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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.

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.

Diesel has traditionally been considered the best-suited and most widely used fuel in various sectors, including manufacturing industries, power production, automobiles and transportation. However, with the ongoing crisis of fossil fuel inadequacy, the search for alternative fuels and their application in these sectors has become increasingly important. One particularly interesting and beneficial alternative fuel is biodiesel derived from bio sources.

In this research, an attempt was made to use biodiesel in an unconventional micro gas turbine engine. It will remove the concentric use of diesel engines for power production by improving fuel efficiency as well as increasing the power production rate. Before the fuel is used enormously, it has to be checked in many ways such as performance, emission and combustion analysis experimentally.

In this paper, a detailed experimental study was made for the use of Spirulina microalgae biodiesel in a micro gas turbine. A small-scale setup with the primary micro gas turbine and secondary instruments such as a data acquisition system and AVL gas analyser. The reason for selecting the third-generation microalgae is due to its high lipid and biodiesel production rate. For the conduction of experimental tests, certain conditions were followed in addition that the engine rotating rpm was varied from 4,000, 5,000 and 6,000 rpm. The favourable and predicted results were obtained with the use of microalgae biodiesel.

The performance and combustion results were not exactly equal or greater for biodiesel blends but close to the values of pure diesel; however, the reduction in the emission of CO was at the appreciable level for the used spirulina microalgae biodiesel. The emission of nitrogen oxides and carbon dioxide was a little higher than the use of pure diesel. This experimental analysis results proved that the use of spirulina microalgae biodiesel is both economical and effective replacement for fossil fuel.

This paper aims to investigate the mechanisms through which social media news consumption across different platforms leads to opinion polarization in society. To this end, the authors draw from cultivation theory to examine whether social media news consumption imparts a mainstreaming or resonance effect. Media consumption imparts a mainstreaming effect if frequent users, regardless of their social identity, develop homogenous attitudes about issues, whereas resonance is at play if there is a differing cultivation effect on various social groups depending on their relatability of life experiences.

The authors conduct the study in the developing context of India, using a population survey dataset from 2019. Regression-based mediation and moderation analyses were carried out to test the hypotheses.

The findings reveal that resonance is the most prominent mechanism through which social media news consumption cultivates opinion polarization, contrary to the mainstreaming effect imparted by television. Further, WhatsApp use was found to strengthen the polarizing effect of overall social media news consumption, while YouTube use weakened the cultivation of polarization.

The paper unearths how social media news consumption influences the opinion polarization of various social groups differently. The authors also find the differential effect of specific platform use. These findings have the potential to inform policymakers and developers about how to mitigate the detrimental effects of platform-based political persuasion.

This study offers significant contributions. First, the authors explain social media-induced polarization using the novel theoretical lens of cultivation. Second, the authors find that social media and television news consumption differ in their polarizing effects. Third, the authors find that while WhatsApp use amplifies the polarizing effect of social media news consumption, YouTube use weakens it.

This study aims to elucidate the machining properties of low-cost expanded clay-reinforced syntactic foams by using different neural network models for the first time in the literature. The main goal of this endeavor is to create a casting machining-neural network modeling flow-line for real-time foam manufacturing in the industry.

Samples were manufactured via an industry-based die-casting technology. For the slot milling tests performed with different cutting speeds, depth of cut and lubrication conditions, a 3-axis computer numerical control (CNC) machine was used and the force data were collected through a digital dynamometer. These signals were used as input parameters in neural network modelings.

Among the algorithms, the scaled-conjugated-gradient (SCG) methodology was the weakest average results, whereas the Levenberg–Marquard (LM) approach was highly successful in foreseeing the cutting forces. As for the input variables, an increase in the depth of cut entailed the cutting forces, and this circumstance was more obvious at the higher cutting speeds.

The effect of milling parameters on the cutting forces of low-cost clay-filled metallic syntactics was examined, and the correct detection of these impacts is considerably prominent in this paper. On the other side, tool life and wear analyses can be studied in future investigations.

It was indicated that the milling forces of the clay-added AA7075 syntactic foams, depending on the cutting parameters, can be anticipated through artificial neural network modeling.

It is hoped that analyzing the influence of the cutting parameters using neural network models on the slot milling forces of metallic syntactic foams (MSFs) will be notably useful for research and development (R&D) researchers and design engineers.

This work is the first investigation that focuses on the estimation of slot milling forces of the expanded clay-added AA7075 syntactic foams by using different artificial neural network modeling approaches.

This paper examines the relationship between tax avoidance and accounting comparability. The authors argue that aggressive tax behavior impairs the comparability of financial statements by altering the accounting function, which maps economic events into accounting data.

The empirical analysis is based on a large sample of United States (US) firms. The authors use raw and industry-adjusted effective tax rates (ETRs) to proxy tax avoidance. The authors use the measure of accounting comparability developed by De Franco et al. (2011), which aims to capture the similarity of the accounting function.

The authors find that firms with more aggressive tax avoidance strategies have substantially lower accounting comparability. The evidence also shows that the negative effect of tax avoidance on accounting comparability is driven by firms with aggressive tax planning strategies beyond the industry norm. Furthermore, using an alternative measure of accounting comparability as a function of pre-tax income, the authors continue to find evidence of the negative effect of tax avoidance behavior. Importantly, this provides evidence that the effect of aggressive tax planning is not limited to the reported tax expense, but affects the comparability of the overall financial reporting system.

The authors identify a new potential cost of tax aggressive activities, being the loss of accounting comparability as driven by tax aggressive activities. The results contribute to the literature on the costs of tax avoidance and on the determinants of accounting comparability.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.