Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.

As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.

Measuring and Achieving Equity in Multiperiod Emergency Material Allocation.

Emergency material allocation is an important part of postdisaster emergency logistics that is significant for improving rescue effectiveness and reducing disaster losses. However, the traditional single-period allocation model often causes local surpluses or shortages and high cost, and prevents the system from achieving an equitable or optimal multiperiod allocation. To achieve equitable allocation of emergency materials in the case of serious shortages relative to the demand by victims, this article introduces a multiperiod model for allocation of emergency materials to multiple affected locations (using an exponential utility function to reflect the disutility loss due to material shortfalls), and illustrates the relationship between equity of allocations and the cost of emergency response. Finally, numerical examples are presented to demonstrate both the feasibility and the usefulness of the proposed model for achieving multiperiod equitable allocation of emergency material among multiple disaster locations. The results indicate that the introduction of a nonlinear utility function to reflect the disutility of large shortfalls can make the material allocation fairer, and minimize large losses due to shortfalls. We found that achieving equity has a significant but not unreasonable impact on emergency costs. We also illustrate that using differing utility functions for different types of materials adds an important dimension of flexibility.

Exploring the Impact of Mindful Leadership on Employee Green Creativity in Manufacturing Firms: A Social Information Processing Perspective.

Increasingly, manufacturing enterprises are compelled to pursue innovative solutions to environmental issues. Addressing such issues requires mindful leadership to support employees and organizations in maintaining awareness during complex situations, which then promotes environmental sustainability. Drawing on social information processing theory, this study theorizes and tests the relationships between mindful leadership, employee moral reflectiveness, environmental passion, and employee green creativity. To test the model, we utilize a two-wave multisource dataset comprising 215 workers from manufacturing firms in China. The findings demonstrate that mindful leadership has a positive relationship with employee green creativity. Additionally, our research reveals that both moral reflectiveness and environmental passion serve as mediators in linking mindful leadership to employee green creativity. Crucially, our empirical analysis suggests a serial mediation model, examining the sequential role of moral reflectiveness and environmental passion in the relationship between mindful leadership and employee green creativity. The findings provide a new viewpoint on employees' eco-friendly behaviors and have practical implications for improving environmental sustainability within organizations.

Research on the Purchase Intention of Social Commerce Consumers in Video Streams: Dual Pathways of Affection and Rationality.

Social commerce blurs the boundary between online social interaction and online shopping. The emergence of video streams introduces novel marketing modalities to social commerce. However, there is a paucity of comprehensive studies investigating the impact of emerging marketing techniques such as short videos and live streaming on consumer purchase intention. This study employs Bourdieu's conceptual framework to construct a Field Theory-based model, investigating the impact of atmospheric and capital characteristics of social commerce platforms on consumer purchase intention through affective and rational pathways, respectively. A survey involving 515 Chinese social commerce consumers demonstrates that atmospheric characteristics (emotion and social presence) and capital characteristics (information quality and quantity) in video streams enhance similarity and power. Both similarity and power are associated with an increase in consumer purchase intention. This study validates the dual-path influence of social commerce characteristics and discusses theoretical and managerial implications.

Preparation of Three-Dimensional Porous Graphene by Hydrothermal and Chemical Reduction with Ascorbic Acid and its Electrochemical Properties.

Three-dimensional porous graphene (3D-PG) has attracted much attention due to its excellent electrochemical performance. Chemical reduction is one of common methods for preparing porous graphene. In order to develop a green and facile method for preparing three-dimensional porous graphene, in this paper, 3D-PG was fabricated by reduction of graphene oxide (GO) with ascorbic acid (AA) as reductant in hydrothermal condition based on non-toxic, non-flammable and mild reducing performance of ascorbic acid. It was found that the size and distribution of pores could be controlled by the reduction time and the concentration of AA in the solution. The pore sizes in R0, R1 and R2 were in the range of 0.5-1 µm, 1-1.5 µm, and 1.5-3 µm, respectively. It was found that the average pore size and volume increased along with the amount of reductants. Under optimal conditions - a reaction time of 20 h and a ratio of GO to AA=1 : 1 - the CV area of the so-obtained sample R1-20 at 100 mV was 0.06 and the specific capacitance of the 3D-PG electrode reaches 153.5 F ⋅ g-1 , which is suitable for use in supercapacitors.

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis.

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

Promoting Green Product Development Performance via Leader Green Transformationality and Employee Green Self-Efficacy: The Moderating Role of Environmental Regulation.

By integrating internal green self-efficacy and external environmental regulation, this research investigates the relationship between green transformational leadership and green product development performance. Taking 23 new energy vehicle enterprises in China as samples, we collected 298 valid questionnaires and verified the hypotheses through structural equation modeling. The results show that both green transformational leadership and green self-efficacy can promote green product development performance; green self-efficacy mediates the positive relationship between green transformational leadership and green product development performance, while environmental regulation positively moderates the mediating effect of green self-efficacy. Furthermore, environmental regulation and green self-efficacy interact to promote green product development performance. Our research provides a new perspective to understand how green transformational leadership is related to green product development performance and how this relationship is molded by contextual antecedents. Enterprises need to comprehensively consider the green influence of transformational leadership, green driving of employees themselves, and green linkage among organizations (macro policy guidance, passive market incentives, and self-issued actions) to improve green product development performance. Limitations and future scope are discussed.

A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images.

PURPOSE: Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation. METHODS: A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature. RESULTS: Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform. CONCLUSIONS: The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.

Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video.

Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Then the anatomic structure is identified in thyroid ultrasound image. Finally the rendering colors on these anatomic regions are extracted and validated to find the frames which satisfy the selection criteria. To test the performance of the proposed method, a thyroid elastogram dataset is built and totally 33 cases were collected. An experienced radiologist manually evaluates the selection results of the proposed method. Experimental results demonstrate that the proposed method finds the qualified rendering frame with 100% accuracy. The proposed scheme assists the radiologists to diagnose the thyroid diseases using the qualified rendering images.