Minimal Cut Sets-Based Reliability Evaluation of the More Electric Aircraft Power System

The More Electric Aircraft (MEA) stands for the direction of aviation development in the new era, and the reliability of power systems on the MEA has attracted widespread attention. Based on the characteristics of MEA power systems, an equivalent method of electrical topology structure is presented in this article, and evaluation method is proposed which shows the reliability of the overall system with the reliability of specific nodes. Firstly, electrical topology structure of a MEA power system is converted into a network node diagram according to the proposed equivalent method. Then, the minimal path sets of specific nodes are obtained by the adjacent matrix algorithm, and the low-order minimal cut sets of disjointed are obtained. After that, the actual failure rate of components is converted to node failure rate, and the reliability of the overall system is evaluated by operational reliability indexes of specific nodes. Finally, taking the MEA A380 as an example, this paper compares and analyzes the reliability of AC loads, DC loads, and key loads to verify the validity and feasibility of the proposed evaluation method. This evaluation system can predict the weak points existing in the MEA power system, as well as providing theoretical support for maintenance schedule

Carbon benefits of wolfberry plantation on secondary saline land in Jingtai oasis, Gansu:A case study on application of the CBP model

The largest global source of anthropogenic CO2 emissions comes from the burning of fossil fuel and approximately 30% of total net emissions come from land use and land use change. Forestation and reforestation are regarded worldwide as effective options of sequestering carbon to mitigate climate change with relatively low costs compared with industrial greenhouse gas (GHG) emission reduction efforts. Cash trees with a steady augmentation in size are recognized as a multiple-beneficial solution to climate change in China. The reporting of C changes and GHG emissions for sustainable land management (SLM) practices such as afforestation is required for a variety of reasons, such as devising land management options and making policy. The Carbon Benefit Project (CBP) Simple Assessment Tool was employed to estimate changes in soil organic carbon (SOC) stocks and GHG emissions for wolfberry (Lycium barbarum L.) planting on secondary salinized land over a 10 year period (2004–2014) in the Jingtai oasis in Gansu with salinized barren land as baseline scenario. Results show that wolfberry plantation, an intensively managed ecosystem, served as a carbon sink with a large potential for climate change mitigation, a restorative practice for saline land and income stream generator for farmers in soil salinized regions in Gansu province. However, an increase in wolfberry production, driven by economic demands, would bring environmental pressures associated with the use of N fertilizer and irrigation. With an understanding of all of the components of an ecosystem and their interconnections using the Drivers-Pressures-State-Impact-Response (DPSIR) framework there comes a need for strategies to respond to them such as capacity building, judicious irrigation and institutional strengthening. Cost benefit analysis (CBA) suggests that wolfberry cultivation was economically profitable and socially beneficial and thus well-accepted locally in the context of carbon sequestration. This study has important implications for Gansu as it helps to understand the role cash trees can play in carbon emission reductions. Such information is necessary in devising management options for sustainable land management (SLM)

Energy Consumption, Carbon Emissions and Global Warming Potential of Wolfberry Production in Jingtai Oasis, Gansu Province, China

During the last decade, China's agro-food production has increased rapidly and been accompanied by the challenge of increasing greenhouse gas (GHG) emissions and other environmental pollutants from fertilizers, pesticides, and intensive energy use. Understanding the energy use and environmental impacts of crop production will help identify environmentally damaging hotspots of agro-production, allowing environmental impacts to be assessed and crop management strategies optimized. Conventional farming has been widely employed in wolfberry (Lycium barbarum) cultivation in China, which is an important cash tree crop not only for the rural economy but also from an ecological standpoint. Energy use and global warming potential (GWP) were investigated in a wolfberry production system in the Yellow River irrigated Jingtai region of Gansu. In total, 52 household farms were randomly selected to conduct the investigation using questionnaires. Total energy input and output were 321,800.73 and 166,888.80 MJ ha−1, respectively, in the production system. The highest share of energy inputs was found to be electricity consumption for lifting irrigation water, accounting for 68.52%, followed by chemical fertilizer application (11.37%). Energy use efficiency was 0.52 when considering both fruit and pruned wood. Nonrenewable energy use (88.52%) was far larger than the renewable energy input. The share of GWP of different inputs were 64.52% electricity, 27.72% nitrogen (N) fertilizer, 5.07% phosphate, 2.32% diesel, and 0.37% potassium, respectively. The highest share was related to electricity consumption for irrigation, followed by N fertilizer use. Total GWP in the wolfberry planting system was 26,018.64 kg CO2 eq ha−1 and the share of CO2, N2O, and CH4 were 99.47%, 0.48%, and negligible respectively with CO2 being dominant. Pathways for reducing energy use and GHG emission mitigation include: conversion to low carbon farming to establish a sustainable and cleaner production system with options of raising water use efficiency by adopting a seasonal gradient water pricing system and advanced irrigation techniques; reducing synthetic fertilizer use; and policy support: smallholder farmland transfer (concentration) for scale production, credit (small- and low-interest credit) and tax breaks

The Effect of Job Security on Deviant Behaviors in Diverse Employment Workplaces: From the Social Identity Perspective

Organizational scholars concur that job security can attach employees to a workplace and improve their job quality. The relationship between job security and employees’ deviant behaviors in the workplace, such as counterproductive work behavior (CWB), lacks insights into how or why this occurs, especially in a diversified employment context. To address these limitations, we developed a theoretical model of job security impact on employees’ CWB from the perspective of social identity. Analysis of employees (N = 208) and their supervisors in a China state-owned company were used to test the hypothesis. Results confirmed the negative relationship between job security and CWB; organizational identification partly mediates the relationship between job security and CWB. Moderated mediation analyses further indicate that the indirect effect of job security on CWB via organizational identification are stronger for temporary employees than for permanent employees. This article contributes to the understanding of job security’s impact on employees’ deviant behavior, practical implications and research aspects are discussed

Mapping multi-seasonal habitats of giant pandas to identify seasonal shifts

Summary: As a flagship species of biodiversity conservation globally, the giant panda has seasonal migration to cope with seasonal changes in available resources. Here, we have mapped the spatial distribution of multi-seasonal habitats of the giant panda across the Baishuijiang reserve in China. Results show that the spatial patterns are different in different seasons, generally, large patches are observed in the western part, while staggered clusters occur in the middle and eastern parts. That is, suitable habitats for giant pandas are mostly distributed in the west part. More than 75% of the predicted suitable habitats are within the core zone of the reserve year-round, indicating the core zone essentially meet giant panda’s ecological needs, although this range could potentially be expanded. This study provides valuable insights into the spatiotemporal migration patterns of endangered species and helps to guide conservation planning

Research on Intelligent Vehicle Trajectory Planning and Control Based on an Improved Terminal Sliding Mode

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term