Electromagnetic Loss Analysis of a Linear Motor System Designed for a Free-Piston Engine Generator

A free-piston engine generator is a new type of power generating device, which has the advantages of high efficiency and simple structure. In this paper, a linear motor system composed of a moving-coil linear motor with axial magnetized magnets and a H-bridge pulse-width modulation (PWM) rectifier is designed for portable free-piston engine generators. Based on the finite-element model of the motor and physical model of the rectifier, the combined electromagnetic model is presented and then validated by the prototype-tested results. The electromagnetic processes of the linear motor system are simulated. The electromagnetic losses during the standard working cycle are analyzed. Under the rated reciprocating frequency of 50 Hz and the rated reciprocating stroke of 36 mm, the mechanical-to-electrical energy conversion efficiency of 86.3% can be obtained by the linear motor system, which meets the requirement of portable free-piston engine generators

Towards a Fairer Green city: measuring unfairness in daily accessible greenery in Chengdu’s Central city

Urban green spaces exert beneficial effects on both individuals and communities. However, as urban sprawl intensifies, socioeconomic disparities widen, and populations burgeon, the concepts of green fairness and environmental justice confront substantial hurdles. The daily exposure to greenery emerges as a crucial determinant of these factors, yet no comprehensive methodologies currently exist to gauge the levels of daily accessible urban greenery or to probe the distribution of green inequities. In this research, we harness the capabilities of Spatial Design Network Analysis (sDNA) to scrutinize spatial choice and integration, using the metropolis of Chengdu as a case study. Three indicators representing daily accessed urban greenery are utilized, including the Green View Index (GVI) at the street level, the assessment of green spaces based on the Normalized Differential Vegetation Index (NDVI), and the level of greenery within the visible range of buildings. Green Accessibility Index (GAI) was further proposed and calculated for three states of commuting, recreation, and work to synthesize the accessibility and greenness levels. The distribution of green unfairness in the study area are evaluated using bivariate local spatial autocorrelation. Our findings reveal that (1) frequent expressway commuting and existing greenery does not satisfy urban fairness needs. (2) Significant differences in unfair areas of building visible greenery (3) Unfair areas are concentrated in high-income neighborhoods (4) Severe unfairness between greenery and population in large cities, where most people do not enjoy the benefits of adequate greenery. We provide recommendations based on these findings, thereby offering actionable insights to optimize the spatial distribution of green unfairness through enhanced accessibility of urban greenery

Leveraging Diffusion Modeling for Remote Sensing Change Detection in Built-Up Urban Areas

In the evolving domain of built-up area surveillance, remote sensing technology emerges as an essential instrument for Change Detection (CD). The introduction of deep learning has notably augmented the precision and efficiency of CD. This study focuses on the integration of deep learning methodologies, specifically the diffusion model, into remote sensing CD tasks for built-up urban areas. The goal is to explore the potential of a pre-trained Text-to-Image Stable Diffusion model for CD tasks and propose a new model called the Difference Guided Diffusion Model (DGDM). DGDM incorporates multiple pre-training techniques for image feature extraction and introduces the Difference Attention Module (DAM) and an Image-to-Text (ITT) adapter to improve the correlation between image features and text semantics. Additionally, DGDM utilizes attention generated from pre-trained Denoise UNet to enhance CD predictions. The effectiveness of the proposed method is evaluated through comparative assessments on four datasets, demonstrating its superiority over previous deep learning methods and its ability to produce more precise and detailed CD results. This innovative approach offers a promising direction for future research in urban remote sensing, emphasizing the potential of diffusion models in enhancing urban CD precision and automation. Our implementation code is available at https://github.com/morty20200301/cd-diffusion

Transcriptional profiling of <it>Medicago truncatula </it>under salt stress identified a novel CBF transcription factor MtCBF4 that plays an important role in abiotic stress responses

<p>Abstract</p> <p>Background</p> <p>Salt stress hinders the growth of plants and reduces crop production worldwide. However, different plant species might possess different adaptive mechanisms to mitigate salt stress. We conducted a detailed pathway analysis of transcriptional dynamics in the roots of <it>Medicago truncatula </it>seedlings under salt stress and selected a transcription factor gene, <it>MtCBF4</it>, for experimental validation.</p> <p>Results</p> <p>A microarray experiment was conducted using root samples collected 6, 24, and 48 h after application of 180 mM NaCl. Analysis of 11 statistically significant expression profiles revealed different behaviors between primary and secondary metabolism pathways in response to external stress. Secondary metabolism that helps to maintain osmotic balance was induced. One of the highly induced transcription factor genes was successfully cloned, and was named <it>MtCBF4</it>. Phylogenetic analysis revealed that MtCBF4, which belongs to the AP2-EREBP transcription factor family, is a novel member of the CBF transcription factor in <it>M. truncatula</it>. MtCBF4 is shown to be a nuclear-localized protein. Expression of <it>MtCBF4 </it>in <it>M. truncatula </it>was induced by most of the abiotic stresses, including salt, drought, cold, and abscisic acid, suggesting crosstalk between these abiotic stresses. Transgenic <it>Arabidopsis </it>over-expressing <it>MtCBF4 </it>enhanced tolerance to drought and salt stress, and activated expression of downstream genes that contain DRE elements. Over-expression of <it>MtCBF4 </it>in <it>M. truncatula </it>also enhanced salt tolerance and induced expression level of corresponding downstream genes.</p> <p>Conclusion</p> <p>Comprehensive transcriptomic analysis revealed complex mechanisms exist in plants in response to salt stress. The novel transcription factor gene <it>MtCBF4 </it>identified here played an important role in response to abiotic stresses, indicating that it might be a good candidate gene for genetic improvement to produce stress-tolerant plants.</p