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105. Association of cigarette smoking with retinal capillary plexus: an optical coherence tomography angiography study

Author

Zhu, Xiaoxuan, primary, Yang, Kai, additional, Xiao, Yunfan, additional, Ye, Cong, additional, Zheng, Jingwei, additional, Su, Binbin, additional, Zheng, Yang, additional, Zhang, Xinyao, additional, Shi, Keai, additional, Li, Chunmei, additional, Lu, Fan, additional, Qu, Jia, additional, Li, Ming, additional, and Cui, Lele, additional

Published
2022
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107. Additional file 1 of Age- and sex-related differences in the retinal capillary plexus in healthy Chinese adults

Author

Su, Binbin, Zhu, Xiaoxuan, Yang, Kai, Xiao, Yunfan, Li, Chunmei, Shi, Keai, Qu, Jia, Lu, Fan, Li, Ming, and Cui, Lele

Abstract

Additional file 1: Figure S1. Vessel densities of the whole measured area, parafovea, and four quadrants (temporal, superior, nasal, and inferior) in the (a, c) SCP and (b, d) DCP measured with optical coherence tomography angiography stratified by age in males and females. Table S1. Baseline characteristics of male participants in the study. Table S2. Baseline characteristics of female participants in the study. Table S3. Vessel densities of the whole measured area, parafovea, and four quadrants in the SCP and DCP stratified by age and sex.

Published
2022
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111. Ceramic Wafer Scintillation Screen by Utilizing Near‐Unity Blue‐Emitting Lead‐Free Metal Halide (C8H20N)2Cu2Br4.

Author

Su, Binbin, Jin, Jiance, Han, Kai, and Xia, Zhiguo

Subjects
*SCINTILLATORS, *METAL halides, *ELECTRONIC equipment, *STOKES shift, *X-ray imaging, *CERAMICS
Abstract

Scintillators with high light yield, low detection limit, large X‐ray attenuation efficiency as well as stable and nontoxic compositions are of great importance for radiation detection applications. Here, 0D (C8H20N)2Cu2Br4 single crystals are obtained and show blue emission peaking at 468 nm with a near‐unity photoluminescence quantum yield of 99.7%, a large Stokes shift of 148 nm (i.e., negligible self‐absorption), and a good environmental stability along with strong X‐ray absorption capability. Moreover, a high light yield of up to ≈ 91 300 photons/MeV and a low detection limit of 52.1 nGyair s−1 are realized, which is more than one hundred times lower than the dose rate of 5.5 µGyair s−1 required for X‐ray medical diagnostics. (C8H20N)2Cu2Br4 ceramic wafer scintillation screen is fabricated by a cold pressing sintering process, and the clear contrast images of opaque metal box and electronic component with a spatial resolution of 9.54 lp mm−1 are realized. This study not only designs a new lead‐free metal halide scintillator, but also develops a universal strategy for the preparation of large‐sized scintillator screen in nondestructive X‐ray imaging. [ABSTRACT FROM AUTHOR]

Published
2023
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112. Ceramic Wafer Scintillation Screen by Utilizing Near‐Unity Blue‐Emitting Lead‐Free Metal Halide (C8H20N)2Cu2Br4.

Author

Su, Binbin, Jin, Jiance, Han, Kai, and Xia, Zhiguo

Subjects
SCINTILLATORS, METAL halides, ELECTRONIC equipment, STOKES shift, X-ray imaging, CERAMICS
Abstract

Scintillators with high light yield, low detection limit, large X‐ray attenuation efficiency as well as stable and nontoxic compositions are of great importance for radiation detection applications. Here, 0D (C8H20N)2Cu2Br4 single crystals are obtained and show blue emission peaking at 468 nm with a near‐unity photoluminescence quantum yield of 99.7%, a large Stokes shift of 148 nm (i.e., negligible self‐absorption), and a good environmental stability along with strong X‐ray absorption capability. Moreover, a high light yield of up to ≈ 91 300 photons/MeV and a low detection limit of 52.1 nGyair s−1 are realized, which is more than one hundred times lower than the dose rate of 5.5 µGyair s−1 required for X‐ray medical diagnostics. (C8H20N)2Cu2Br4 ceramic wafer scintillation screen is fabricated by a cold pressing sintering process, and the clear contrast images of opaque metal box and electronic component with a spatial resolution of 9.54 lp mm−1 are realized. This study not only designs a new lead‐free metal halide scintillator, but also develops a universal strategy for the preparation of large‐sized scintillator screen in nondestructive X‐ray imaging. [ABSTRACT FROM AUTHOR]

Published
2023
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113. Band Alignment Engineering in ns2 Electrons Doped Metal Halide Perovskites.

