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This study investigated what are the effective leadership styles and practices that boost employees’ work outcomes during the COVID-19 pandemic from the perspective of facilities management professionals (FMPs).

Three predominant leadership styles (transformational, transactional contingent reward and disaster management) were operationalized into 38 leadership practices (X variables) and 8 work outcomes (Y variables). The explanatory sequential research design was adopted. Online questionnaire survey was first conducted on FMPs who managed facilities during the critical periods of COVID-19 pandemic in Singapore. In-depth interviews were then carried out with subject matter experts to elaborate on the quantitative findings.

During the pandemic, FMPs were significantly stressed at work, but also experienced significant job satisfaction and satisfaction with their leaders/supervisors. Statistical results revealed a range of leadership practices that are significantly correlated with FMPs’ work outcomes. One leadership practice is critical as it affects 4 of the 8 FMPs’ work outcomes - frequently acknowledging employees’ good performance during the pandemic.

The study explored 3 leadership styles. There are other styles like laissez faire and servant leadership that might also affect work outcomes.

Based on the findings, suggestions were provided to organizations that employ FMPs on how to improve their work outcomes during a crisis such as a pandemic.

The novelty is the discovery that in the context of a global disaster such as the COVID-19 pandemic, the most relevant leadership styles to boost employees’ work outcomes are transactional contingent reward and disaster management leadership. The study adds to knowledge by showing that not one leadership style is superior – all 3 styles are complementary, but distinct, forms of leadership that need to work in tandem to boost FMPs’ work outcomes during a crisis such as a pandemic.

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.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

This paper aims to investigate whether and how enterprise social media (ESM) affordances affect employee agility.

Adopting self-determination theory (SDT), this study examines a model in which the four ESM affordances (i.e. visibility, association, editability and persistence) impact employee agility through the three basic psychological needs satisfaction (i.e. perceived autonomy, perceived relatedness and perceived competence) of employees. Mplus 7.4 was used to analyze survey data gathered from 304 employees who used ESM in the workplace.

The authors’ findings show that all four ESM affordances contribute to perceived relatedness and perceived competence; visibility and association affordances also have positive impacts on perceived autonomy; and all three psychological needs satisfaction positively impact employee agility.

First, this study adapted SDT to explore how ESM influences employee agility. Second, this study enriches the relevant research on the antecedents of employee agility and also provides new evidence and theoretical support for employee agility. Third, this study effectively expands the antecedents and outcomes of employee basic psychological needs satisfaction in the domain of ESM and agility.

Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors.

This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness.

Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers.

This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

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.