Effective Spectral Emissivity of Gas Turbine Blades for Optical Pyrometry

Turbine blade temperature measurements are important for monitoring the turbine engine performance to protect the hot components from damage due to excess temperatures. However, the reflected radiation from the blades and the surrounding environment complicate the blade temperature measurements by optical pyrometers. This study characterizes the effect of the reflected radiation on the effective spectral emissivity of a three-dimensional turbine blade in a confined turbine space for optical pyrometry temperature measurements. The effective spectral emissivity distribution on a threedimensional blade was numerically determined for various wavelengths (0.8-15.0 lm) and actual blade surface emissivities for a specified turbine blade model. When the actual spectral emissivity of the blade surface is assumed to be 0.5, the effective spectral emissivity varies from 0.5 to 0.538 at the longer wavelength of 10.0 lm and further increases from 0.5 to 1.396 at the shorter wavelength of 0.9 lm. The results show that the effective emissivity distributions at shorter wavelengths differ greatly from those at longer wavelengths. There are also obvious differences between the effective spectral emissivity and the actual surface emissivity at shorter wavelengths. The effect of the effective emissivity on the temperature measurement accuracy, when using the optical pyrometry, was also investigated for various wavelengths (0.8-15.0 lm). The results show that the radiation reflected from the blades has less effect on the temperature measurements than on the effective emissivity, especially at the shorter wavelengths of 0.8-3.0 lm. However, the temperature measurements still need to be corrected using the effective spectral emissivity to improve the temperature calculation accuracy. This analysis provides guidelines for choosing the optimum measurement wavelengths for optical pyrometry in turbine engines

Allopatric divergence and hybridization within Cupressus chengiana (Cupressaceae), a threatened conifer in the northern Hengduan Mountains of western China

Having a comprehensive understanding of population structure, genetic differentiation and demographic history is important for the conservation and management of threatened species. High‐throughput sequencing (HTS) provides exciting opportunities to address a wide range of factors for conservation genetics. Here, we generated HTS data and identified 266,884 high‐quality single nucleotide polymorphisms from 82 individuals of Cupressus chengiana , to assess population genomics across the species' full range, comprising the Daduhe River (DDH), Minjiang River (MJR) and Bailongjiang River (BLJ) catchments in western China. admixture , principal components analysis and phylogenetic analyses indicated that each region contains a distinct lineage, with high levels of differentiation between them (DDH, MJR and BLJ lineages). MJR was newly distinguished compared to previous surveys, and evidence including coalescent simulations supported a hybrid origin of MJR during the Quaternary. Each of these three lineages should be recognized as an evolutionarily significant unit (ESU), due to isolation, differing genetic adaptations and different demographic history. Currently, each ESU faces distinct threats, and will require different conservation strategies. Our work shows that population genomic approaches using HTS can reconstruct the complex evolutionary history of threatened species in mountainous regions, and hence inform conservation efforts, and contribute to the understanding of high biodiversity in mountains.Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31622015Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31590821Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31370261Funding provided by: National Basic Research Program of China*Crossref Funder Registry ID: Award Number: 2014CB954100Funding provided by: Sichuan University Fundamental Research Funds for the Central Universities*Crossref Funder Registry ID: Award Number: SCU2019D013Funding provided by: Sichuan University Fundamental Research Funds for the Central Universities*Crossref Funder Registry ID: Award Number: SCU2018D006Funding provided by: National Basic Research Program of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100012166Award Number: 2014CB954100Funding provided by: Sichuan University Fundamental Research Funds for the Central UniversitiesCrossref Funder Registry ID: Award Number: SCU2019D013We identified 266,884 high-quality SNPs from 82 individuals to assess population genomics of Cupressus chengiana across its full range. To get a high-quality reference, single-molecule real-time (SMRT) sequencing was used to obtain the full-length transcriptome of C. chengiana. A total of 82 samples were collected for RNA-seq. We used bwa-mem to align the quality-filtered reads of each individual to the refences sequences. We used the "mpileup" command in SAMTOOLS to identify SNPs. Data were filtered with the following processes: SNPs with a mapping quality <30, a mapping depth <10, genotyping rate <50% per group, minor allele frequency (MAF) <5%, or in 5bps windows around any indel

Event-Driven Learning for Spiking Neural Networks

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. In this paper, we conduct a comprehensive examination of the existing event-driven learning algorithms, reveal their limitations, and propose novel solutions to overcome them. Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise neuronal spike timing and membrane potential, respectively, for effective learning. The two methods are extensively evaluated on static and neuromorphic datasets to confirm their superior performance. They outperform existing event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the CIFAR-100 dataset. In addition, we theoretically and experimentally validate the energy efficiency of our methods on neuromorphic hardware. On-chip learning experiments achieved a remarkable 30-fold reduction in energy consumption over time-step-based surrogate gradient methods. The demonstrated efficiency and efficacy of the proposed event-driven learning methods emphasize their potential to significantly advance the fields of neuromorphic computing, offering promising avenues for energy-efficiency applications

Analysis and Fault-Tolerant Control for Dual-Three-Phase PMSM Based on Virtual Healthy Model

