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This study aimed to explore the spatial accessibility dynamics of urban parks and their driving forces from 1901 to 2010 in terms of the dynamic relationships between spatial morphology and road networks, taking Nanjing City as an example.

This study mapped and examined the spatiotemporal distribution of urban parks and road networks in four time points at Nanjing: the 1910s, 1930s, 1960s and 2010s, using the analysis methodology of spatial design network analysis, kernel density estimation and buffer analysis. Two approaches of spatial overlaying and data analysis were adopted to investigate the accessibility dynamics. The spatial overlaying compared the parks' layout and the road networks' core, subcore and noncore accessible areas; the data analysis clarified the average data on the city-wide and local scales of the road networks within the park buffer zone.

The analysis of the changing relationships between urban parks and the spatial morphology of road networks showed that the accessibility of urban parks has generally improved. This was influenced by six main factors: planning implementation, political policies, natural resources, historical heritage and cultural and economic levels.

The results provide a reference for achieving spatial equity, improving urban park accessibility and supporting sustainable urban park planning.

An increasing number of studies have explored the spatial accessibility of urban parks through the relationships between their spatial distribution and road networks. However, few studies have investigated the dynamic changes in accessibility over time. Discussing parks' accessibility over relatively long-time scales has practical, innovative and theoretical values; because it can reveal correlational laws and internal influences not apparent in short term and provide reference and implications for parks' spatial equity.

Compressing project timelines represents a prevalent temporal tactic aimed at accelerating the innovation process. However, empirical evidence on the impact of such time constraints on innovation remains inconclusive. This study aims to investigate the relationship between a prevalent organizational time mechanism—Performance Appraisal Interval (PAI)—and employee exploratory innovation behavior. Additionally, we explore the boundary conditions that may influence this relationship: the moderating effects of future work self salience and supervisory developmental feedback.

Using online survey data collected in two waves from 426 employees working in hi-tech companies in China, we tested all the hypotheses.

(1) PAI demonstrates an inverted U-shaped influence on employees exploratory innovation behavior; (2) Employees’ future work self salience serves as a moderator that enhances the positive nature of this inverted U-shaped relationship; (3) Supervisory developmental feedback amplifies the moderating role of future work self salience, and the synergistic effect of PAI, future work self salience, and supervisory developmental feedback significantly enhances exploratory innovation behavior.

By providing insights that are attuned to the temporal aspects of performance appraisal, this study aids organizations in making more informed, strategic decisions that enhance both the effectiveness of performance assessments and the cultivation of an environment that encourages exploratory innovation. Additionally, it is recommended that organizational leaders incorporate future-oriented interventions and developmental feedback into their management practices to further promote employees' engagement in exploratory innovation.

Drawing on the interactive theory of performance, this study introduces a novel perspective on how an organizational temporal mechanism influences exploratory innovation and advances our understanding of the non-linear link between time constraints and employees' innovative behaviors.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

This study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.

Amidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.

We find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.

In the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

As more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood, especially the effect of AI service failure events. This study examines the influences of AI service failure events, including their industry, size, timing, and type, on firm value.

This study will conduct an event study of 120 AI service failure events in listed companies to evaluate the costs of such events.

First, AI service failure events have a negative impact on the firm value. Second, small firms experience more share price declines due to AI service failure events than large firms. Third, AI service failure events in more recent years have a more intensively negative impact than those in more distant years. Finally, we identify different types of AI service failure and find that there are order effects on firm value across the service failure event types: accuracy > safety > privacy > fairness.

First, this study is the initial effort to empirically examine market reactions to AI service failure events using the event study method. Second, this study comprehensively considers the effect of contextual influencing factors, including industry type, firm size and event year. Third, this study improves the understanding of AI service failure by proposing a novel classification and disclosing the detailed impacts of different event types, which provides valuable guidance for managers and developers.

