Search results

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

This study investigates the impact of tax planning, both independently and in conjunction with earnings management, on the persistence of earnings and its various components.

In this study, tax planning refers to corporate strategies aimed at minimizing taxes, while earnings management involves manipulating reported earnings through accounting accruals. The analysis uses a dataset of US companies from 1989 to 2016 and includes a series of regression tests.

The study finds that firms implementing aggressive tax strategies exhibit lower persistence in cash flows from operations and earnings. Furthermore, companies using both aggressive tax planning and earnings management techniques show the lowest persistence in total accruals, cash flows from operations and reported earnings.

Our sample of US firms limits generalizability. Future research could explore the international impacts of tax planning and earnings management on earnings quality and include post-2016 data for insights on the 2018 tax cuts and COVID-19. Investigating other earnings quality measures and their influence on investors and analysts could enhance performance assessment.

This research identifies key factors influencing the interpretation of financial statements, offering valuable insights for regulators, auditors, tax authorities, financial analysts and other users with significant practical and social implications.

This study contributes to prior research by highlighting the need to investigate the real effects of tax avoidance and extends prior research by examining the impact of high levels of tax planning, along with aggressive earnings management, on earnings persistence.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

This study examines whether common directors are guilty of contagion of corporate frauds from the customer side and, if so, how contagion occurs. Moreover, it explores a way to mitigate it, which is the increased digital orientation of firms.

Secondary data analysis is applied in this paper. We extract supply chain relations from the China Stock Market and Account Research (CSMAR) database as well as corporate fraud data from the same database and the official website of the China Securities Regulatory Commission (CSRC). Digital orientations are estimated through text analysis. Poisson regression is conducted to examine the moderating effect of common directors and the moderated moderating effect of the firms’ digital orientations.

By analysing the 2,096 downstream relations from 2000 to 2021 in China, the study reveals that corporate frauds are contagious through supply chains, while only customers’ misconduct can contagion to upstream firms. The presence of common directors strengthens such supply chain contagion. Additionally, the digital orientation can mitigate the positive moderating effect of common directors on supply chain contagion.

This study highlights the importance of understanding supply chain contagion through corporate fraud by (1) emphasising the existence of the contagion effects of corporate frauds; (2) understanding the potential channel in the process of contagion; (3) considering how digital orientation can mitigate this contagion and (4) recognising that the effect of contagion comes only from the downstream, not from the upstream.

This paper aims to investigate the actual impact, mechanism and internal and external environmental adjustment effect of ESG performance on the persistence of green innovation using data from China’s A-share manufacturing listed companies from 2009 to 2021.

Panel data regression models are used to explore the effect of ESG performance of manufacturing enterprises on the persistence of green innovation. To examine the mechanism of ESG performance affecting the persistence of green innovation of manufacturing enterprises, this paper refers to the research of Wen and Ye (2014) and constructs an analysis framework of intermediary effect.

This research was funded by Shandong Provincial Natural Science Foundation, grant number ZR2023MG075 & ZR2024QE171.

There are a few more limitations to this study that might be discussed from the following angles: first, due to data availability, this paper examines the persistence of green innovation from the output perspective. The authors can expand the data sources in the future and investigate the input-output combinations in green innovation as a means of understanding its sustainability. Second, the mechanism studied in this paper includes management costs, entry of green investors and risk-taking ability. In fact, it is possible that ESG performance influences green innovation persistence in other ways as well; these can be investigated more in the future.

First, it concentrates on the persistence of green innovation in manufacturing enterprises, surpassing the quantitative aspect and thereby broadening the research scope. Second, by including the “management expense ratio,” “green investor entry” and “risk-taking” as mediating factors, the study delves deeper into the mechanisms through which ESG performance impacts the persistence of green innovation in manufacturing enterprises, further broadening the research scope. Third, this research incorporates the internal and external environments encountered by manufacturing enterprises into the analytical framework to investigate their adjustment effects in the process of ESG performance influencing persistent green innovation, thus widening the research perspective. Fourth, this study introduces the subdimensions of ESG performance, specifically environmental responsibility, social responsibility and corporate governance, and assesses their impacts on the persistence of green innovation in manufacturing enterprises, thus enriching the research narrative.

This research investigates whether focal firms employ strategic supply chain information disclosure, focusing on the concealment of supplier and customer identities, as part of their supply chain environmental risk management strategies (supplier sustainability risk and customer loss risk, respectively).