Dual-three-phase permanent magnet synchronous machines (DTP-PMSMs) are famous for their fault-tolerant capability. However, the complex modeling, high copper loss, and torque ripple under postfault operation limit their further application. In this article, a fault-tolerant control (FTC) strategy is developed for DTP-PMSMs under the open-phase fault (OPF) with straightforward modeling and smooth output torque. The virtual healthy DTP-PMSM model, where the coordinate transformation, the modulation strategy, and the controller structure remain unchanged under OPF, is adopted in the proposed FTC scheme. And the current references are derived in sinusoidal waves with minimum copper loss. The inaccurate transmission of control signals under OPF is also focused on. Comprehensive theoretical analysis shows the relationship between the controller output voltage and the actual stator voltage should be considered in the proposed FTC strategy; otherwise, distortion in torque and current will be introduced. The voltage compensation is utilized to compensate for the voltage difference and ensure the smooth torque output. Besides, a quasi proportional resonance controller is designed to further suppress the residual torque ripple. The proposed strategy will not induce complex implementation and heavy computation burden. The simulation and experimental results prove the analysis and the effectiveness of the proposed strategy

Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes

We advocate the idea of the natural-language-driven(NLD) simulation to efficiently produce the object interactions between multiple objects in the virtual road scenes, for teaching and testing the autonomous driving systems that should take quick action to avoid collision with obstacles with unpredictable motions. The NLD simulation allows the brief natural-language description to control the object interactions, significantly reducing the human efforts for creating a large amount of interaction data. To facilitate the research of NLD simulation, we collect the Language-to-Interaction(L2I) benchmark dataset with 120,000 natural-language descriptions of object interactions in 6 common types of road topologies. Each description is associated with the programming code, which the graphic render can use to visually reconstruct the object interactions in the virtual scenes. As a methodology contribution, we design SimCopilot to translate the interaction descriptions to the renderable code. We use the L2I dataset to evaluate SimCopilot's abilities to control the object motions, generate complex interactions, and generalize interactions across road topologies. The L2I dataset and the evaluation results motivate the relevant research of the NLD simulation.Comment: 12 pages, 6 figure

Evolutionary history of two rare endemic conifer species from the eastern Qinghai-Tibet plateau

BACKGROUND AND AIMS: Understanding the population genetics and evolutionary history of endangered species is urgently needed in an era of accelerated biodiversity loss. This knowledge is most important for regions with high endemism that are ecologically vulnerable, such as the Qinghai–Tibet Plateau (QTP). METHODS: The genetic variation of 84 juniper trees from six populations of Juniperus microsperma and one population of Juniperus erectopatens, two narrow-endemic junipers from the QTP that are sister to each other, was surveyed using RNA-sequencing data. Coalescent-based analyses were used to test speciation, migration and demographic scenarios. Furthermore, positively selected and climate-associated genes were identified, and the genetic load was assessed for both species. KEY RESULTS: Analyses of 149 052 single nucleotide polymorphisms showed that the two species are well differentiated and monophyletic. They diverged around the late Pliocene, but interspecific gene flow continued until the Last Glacial Maximum. Demographic reconstruction by Stairway Plot detected two severe bottlenecks for J. microsperma but only one for J. erectopatens. The identified positively selected genes and climate-associated genes revealed habitat adaptation of the two species. Furthermore, although J. microsperma had a much wider geographical distribution than J. erectopatens, the former possesses lower genetic diversity and a higher genetic load than the latter. CONCLUSIONS: This study sheds light on the evolution of two endemic juniper species from the QTP and their responses to Quaternary climate fluctuations. Our findings emphasize the importance of speciation and demographic history reconstructions in understanding the current distribution pattern and genetic diversity of threatened species in mountainous regions

Case report: Resection of a giant right ventricular myxoma

Myxoma constitutes the main subtype of all benign cardiac tumors, tending to be more common in women and occurring mostly in the left and right atria. Its classic clinical presentations are intracardiac obstruction, embolization, and systemic or constitutional symptoms, such as fever, in decreasing order. Several imaging techniques such as echocardiography, computed tomography, and angiocardiography contribute to the diagnosis of myxoma, ruling out significant coronary diseases, and assessment of myocardial invasion and tumor involvement of adjacent structures. Surgical resection is the only effective therapeutic option for patients with cardiac myxoma. Here, we report a unique case of a middle-aged man who presented with a giant myxoma and a 3-day history of chest tightness and shortness of breath after physical activity. Subsequently, transthoracic echocardiography revealed a mass of solid echodensity located within the right ventricle, complicated by abnormal hemodynamics. A cardiac computed tomographic angiography showed a large homogeneous density filling defect consuming most parts of the right ventricle and protruding from beat to beat. A surgical resection and histological study later successfully confirmed the diagnosis, and the patient's postoperative recovery course was found to be uneventful

Study of brain network alternations in non-lesional epilepsy patients by BOLD-fMRI

ObjectiveTo investigate the changes of brain network in epilepsy patients without intracranial lesions under resting conditions.MethodsTwenty-six non-lesional epileptic patients and 42 normal controls were enrolled for BOLD-fMRI examination. The differences in brain network topological characteristics and functional network connectivity between the epilepsy group and the healthy controls were compared using graph theory analysis and independent component analysis.ResultsThe area under the curve for local efficiency was significantly lower in the epilepsy patients compared with healthy controls, while there were no differences in global indicators. Patients with epilepsy had higher functional connectivity in 4 connected components than healthy controls (orbital superior frontal gyrus and medial superior frontal gyrus, medial superior frontal gyrus and angular gyrus, superior parietal gyrus and paracentral lobule, lingual gyrus, and thalamus). In addition, functional connectivity was enhanced in the default mode network, frontoparietal network, dorsal attention network, sensorimotor network, and auditory network in the epilepsy group.ConclusionThe topological characteristics and functional connectivity of brain networks are changed in in non-lesional epilepsy patients. Abnormal functional connectivity may suggest reduced brain efficiency in epilepsy patients and also may be a compensatory response to brain function early at earlier stages of the disease