Search results

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.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

This study aims to investigate the effect of supervisor bottom-line mentality (SBLM) on subordinate work well-being using self-determination theory. Furthermore, it examines the mediating role of subordinate harmonious work passion (HWP) and obsessive work passion (OWP), as well as the moderating role of subordinate family motivation on the indirect effect of SBLM on subordinate work well-being.

The authors conducted two studies, an experiment and a field study, to test the hypotheses. In Study 1, the authors conducted an experimental study using a sample of 127 undergraduate students to examine how family motivation moderates the relationship between SBLM and subordinate work passion. Concurrently, in Study 2, the authors conducted a time-lagged field study involving 261 corporate employees in China to validate the findings derived from Study 1, as well as test the entire conceptual model.

The authors find in Study 1 that family motivation moderates the effects of SBLM on subordinate HWP and OWP. Nevertheless, Study 2 uncovers a negative association between SBLM and subordinate work well-being, with HWP and OWP mediating this relationship. Besides, family motivation moderates the mediating effect of HWP on the relationship between SBLM and subordinate work well-being.

The main contribution of this study is that the negative effect of SBLM impacts subordinate work well-being, thereby building an accurate and fine-grained knowledge base of the detrimental effects of bottom-line mentality (BLM). Additionally, this study expands the frontiers of knowledge in this area by investigating the mediating mechanisms and boundary conditions of SBLM on subordinate work well-being, effectively addressing a theoretical gap in BLM research.

This study aims to investigate the impact of the development of high-speed rail (HSR) network on earnings management, especially on the trade-off between the usage of accruals-based earnings management (AM) and real earnings management (RM) techniques, and consequently, examines the extent to which the HSR network–earnings management nexus is moderated by governance and religion factors.

Using a sample of Chinese A-listed firms over an 11-year period, this study uses regression techniques as the baseline methodology while controlling for industry and year-fixed effects. The authors also use endogeneity tests (including instrumental variable method, Generalized Methods of Moments estimation and difference-in-difference) and different robustness checks.

The key findings are threefold. First, the HSR network development reduces AM. This suggests that the presence of HSR network is effective in reducing information asymmetry. Second, the use of RM technique increases with the HSR network development. This indicates that managers do not seem to engage in less earnings management with the HSR network development but instead appear to switch from the easy-to-detect AM to the more costly RM approach. Finally, the HSR network and earnings management nexus is moderated by governance and religion factors.

This study provides new evidence on the trade-off between AM and RM by managers and pioneers in examining the impacts of governance and religion factors on the relationship between the HSR network and the trade-off of earnings management techniques.

Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range.

The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances.

The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally.

The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.