Using a panel dataset of Chinese listed firms from 2009 to 2019 and utilizing the suppliers’ environmental punishment of peer firms (peer events) as an exogenous shock and employing ordinary least squares (OLS) estimation, this study conducts a regression analysis to test how focal firms disclose the identities of their suppliers and customers.

Our results indicate that focal firms prefer to hide the identities of their suppliers and customers following the environmental punishment of peer firms’ suppliers. In addition, supplier concentration weakens the effect of withholding supplier identities, whereas customer concentration strengthens the effect of hiding customer identities. Mechanism analysis shows that firms hide supplier identities to avoid their reputation being affected and hide customer identities to prevent the deterioration of customers’ reputations and thus impact their market share.

Our study reveals that reputation spillover is another crucial factor in supply chain transparency. It is also pioneering in applying the anonymity theory to explain focal firms’ information disclosure strategy in supply chains.

Silence is a commonly seen phenomenon at the workplace. However, little is known about the cause and effect of leader silence. Drawing on the affective events theory, we develop a moderated mediation model to examine the effect of subordinates’ creative deviance on leader's authoritative silence and test the moderating effect of subordinates’ political skills.

Our research adopts a novel bottom-up perspective to investigate the subordinates’ influence on leader silence. A two-wave survey study involving 196 corporate team leaders in China was employed.

We found that leader’s workplace anxiety mediated the relationship between subordinates' creative deviance and leader's authoritative silence and subordinates’ political skills moderated the mediating effect.

Our research contributes to the leader silence literature in three folds. First, we employ the AET framework to study leader silence from the emotional perspective. Second, this research adopts a bottom-up angle to reveal the influence of subordinates’ behavior on leader silence. Third, the political skills lens offers novel explanation of why the anxious emotions triggered by followers’ creative deviance vary among leaders.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

This cross-country study aims to investigate from an interdisciplinary perspective the impacts of the accounting regulation's strength and cultural values of long-term orientation (LTO) and indulgence (ND) on board efficacy in developing countries.

Board Efficacy Index scores for 54 developing countries over the period 2007–2016 were employed to ascertain predictors of management's accountability to boards of directors and investors. Two types of explanatory variables – formal and informal – were employed in a pooled Ordinary Least Squares (OLS) analysis.

The research is the first to empirically show that more LTO and ND in a country have significant and positive effects on board efficacy. The findings also show that the strength of auditing and reporting standards (SARS) has a dominant impact on board efficacy, and the SARS' consideration is recommended in future cross-country research on board efficacy.

To restore investor confidence and increase the credibility toward firms, regulatory authorities in developing countries are called upon to integrate compliance with accounting and auditing regulations combined with cultural values in the implementation of good governance practices.

This study contributes to the board efficacy literature in two significant ways. First, the study constructs and empirically tests a conceptual model that integrates both informal factors, the six cultural dimensions of Hofstede et al. (2010), and formal factors, the strength of accounting regulations. Second, conducting a study on a sample not widely used in the literature, over a fairly long period of time, highlights the governance characteristics of this context and strengthens the internal and external validity of the study.

This paper aims to investigate the improvement of Sn58Bi solder properties by Ni nanoparticle to provide theoretical support in the field of electronic packaging.

In this study, nickel nanoparticles (Ni NPs) were doped into Sn58Bi solder as a reinforcing agent to prepare a composite solder. The wettability of composite solder, melting characteristics of solder alloy, microstructure of joints, mechanical properties and intermetallics growth at the interface were investigated.

Test results show that Ni had little substantial effect on the solders alloy melting temperature yet decreased the alloy undercooling. Notably, Ni enhanced solders wettability on Cu substrates. The spreading area increased by 14% at 0.6 wt.% Ni. The Ni refined the microstructure of Sn58Bi solder joints and inhibited the coarsening of the Bi-rich phase in the solder. At the same time, it also led to the production of free intermetallic compounds, increasing growth of the intermetallic compound (IMC) layer. However, upon surpassing 0.6 wt.% Ni, (Cu, Ni)₆Sn₅ accumulated and formed irregular clumps in the matrix. The grain size of the IMC layer gradually decreased as Ni was added. In shear tests, the fracture of joints occurred mainly inside the solder matrix. Sn58Bi-0.6Ni joints displayed a 40% rise in shear strength and a 25% rise in Vickers hardness. In addition, the appearance of dimples at the fracture indicated the improved flexibility of joints.

In this paper, Ni nanoparticle with different contents were added to Sn58Bi solder to prepare a composite solder, and the properties of the composite solder were investigated from different perspectives.