Owing to the finite nature of the boundary of the line (BOL), the conventional method, involving the strong matching of single-variety parts with storage locations at the periphery of the line, proves insufficient for mixed-model assembly lines (MMAL). Consequently, this paper aims to introduce a material distribution scheduling problem considering the shared storage area (MDSPSSA). To address the inherent trade-off requirement of achieving both just-in-time efficiency and energy savings, a mathematical model is developed with the bi-objectives of minimizing line-side inventory and energy consumption.

A nondominated and multipopulation multiobjective grasshopper optimization algorithm (NM-MOGOA) is proposed to address the medium-to-large-scale problem associated with MDSPSSA. This algorithm combines elements from the grasshopper optimization algorithm and the nondominated sorting genetic algorithm-II. The multipopulation and coevolutionary strategy, chaotic mapping and two further optimization operators are used to enhance the overall solution quality.

Finally, the algorithm performance is evaluated by comparing NM-MOGOA with multi-objective grey wolf optimizer, multiobjective equilibrium optimizer and multi-objective atomic orbital search. The experimental findings substantiate the efficacy of NM-MOGOA, demonstrating its promise as a robust solution when confronted with the challenges posed by the MDSPSSA in MMALs.

The material distribution system devised in this paper takes into account the establishment of shared material storage areas between adjacent workstations. It permits the undifferentiated storage of various part types in fixed BOL areas. Concurrently, the innovative NM-MOGOA algorithm serves as the core of the system, supporting the formulation of scheduling plans.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

This paper aims to describe a case study of a formative study for the development of a social marketing campaign for increased biowaste sorting. In following the social marketing principles, it provides insights for practitioners willing to implement behaviour change interventions.

The case study describes the scoping review, expert interviews, online survey and focus groups, which were integrated to comprehend the specificities of biowaste sorting in the South Suburbs of Athens. This mixed-method research design enabled a comprehensive understanding of biowaste sorting practices that would not have been attainable through a single method.

This study found that the “descriptive norm” and “perceived controllability” significantly influenced biowaste sorting. Differential challenges of at-home and out-of-home sorting were also identified. The demand for more information was also highlighted. The tailored intervention comprises of a mix of behavioural modelling, persuasive communication, education and enablement.

By concretely showcasing how the integration of multiple research methods through the application of social marketing principles can guide the design of a tailored behaviour change intervention, this paper offers an outline for informed decision-making and strategic planning in the realm of (environmental) behaviour change.

This study aims to examine the influence of managerial myopia on the excessive financialization behavior of listed firms on Bursa Malaysia.

Through a sample of 313 firms from 2015 to 2021, the author examine whether managerial myopia promotes or inhibits corporate financialization. The author uses ordinary least squares and Logit as the baseline models and addresses potential endogeneity through the dynamic-panel generalized method of moments. The results are also robust to alternative measures of financialization and managerial myopia.

The results show a significant positive effect of managerial myopia on the excessive financialization of enterprises. Furthermore, the findings indicate that the impact of managerial myopia on the over-financialization of enterprises is more prominent in periods of low economic policy uncertainty. However, the relationship between excessive financialization and managerial myopia is weakened in the presence of female chief executive officers.

The empirical results have useful policy implications. First, firms should establish scientific managerial assessment and supervision systems to avoid excessive financial investment behavior by myopic managers caused by assessments that place too much emphasis on short-term performance. Second, regulators and policymakers should encourage firms to appoint women to top management positions, which may inhibit short-sighted financialization behavior. Finally, the regulatory authorities should undertake the necessary measures driving companies to disclose the investment direction of the funds so that shareholders and investors can understand the use direction of the funds in a timely manner, which can effectively prevent the economy “from the real to the virtual” and promote the development of the real economy.

This paper expands the existing research on corporate financialization behavior and provides a new theoretical basis for the underlying factors of excessive financialization. It studies the influence of corporate financialization from the perspective of short-run managerial actions and deepens the understanding of managerial myopia and companies’ financialization levels.