Unabridged phase diagram for single-phased FeSexTe1-x thin films

A complete phase diagram and its corresponding physical properties are essential prerequisites to understand the underlying mechanism of iron based superconductivity. For the structurally simplest 11 (FeSeTe) system, earlier attempts using bulk samples have not been able to do so due to the fabrication difficulties. Here, thin FeSexTe1-x films with the Se content covering the full range were fabricated by using pulsed laser deposition method. Crystal structure analysis shows that all films retain the tetragonal structure in room temperature. Significantly, the highest superconducting transition temperature (TC = 20 K) occurs in the newly discovered domain, 0.6 - 0.8. The single-phased superconducting dome for the full Se doping range is the first of its kind in iron chalcogenide superconductors. Our results present a new avenue to explore novel physics as well as to optimize superconductors

Transcriptomic analysis of Synechocystis sp PCC6803 under low-temperature stress

In this study, cDNA microarrays were developed from 3569 mRNA reads to analyze the expression profiles of the transcriptomes of Synechocystis sp. PCC6803 under low temperature (LT) stress. Among the genes on the cDNA microarrays, 899 LT-affected genes exhibited a 1.5-fold (or greater) difference in expression compared with the genes from normal unstressed Synechocystis sp. PCC6803. Of the differentially expressed genes, 353 were up-regulated and 246 were down-regulated. The results showed that genes involved in photosynthesis were activated at LT (10A degrees C), including genes for photosystem I, photosystem II, photosynthetic electron transport, and cytochrome b6/f complex. Moreover, desB, one of four genes that encode the fatty acid desaturases, was also induced by LT. However, the LT conditions to some degree enhanced the transcription of some genes. In addition, LT (10A degrees C) may reduce cellular motility by regulating the transcription of spkA (sll1575), a serine/threonine protein kinase. The results reported in this study may contribute to a better understanding of the responses of the Synechocystis cell to LT, including pathways involved in photosynthesis and repair.In this study, cDNA microarrays were developed from 3569 mRNA reads to analyze the expression profiles of the transcriptomes of Synechocystis sp. PCC6803 under low temperature (LT) stress. Among the genes on the cDNA microarrays, 899 LT-affected genes exhibited a 1.5-fold (or greater) difference in expression compared with the genes from normal unstressed Synechocystis sp. PCC6803. Of the differentially expressed genes, 353 were up-regulated and 246 were down-regulated. The results showed that genes involved in photosynthesis were activated at LT (10A degrees C), including genes for photosystem I, photosystem II, photosynthetic electron transport, and cytochrome b6/f complex. Moreover, desB, one of four genes that encode the fatty acid desaturases, was also induced by LT. However, the LT conditions to some degree enhanced the transcription of some genes. In addition, LT (10A degrees C) may reduce cellular motility by regulating the transcription of spkA (sll1575), a serine/threonine protein kinase. The results reported in this study may contribute to a better understanding of the responses of the Synechocystis cell to LT, including pathways involved in photosynthesis and repair

SLAM-based localization method of coal mine underground robot with adaptability to dark illumination environment

Under the background of intelligent mine construction, the application of intelligent equipment has increasingly become the main content of mine intelligent transformation. Intelligent robots in coal mine that are designed for inspecting and dangerous area surveying and doing other tasks depends on the construction of digital map of underground coal mine and the localizing of the robot itself. But most of the traditional localizing methods are inefficient or even ineffective in the underground. Simultaneous Localization and Mapping (SLAM) has become a better choice for underground intelligent robot localization methods. However, due to the high cost of lidar and the poor performance of camera in low illumination environment, it is necessary to design a SLAM localization method that takes into account both low cost and adaptability to low illumination environment. Therefore, a localization method of robot with underground dark light environment adaptability in coal mine is proposed. Firstly, the real images of the gallery of a coal mine in Fengxian County, Baoji City, Shaanxi Province and the dataset of the camera and IMU required for SLAM were collected. According to the images, non-matching dark light and normal light dataset was made, and 3560 images were obtained after data amplification. An EnlightenGAN image enhancement network combined with self-attention module is designed, which takes into account the dependence of different regions of the image without relying on the paired dataset. Based on the ORB–SLAM3 framework, the whole local image detection is introduced to screen the input image, and an improved IMU initialization strategy based on analytical solution is introduced to improve the initialization speed, and the improved image enhancement network is transplanted to enhance the low illumination and uneven illumination images. Experiments on the EuRoC dataset show that the image enhancement-based underground coal mine robot localization method can reduce ERMS by 12.17% and ESD by 14.35% in low-light environments. In two actual coal mine roadway scenarios, the low-light environment can be identified, and the increasing number of feature points are extracted and the drift phenomenon of positioning trajectory is reduced by the SLAM system and. Finally, the localizing effect of the system is improved in the dark area of the roadway

Charging load prediction method for expressway electric vehicles considering dynamic battery state-of-charge and user decision

Accurate prediction of electric vehicle (EV) charging loads is a foundational step in the establishment of expressway charging infrastructures. This study introduces an approach to enhance the precision of expressway EV charging load predictions. The method considers both the battery dynamic state-of-charge (SOC) and user charging decisions. Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow features. A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow. An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values. Based on this foundation, an EV charging decision model was developed which considers expressway node distinctions. EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model. Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions. Finally, the proposed method is applied to a case study of the Lian-Huo expressway. An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways, thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios

Species delimitation in the Populus laurifolia complex (Salicaceae) based on phylogenetic and morphometric evidence

Due to significant morphological differences and extensive interspecific hybridization, there are numerous species complexes with taxonomic challenges in the genus Populus. Integrative taxonomy, which combines evidence of morphology, molecular phylogeny, niche differentiation, and reproductive isolation, provides the most effective approaches for species delimitation. The Populus laurifolia complex, which belongs to Populus subg. Tacamahaca (Salicaceae), is distributed in the Altai Mountains and Tianshan Mountains. This complex exhibits morphological variability, making species delimitation challenging. Due to limited sampling and systematic studies, its taxonomy has remained unresolved. In this study, 337 specimens, along with online digital samples representing nearly all wild populations, were collected. Morphological analyses were performed to evaluate key traits and clarify species boundaries. Phylogenetic relationships were reconstructed using concatenation and coalescent methods based on 566,375 nuclear single-nucleotide polymorphisms (SNPs). Ecological niche differentiation was assessed, and ABBA–BABA analysis was used to examine interspecific hybridization. The results revealed that this complex, based on a series of significant character states, could be morphologically distinguished into three species—P. laurifolia (Populus pilosa considered a synonym of P. laurifolia), Populus talassica, and Populus pamirica—which also correspond to three well-supported clades in the phylogenetic trees. P. pamirica exhibits some degree of ecological niche differentiation from P. talassica and P. laurifolia, whereas the latter two show minimal differentiation. Gene flow within the complex remains limited. This research underscores the importance of integrating multiple lines of evidence in the classification of Populus, providing a framework for future taxonomic studies