Author

Han, Kai, Qiao, Jianwei, Zhang, Shuai, Su, Binbin, Lou, Bibo, Ma, Chong‐Geng, and Xia, Zhiguo

Subjects
METAL halides, PEROVSKITE, X-ray imaging, SCINTILLATORS, THREE-dimensional imaging, ENGINEERING, LUMINESCENCE spectroscopy
Abstract

Luminescent metal halide perovskites (MHPs) open new avenues for highly efficient radiation detection. To challenge the state‐of‐art technology, fundamental understanding of factors controlling radiation light yield of MHP scintillators is urgent. Herein, a design method is established by simultaneously considering charge‐transfer and recombination efficiencies via band alignment engineering in doped MHPs materials, and this strategy is corroborated experimentally and computationally by applying it to the luminescence of ns2 electron (Sb3+, Bi3+, and Te4+) doped vacancy‐ordered double perovskite Cs2ZrCl6. Alloying Te4+ into Cs2ZrCl6 is optimized and significantly improves the scintillation performance, including a twofold increase in light yield and a threefold increase in detection limit over pristine Cs2ZrCl6, and high‐resolution X‐ray imaging with 20 μm for 2D and 0.2 mm for 3D imaging. It is believed that doping engineering in MHPs enabling band alignment method holds great potential for the development of next‐generation MHP scintillators. [ABSTRACT FROM AUTHOR]

Published
2023
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123. Human motion prediction using wearable sensors and machine Learning

Author

Su, Binbin

Subjects
Annan maskinteknik, Other Mechanical Engineering, wearable sensors, gait segmentation, lower limb angular velocity, machine learning, deep learning, reinforcement learning
Abstract

