MEMS 411: Portable Bridge Crane with Pulleys

The bridge crane demonstrates dynamics behavior that the flexible mode has a natural frequency depending on the string length. In this project, a portable bridge crane is designed and made to demonstrate this dynamical phenomena by changing speed and string length. The portable bridge crane requires a light mass and a small size. At the same time, because it needs to be observed in a two-dimensional plane, the trolley must be able to move horizontally on a bridge with tracks, and the speed of the trolley needs to be controlled by a speed controllable motor. The crane needs to suspend a weight of 1 kg to demonstrate the change in natural frequency. The length of the rope needs to be adjustable. In addition, in order to ensure the stability of the instrument, it is necessary to reduce the impact force of the collision when the trolley hits the ends of the rail. Therefore, a soft stop is necessary to stop the motion of the cart before the collision at the end. In this way, the stability of the structure can be guaranteed in order to avoid tipping due to collision. Our portable bridge crane will demonstrate the effect of changing the speed and the string length on the natural frequency

Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication

Semantic communication serves as a novel paradigm and attracts the broad interest of researchers. One critical aspect of it is the multi-user semantic communication theory, which can further promote its application to the practical network environment. While most existing works focused on the design of end-to-end single-user semantic transmission, a novel non-orthogonal multiple access (NOMA)-based multi-user semantic communication system named NOMASC is proposed in this paper. The proposed system can support semantic tranmission of multiple users with diverse modalities of source information. To avoid high demand for hardware, an asymmetric quantizer is employed at the end of the semantic encoder for discretizing the continuous full-resolution semantic feature. In addition, a neural network model is proposed for mapping the discrete feature into self-learned symbols and accomplishing intelligent multi-user detection (MUD) at the receiver. Simulation results demonstrate that the proposed system holds good performance in non-orthogonal transmission of multiple user signals and outperforms the other methods, especially at low-to-medium SNRs. Moreover, it has high robustness under various simulation settings and mismatched test scenarios.Comment: accepted by IEEE Transactions on Cognitive Communications and Networkin

Particle‐in‐cell simulation of electron cyclotron harmonic waves driven by a loss cone distribution

Electron Cyclotron Harmonic (ECH) waves driven by a loss cone distribution are studied in this work by self‐consistent particle‐in‐cell simulations. These waves have been suggested to play an important role in diffuse auroral precipitation in the outer magnetosphere. However, particle simulation of this instability is difficult because the saturation amplitude of the wave driven by a realistic size loss cone distribution is very small. In this work we use an extraordinarily large number of particles to reduce simulation noise so that the growth and saturation of ECH waves can be investigated. Our simulation results are consistent with linear theory in terms of growth rate, and with observation in terms of wave amplitude. We demonstrate that the heating of cold electrons is negligible and non‐resonant, different from previous conclusions, and suggest that the saturation of the wave is caused by the filling of the loss cone of hot electrons

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia : a systematic literature review and external validation study

Background People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. Methods A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). Results Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). Conclusions In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.Peer reviewe

Flight training changes the brain functional pattern in cadets

IntroductionTo our knowledge, this is the first study to use MRI (Magnetic Resonance Imaging) before and after an intensive flight training. This study aimed to investigate the effectiveness of flight training in civil flying cadets.MethodsThe civil flying cadets and controls completed two study visits. Visit 1 was performed in 2019, and high spatial resolution structural image and resting-state functional MRI data were collected. The second visit was completed in 2022. In addition to the MRI data mentioned above, participants completed the cognitive function assessment at the second visit.ResultsMixed-effect regression model analysis found that flight training enhanced the degree centrality (DC) values of the left middle frontal gyrus and left lingual gyrus. The subsequent correlation calculation analysis suggested a possible relationship between these alterations and cognitive function.DiscussionThese results suggest that flight training might promote the DC value of the prefrontal and occipital cortices and, in turn, enhance their executive function

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BACKGROUND: People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures. METHODS: A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). RESULTS: Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05). CONCLUSIONS: In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.This work was supported by 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant no. ZYGD18017 to NT)