Construction and Application of Cloud Computing Model for Reciprocal and Collaborative Knowledge Management

Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.

Unique Characteristics of Vertical Carbon Nanotube Field-effect Transistors on Silicon

A vertical carbon nanotube field-effect transistor(CNTFET) based on silicon(Si) substrate has been proposed and simulated using a semi-classical theory. A single-walled carbon nanotube(SWNT) and an n-type Si nanowire in series construct the channel of the transistor. The CNTFET presents ambipolar characteristics at positive drain voltage(Vd) and n-type characteristics at negative Vd. The current is significantly influenced by the doping level of n-Si and the SWNT band gap. The n-branch current of the ambipolar characteristics increases with increasing doping level of the n-Si while the p-branch current decreases. The SWNT band gap has the same influence on the p-branch current at a positive Vd and n-type characteristics at negative Vd. The lower the SWNT band gap, the higher the current. However, it has no impact on the n-branch current in the ambipolar characteristics. Thick oxide is found to significantly degrade the current and the subthreshold slope of the CNTFETs.

Prognosis in prolonged coma patients with diffuse axonal injury assessed by somatosensory evoked potential

A total of 43 prolonged coma patients with diffuse axonal injury received the somatosensory evoked potential examination one month after injury in the First Affiliated Hospital, School of Medicine, Zhejiang University in China. Somatosensory evoked potentials were graded as normal, abnormal or absent （grades I-III） according to N20 amplitude and central conduction time. The outcome in patients with grade III somatosensory evoked potential was in each case unfavorable. The prognostic accuracy of grade III somatosensory evoked potential for unfavorable and non-awakening outcome was 100% and 80%, respectively. The prognostic accuracy of grade I somatosensory evoked potential for favorable and wakening outcome was 86% and 100%, respectively. These results suggest that somatosensory evoked potential grade is closely correlated with coma severity and degree of recovery. Somatosensory evoked potential is a valuable diagnostic tool to assess prognosis in prolonged coma patients with diffuse axonal injury.

Micro gas turbine:Developments,applications,and key technologies on components

Owing to their precedent characteristics,micro gas turbines(MGTs)have been favored as popular power machinery in plenty of energy systems such as distributed energy systems,range extenders,solar power generations,fuel cell systems and individual power supplies.Their specific features essentially include but are not limited to strong fuel adaptability,low emissions,flexible structure,and easy maintenance.Over the past 20 years,various types of MGTs have been developed.Classical and forward-looking technologies have been employed in the design and production of MGTs and their components.Among them,fully radial flow structures,gas lubricated bearings and efficient recuperators are typical approaches to enhance the overall performance and compactness,however,the exploitation of ceramic based materials and intelligent algorithms in component design can also assist in improving the performance.The applications of MGTs have been expanded to many fields,and the research on related components has also made new progress.Due to the time frame,there is no systematic summary of the latest relevant research,so it is essential to have a comprehensive understanding of the applications of MGTs and their pertinent components.This paper aims to present a comprehensive review on MGTs,covering the development status,applications,factors of performance and representative explorations of their components.Some investigations regarding the characteristics of commercial MGTs are also conducted.Applications in distributed energy,range extenders,solar generations,and fuel cell systems are distinctly introduced.Recent research work on compressors,turbines,combustors,recuperators,and rotor systems are reviewed and analyzed.The technologies and methods associated with materials,manufacturing,and cycles beneficial to the future development of MGTs are also explained and discussed in some detail.

Comparative Study on Torque Performance of Five-phase Single-Stator and Double-Stator Permanent Magnet Synchronous Motors

This paper compares the torque characteristics of single stator permanent magnet synchronous motor(PMSM)and double-stator PMSM under different split-ratios,air-gap lengths and shaft diameters by finite element method.Firstly,the effects of split-ratio towards the torque characteristics of the two motor structures under different air-gap lengths are researched,the results show that the optimal split-ratios of the two motor structures do not change under different air-gap lengths,and the optimal split-ratio of the double-stator motor is greater than that of single-stator,and the torque of the double-stator motor is greater than that of single-stator motor with arbitrary split-ratio under the same air-gap length;Finally,the effects of the shaft diameter to the torque of the two motor structures are investigated,obtaining that with the increasing of shaft diameter,the electromagnetic torque of the single-stator motor is almost unchanged,however,the torque of the double-stator is gradually reduced,when the shaft diameter reached a certain extent,the electromagnetic torque of the double-stator motor is smaller than that of single-stator motor with the split ratio within a certain range,and the torque/quality ratio of the double-stator motor is smaller than that of single-stator motor with their optimal split ratio separately.

Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only.Off-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available.To overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd counting.Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains.Specifically,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning process.Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

Prompt learning in computer vision: a survey

Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.

曲线网格下带有自由流和涡流保存特性的低耗散数值方案

本文提出了一种改进的THINC数值方案,用于在曲线网格下保持自由来流和等熵涡特性,其中采用对称保守度量方法来消除坐标转换系数在数值离散中诱导的几何误差.由于非等间距的网格会导致原本带有固定陡度参数的THINC方案的数值精度和计算稳定性降低.因此,本文引入了一种带有自适应陡度参数的THINC方案用于减少数值耗散,其中陡度参数根据网格间隔的大小建立方程实现自适应缩放.数值测试表明,与原始THINC方案相比,新的THINC方案能够有效地在一般结构化曲线网格下保持自由流和涡旋保存,并且能够更加精准地拟合流场中的各类型间断和小尺度结构.此外,通过边界变差最小(BVD)原理,将改进的THINC方案与加权本质非振荡方案(WENO)相结合,兼顾了光滑解和间断解的数值精度.本文在波浪形网格和随机网格下设置了一系列一维和二维数值算例,分别测试了在有限差分和有限体积框架下改进的THINC方法与低耗散的WENO-THINC综合方案的性能.数值验证结果表明改进后的数值方案可以有效地减少非物理振荡和数值耗散,相较于原始方案和同类型数值方案有较明显的优势.

Responses of Patients with Disorders of Consciousness to Habit Stimulation:A Quantitative EEG Study

Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smoking habits were recruited and 64-channel EEG signals were acquired both at the resting state and at three stimulation states. Wavelet transformation and nonlinear dynamics were used to extract the features of EEG signals and four brain lobes were selected to investigate the degree of EEG response to habit stimulation. Results showed that the highest degree of EEG response was from the callname stimulation, followed by habit and music stimulations. Significant differences in EEG wavelet energy and response coefficient were found both between habit and music stimulation, and between habit and call-name stimulation. These findings prove that habit stimulation induces relatively more intense EEG responses in DOC patients than music stimulation, suggesting that it may be a relevant additional method for eliciting patient arousal.