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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 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.

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

The purpose of this study is to investigate the recovery and resilience tourism strategies and possible future development of four main Chinese tourism cities.

The authors collected data from the official accounts of tourism administrations of these cities, tourist attractions and opinions from media and newspapers in Sina Weibo platform. The authors adopted an inductive approach in observing relevant social media posts and applied content analysis to identify main China’s tourism prevention and recovery strategies.

During the mass pandemic infection period, top-down prevention and control measures were implemented by the Chinese central and local governments, with feasible and regional recovery policies and protocols being adapted according to local situations. Measures related to tourism industrial re-employment, improvement of international images and governmental financial supports to re-boost local tourism in Chinese cities were paid great attention. Digitalization, close-to-nature and cultural heritages became important factors in the future development of China’s tourism. Dark tourism, as a potential tourism recovery strategy, also obtained huge emergence, for the memory of people deceased in the pandemic and for the inheritance of national patriotism.

This study enriches the current literature in urban tourism recovery studies analyzing the specific case of Chinese tourism cities and fulfill some voids of previous research mostly focused on the first wave of the pandemic and the recovery strategies mainly of Western cities. It also provides valuable suggestions to tourism practitioners, destinations and urban cities in dealing with regional tourism recession and finding possible solutions for the scenario associated to the COVID-19 and other similar health crisis.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This study investigates the effect of COVID-19 pandemic perceived health risk on traveller's post-pandemic perception and future travel intention. The study aims to provide insight into the unprecedented COVID-19 pandemic and its potential influence on tourist behaviour.

Two hundred and forty-four responses were gathered quantitatively through an online survey. The research hypotheses were analysed using the partial least square structural equation modelling (PLS-SEM).

This study found that COVID-19 affects tourists' travel behaviour. Key findings found that perceived health risk discourages travel attitudes and eventually lessens their future travel intentions. Results also suggest future strategies/directions for restarting the tourism industry.

The study outcome assists tourism stakeholders in understanding the changes in tourist behaviour amid the heightened perceived health risk of COVID-19. Tourism policymakers and industry players should consider exploring how to mitigate similar health crises in the future.

By extending the theory of planned behaviour (TPB), this study establishes a theoretical framework in exploring the interrelationships between perceived risk, post-pandemic perception and future travel intention. This study sets a significant research agenda for future tourism research in understanding the mechanism behind health risk perceptions and tourist behaviour.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.