Search results

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Traditional level inverter technology has drawbacks in the aspect of Total harmonic distortion (THD) and switching losses for higher frequencies. Due to these drawbacks, two-level inverters have become unprofitable for high-power applications. Multilevel inverters (MLIs) are used to enhance the output waveform characteristics (i.e. low THD) and to offer various inverter topologies and switching methods.

MLIs are upgraded versions of two-level inverters that offer more output levels in current and voltage waveforms while lowering the dv/dt and di/dt ratios. This paper aims to review and compare the different topologies of MLI used in high-power applications. Single and multisource MLI's working principal and switching states for each topology are demonstrated and compared. A Simulink model system integrated using detailed circuit simulations in developed in MATLAB®–Simulink program. In this system, a constant voltage source connected to MLI to feed asynchronous motor with squirrel cage rotor type is used to demonstrate the efficacy of the MLI under different varying speed and torque conditions.

MLI has presented better control and good range of system parameters than two-level inverter. It is suggested that the MLIs like cascade-five-level and NPC-five-level have shown low current harmonics of around 0.43% and 1.87%, respectively, compared to two-level inverter showing 5.82%.

This study is the first of its kind comparing the different topologies of single and multisource MLIs. This study suggests that the MLIs are more suitable for high-power applications.

This work analyzes, through social-environmental reports, whether banks with higher transparency in social-environmental policies better safeguard financial stability in Brazil.

The analysis is carried out through a panel database analysis of the 42 largest Brazilian banks, representing 98% of the Brazilian financial system. Seeking to avoid spurious results, we followed rigorous methodological standards. Hence, we conducted an empirical analysis using a dynamic panel data model, we used the difference generalized method of moments (D-GMM) and the system generalized method of moments (S-GMM).

The results show that the higher the transparency of social-environmental policies, the lower the chance of possible stress on the financial stability of Brazilian banks. In sum, this study builds evidence that disclosing risks related to policies about sustainability can enhance financial stability. It is essential to highlight that social-environmental transparency does not have as direct objective financial stability.

The manuscript submitted represents an original work that analyzes whether banks with higher transparency in social-environmental policies better safeguard financial stability. Some countries, such as Brazil, have their potential for sustainable policies spotlighted due to their green territory and diverse natural ecosystems. Besides having green potential, Brazil is a developing country with a well-developed financial system. These characteristics make Brazil one of the best laboratories for studying the relationship between transparency in social-environmental policies and financial stability.

In this article, the author discusses dynamical behaviors of a prey-predator population model with nonlinear harvesting rate and offers a mathematical analysis of the model.

The design is by using modelization of populations interaction, qualitative theory of ordinary différential equations, bifurcations analysis, invariant center manifolds theory and Dulac's criterion.

The author studies the stability of solutions and the existence of periodic solutions in the model, and proves the existence of some invariant sets and the production of a transcritical together with a saddle-node bifurcation.

The author studies the effects of harvesting on the persistence and extinction properties and its influence in the perspectives of economic views.

The authors considers a predator–prey model with a new nonlinear form of harvesting rate. The author’s intention is to make conceptual adjustments to a well-known predator–prey model in order to incorporate the effects of harvesting.

The aim of this study is to conduct a comparative analysis between Islamic and conventional banks in terms of whether Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. It also examines the role of capital in improving the performance and stability within the two banking systems.

This study uses 82 banks from MENA (Middle East and North Africa) region for periods across 2011–2020, and employs a dynamic panel data approach to examine the resilience within both banking systems during the Covid-19 pandemic.

The results show that the Covid-19 pandemic has a negative impact on conventional banks' stability. However, Islamic banks performed better and were less risky than conventional ones. Banks with high-quality capital are more effective at controlling their risks and improving their performance during the pandemic.

The results offer important financial observations and policy implications to many stakeholders engaging with banks. Actually, the findings of this study facilitate to the stakeholders and bankers to have an alluded picture about determinants of risk and performance. The results can be used by bankers’ policy decision-makers to improve and enhance their consideration for risk management, taking into consideration the type of banking systems.

Compared to the various studies on the stability of Islamic and conventional banks, researchers have not sufficiently addressed the effect of the Covid-19 pandemic on risk and performance. Moreover, none of these studies has examined if Islamic banks was more or less resilient/risky than conventional counterparts to the pandemic shock. This leads the authors to identify the similarities and differences between two types of banks in the MENA region in a pandemic shock context.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

We explore the impact of equity liquidity on a firm’s dynamic leverage adjustments and the moderating impacts of leverage deviation and target instability on the link between equity liquidity and dynamic leverage in the UK market.

In applying the two-step system GMM, we estimate our model by exploring suitable instruments for the dynamic variable(s), i.e. lagged values of the dynamic term(s).

Our analyses document that a firm’s equity liquidity has a positive impact on the speed of adjustment (SOA) of its leverage ratio back to the target ratio in the UK market. We also demonstrate that the positive relationship between liquidity and SOA is more pronounced for firms whose current position is relatively close to their target leverage ratio and whose target ratio is relatively stable.

This study provides important implications for both firms’ managers and investors. Particularly, firms’ managers who wish to increase the leverage SOA to enhance firms’ value need to give great attention to their equity liquidity. Investors who want to evaluate firms’ performance could also consider their equity liquidity and leverage SOA.

We are the first to enrich the literature on leverage adjustments by identifying equity liquidity as a new determinant of SOA in a single developed country with many differences in the structure and development of capital markets, ownership concentration and institutional characteristics. We also provide new empirical evidence of the joint effect of equity liquidity, leverage deviation and target instability on leverage SOA.

Our paper studies a central issue with a long history in economics: the relationship between population and economic growth. We analyze the joint dynamics of economic and demographic growth in 111 countries during the period 1960–2019.

Using the concept of economic regime, the paper introduces the notion of distance between the dynamical paths of different countries. Then, a minimal spanning tree (MST) and a hierarchical tree (HT) are constructed to detect groups of countries sharing similar dynamic performance.

The methodology confirms the existence of three country clubs, each of which exhibits a different dynamic behavior pattern. The analysis also shows that the clusters clearly differ with respect to the evolution of other fundamental variables not previously considered [gross domestic product (GDP) per capita, human capital and life expectancy, among others].

Our results indirectly suggest the existence of dynamic interdependence in the trajectories of economic growth and population change between countries. It also provides evidence against single-model approaches to explain the interdependence between demographic change and economic growth.

We introduce a methodology that allows for a model-free topological and hierarchical description of the interplay between economic growth and population.

To endure and cope in the rapidly changing environment, it is required of firms to gain a deeper acquisition of knowledge on market dynamics and subsequently concentrate on corporations' capacity to create, restructure and integrate their internal and external competences. Hence, the objective of this study is to investigate the influence of eco-dynamic capability (EDC) on the sustainability performance of small and medium-sized enterprises (SMEs).

Structured questionnaires were used to obtain primary data. The data were solicited from 500 employees and owner-managers of SMEs. The study’s hypotheses were tested using standard multiple regression through IBM SPSS Statistics (version 24).

The study revealed that EDC has a substantial positive effect on the economic, social and environmental sustainability performance dimensions.

The focus of this study is on EDC. Thus, although dynamic capability has been the subject of substantial study, little is known regarding the effect of EDC on the economic sustainability performance (ESP) (financial), environmental sustainability performance (ENSP) and social sustainability performance (SSP) of SMEs, predominantly amongst SMEs in emerging economies.

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.