Accurately measuring and predicting human movement is important in many contexts, such as in rehabilitation and the design of assistive devices. Thanks to the development and availability of a wide variety of sensors, scientists study human movement in many settings and capture characteristic properties unique to individuals as well as to larger study populations. Inertial measurement units (IMU), which contain accelerometers and gyroscopes, measure segment accelerations and angular velocities, and electromyography (EMG) sensors measure muscle excitation. These types of wearable sensors can be donned at the same time and can record data at a high frequency, potentially resulting in a large amount of data. Machine learning (ML) is an effective tool to extract the prominent features and make statistical inferences from data and has the potential to enhance human motion analyses through data-driven prediction. The overall aim of this thesis was to predict human motion through data-driven approaches and musculoskeletal simulations using wearable sensors and ML. A deterministic machine learning approach using a convolutional neural network (CNN) was first proposed to segment gait cycles into five phases based on experimental IMU data in subjects at different walking speeds. The proposed CNN was able to capture kinematic characteristics in raw IMU data, such as linear acceleration, rotational velocity, and magnetic field, and distinguish different gait phases. In recognizing all gait phases, it achieved an overall accuracy of 97.5% on a well-trained model, with up to 99.6% accuracy in detecting the swing phase. Our results also showed walking speed did not have a major influence on the overall gait phase recognition accuracy for people with typical gait patterns. However, while the swing was most accurately recognized, the terminal stance was least accurately recognized, and even more so at lower walking speeds. We then developed a long short-term (LSTM) network to predict both gait phase (loading response, midstance, terminal stance, pre-swing, and swing) and gait trajectory (angular velocities of thigh, shank, and foot segments) in up to the subsequent 200ms, based on immediately prior data. The overall accuracy of gait phase prediction was up to 94%, with the swing phase the most accurately predicted (97%). Our results also showed a high correlation between predicted and true values of the angular velocity of the thigh, shank, and foot segments. People walk on different terrains daily, for instance, level-ground walking, ramp/stairs ascent/descent, and stepping over obstacles. Movements patterns change as people move from one terrain to another, i.e. transition from one locomotion mode to another. Locomotion modes are typically labeled between two gait events, foot contact (FC) and toe off (TO). Since there is no exact instance for discriminating the transition between two locomotion modes, we identified TO as the critical gait event. We integrated locomotion mode prediction and gait event identification into one machine learning framework comprising two multilayer perceptrons (MLP), using fused data from two types of wearable sensors, namely EMG sensors and IMUs. The first MLP successfully identified FC and TO; FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 and -5.3 ms for FC and TO respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time before the critical events, using EMG and IMU signals as input features. Data-driven approaches using wearable sensors are incapable of modeling the mechanism between neuromuscular control and wearable sensor outputs. Musculoskeletal simulation can, on the other hand, explain the interactions between muscular control, kinematics, and kinetics in human motion. Thus, we integrated a reinforcement learning algorithm, a reflex-based controller, and a musculoskeletal model including trunk, pelvis, and leg segments to simulate reasonably realistic human walking at different speeds. We further generated pathological gaits that may result from ankle plantarflexor weakness using the same approach. The simulated hip and knee angles correlate reasonably well with reported experimental data, though less so for ankle kinematics. The computed muscle excitations in major low limb muscles largely correspond to the expected on-off timing of these muscles during walking. In summary, the studies in this thesis describe and predict human movement with wearable sensors and machine learning algorithms. We detected and predicted gait phases and events, predicted segment movements and identified intended transitions between walking modes during the stance phase of the previous gait cycle on the same side, before the step into the new mode, all based on data from wearable sensors. This has important potential implications in continuous monitoring and analysis of a person's movements outside a lab environment. The musculoskeletal simulation provided insight into the relationship between neuromuscular control and sensory feedback, which could also be applied to better understand and predict likely changes in gait when changes occur in neuromuscular control. Our approaches combining wearable sensors and machine learning could be ultimately applied to facilitate the design of exoskeletons that can provide seamless assistance for people with motor disorders.

Published
2021

125. Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons

Author

Su, Binbin, Liu, Yixing, Gutierrez-Farewik, Elena, Su, Binbin, Liu, Yixing, and Gutierrez-Farewik, Elena

Abstract

People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors-specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and -5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side's mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders., QC 20220308

Published
2021
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126. Highly Distorted Antimony(III) Chloride [Sb2Cl8]2− Dimers for Near‐Infrared Luminescence up to 1070 nm.

Author

Su, Binbin, Geng, Shining, Xiao, Zewen, and Xia, Zhiguo

Subjects
*CHLORINE, *ANTIMONY, *DIMERS, *LUMINESCENCE, *METAL halides, *DEGREES of freedom, *DENSITY functional theory, *CHLORIDES
Abstract

Zero‐dimensional (0D) hybrid metal halides with unique compositional and structural tunability appear as an emerging class of luminescent materials, but near‐infrared (NIR) emitters therein are largely unexplored to date. This study presents three novel 0D hybrid antimony chlorines with edge‐sharing [Sb2Cl8]2− dimers, showing unusual room‐temperature broadband NIR emission with the maximum emission wavelength up to 1070 nm. Photoluminescence studies and density functional theory calculation demonstrate that the emissions originate from the highly localized excitons, and that the confined [Sb2Cl8]2− dimers in these structures show low symmetry and a large degree of structural freedom. These hybrid antimony chlorines with [Sb2Cl8]2− dimers expand the range of new NIR materials in 0D metal halides. [ABSTRACT FROM AUTHOR]

Published
2022
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127. Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Author

Su, Binbin and Gutierrez-Farewik, Elena

Subjects
Annan maskinteknik, Other Mechanical Engineering, multi-step forecasting, gait segmentation, lower limb angular velocity, machine learning, deep learning
Abstract

Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank, and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing, and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly the swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTMnetwork to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases. QC 20211110

Published
2020

128. Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Author

Su, Binbin, Smith, Christian, Gutierrez-Farewik, Elena, Su, Binbin, Smith, Christian, and Gutierrez-Farewik, Elena

Abstract

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit(IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance., QC 20211028

Published
2020
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139. Numerical investigation of squeeze film lubrication on bioinspired hexagonal patterned surface.

