

The research findings validate how a DAO-led enterprise marketplace governance platform can create a cohesive collaboration between consumers (enterprises) and providers (solution vendors) in a disintermediated way. The proposed novel layered solution for an autonomous governance-led enterprise marketplace promises algorithmic trust-led, self-governed tactical alternatives to a strategic plan.

The research targets multiple industry outlooks to understand decentralised autonomous marketplace governance and develop the theoretical foundation for research and extensive corporate suitability.

The research underpinnings boost the entrepreneurs’ ability to realise the practical potential of DAO between multiple parties using SCs and tokenise the entire product and service offerings over immutable ledger technologies.

To the best of the authors’ knowledge, this research is unique and the first of its kind to study the multi-industry role of algorithmic trust and governance in enterprise technology sourcing marketplaces driven by 98 decentralised and consensus-based DAO products across 11 industries.

This study aims to investigate the basic public service level in the Beijing-Tianjin-Hebei region under the impact of COVID-19.

This study constructed a basic public service-level evaluation system from the five dimensions of education, culture, health, social security and infrastructure and environment, and measures the basic public service level in 13 cities in Beijing, Tianjin and Hebei using the entropy method. The spatial pattern and dynamic evolution of the public service level are analysed from the perspective of dynamic trends in time series and spatial distribution, along with the reasons for the evolution of spatial distribution.

(1) The basic public service level in the 13 cities is generally on the rise, but the trend is unstable. (2) The basic public service level in space shows a general trend of attenuation from northeast to southwest, with significant spatial imbalance and orientation. (3) The regional differences first increase and then decrease. (4) The inter-group mobility of different basic public service levels is low, and cities with lower initial levels find it difficult to achieve leapfrog development. Moreover, the health service level of the region is still at a low stage, which is not conducive to effectively preventing and controlling the epidemic.

From the perspective of this research, the spatial pattern and dynamic evolution of basic public service were adopted to analyse the coordinated development of the Beijing-Tianjin-Hebei region. Furthermore, this study discusses how to improve the basic public service level to ensure sustainable operation in the region under the impact of COVID-19.

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.

Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.

This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

This paper is devoted to prepare micro-cone structure with variable cross-section size by Stereo Lithography Appearance (SLA)-based 3D additive manufacturing technology. It is mainly focused on analyzing the forming mechanism of equipment and factors affecting the forming quality and accuracy, investigating the influence of forming process parameters on the printing quality and optimization of the printing quality. This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

The µ-SLA process is optimized based on the variable cross-section micro-cone structure printing. Multi-index analysis method was used to analyze the influence of process parameters. The process parameter influencing order is determined and validated with flawless micro array structure.

After the optimization analysis of the top diameter size, the bottom diameter size and the overall height, the influence order of the printing process parameters on the quality of the micro-cone forming is: exposure time (B), print layer thickness (A) and number of vibrations (C). The optimal scheme is A1B3C1, that is, the layer thickness of 5 µm, the exposure time of 3000 ms and the vibration of 64x. At this time, the cone structure with the bottom diameter of 50 µm and the cone angle of 5° could obtain a better surface structure.

This study is expected to provide a µ-SLA surface preparation technology and process parameters selection with low cost, high precision and short preparation period for microstructure forming.

As a high-performance engineering plastic, polyimide (PI) is widely used in the aerospace, electronics and automotive industries. This paper aims to review the latest progress in the tribological properties of PI-based composites, especially the effects of nanofiller selection, composite structure design and material modification on the tribological and mechanical properties of PI-matrix composites.

The preparation technology of PI and its composites is introduced and the effects of carbon nanotubes (CNTs), carbon fibers (CFs), graphene and its derivatives on the mechanical and tribological properties of PI-based composites are discussed. The effects of different nanofillers on tensile strength, tensile modulus, coefficient of friction and wear rate of PI-based composites are compared.

CNTs can serve as the strengthening and lubricating phase of PI, whereas CFs can significantly enhance the mechanical properties of the matrix. Two-dimensional graphene and its derivatives have a high modulus of elasticity and self-lubricating properties, making them ideal nanofillers to improve the lubrication performance of PI. In addition, copolymerization can improve the fracture toughness and impact resistance of PI, thereby enhancing its mechanical properties.

The mechanical and tribological properties of PI matrix composites vary depending on the nanofiller. Compared with nanofibers and nanoparticles, layered reinforcements can better improve the friction properties of PI composites. The synergistic effect of different composite fillers will become an important research system in the field of tribology in the future.