Author

Su, Binbin, Zou, Xianghe, and Huang, Lirong

Subjects
*ELASTOHYDRODYNAMIC lubrication, *REYNOLDS equations, *HYLIDAE, *ELASTICITY, *MICROSTRUCTURE, *FRICTION
Abstract

Purpose: This paper aims to investigate the squeeze film lubrication properties of hexagonal patterned surface inspired by the epidermis structure of tree frog's toe pad and numerically explore the working mechanism of hexagonal micropillar during the acquisition process of high adhesive and friction for wet contacts. Design/methodology/approach: A two-dimensional elastohydrodynamic numerical model is employed for the squeezing contacts. The pressure distribution, load carrying capacity and liquid flow rate of the squeeze film are obtained through a simultaneous solution of the two-dimensional Reynolds equation and elasticity deformation equations. Findings: Higher pressure is found to be longitudinally distributed across individual hexagonal pillar, with pressure peak emerging at the center of hexagonal pillar. Expanding the area density and shrinking the channel depth or initial film thickness will improve the magnitude of squeezing pressure. Relatively lower pressure is generated inside interconnected channels, which reduces the load carrying capacity of the squeeze film. Meanwhile, the introduction of microchannel is revealed to downscale the total mass flow rate of squeezing contacts. Originality/value: This paper provides a good proof for the working mechanism of surface microstructures during the acquisition process of high adhesive and friction for wet contacts. [ABSTRACT FROM AUTHOR]

Published
2022
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140. Outage Constrained Robust Beamforming Design for SWIPT-Enabled Cooperative NOMA System

Author

Su, Binbin, Ni, Qiang, Yu, Wenjuan, Pervaiz, Haris, Su, Binbin, Ni, Qiang, Yu, Wenjuan, and Pervaiz, Haris

Abstract

We investigate the robust beamforming design for a simultaneous wireless information and power transfer (SWIPT) enabled system, with the cooperative non-orthogonal multiple access (NOMA) protocol applied. A novel cooperative NOMA scheme is proposed, where a strong user with better channel conditions adopts power splitting (PS) scheme and acts as an energy-harvesting relay to forward the decoded signal to the weak user. The presence of channel uncertainties is considered by introducing the outage-based constraints of signal to interference plus noise ratio (SINR). Specifically, it is assumed that only imperfect channel state information (CSI) is known at the base station (BS), due to the reason that the BS is far away from both users and suffers serious feedback delay. Our aim is to maximize the strong user's data rate, by optimally designing the robust transmit beamforming and PS ratio, while guaranteeing the correct decoding of the weak user. The proposed formulation yields to a challenging nonconvex optimization problem. To solve it, we first approximate the probabilistic constraints with the Bernstein-type inequalities, which can then be globally solved by two-dimensional exhaustive search. To further reduce the complexity, an efficient low-complexity algorithm is proposed with the aid of successive convex approximation (SCA). Numerical results show that the proposed algorithm converges quickly, and the proposed SWIPT-enabled robust cooperative NOMA system achieves better performance than existing protocols.

Published
2019

141. Robust Transmit Beamforming for SWIPT-Enabled Cooperative NOMA with Channel Uncertainties

Author

Su, Binbin, Ni, Qiang, Yu, Wenjuan, Su, Binbin, Ni, Qiang, and Yu, Wenjuan

Abstract

In this paper, we study the robust beamforming design for a simultaneous wireless information and power transfer (SWIPT) enabled system, with cooperative nonorthogonal multiple access (NOMA) protocol applied. A novel cooperative NOMA scheme is proposed, where the strong user with better channel conditions adopts power splitting (PS) scheme and acts as an energy-harvesting relay to transmit information to the weak user. The presence of channel uncertainties is considered and incorporated in our formulations to improve the design robustness and communication reliability. Specifically, only imperfect channel state information (CSI) is assumed to be available at the base station (BS), due to the reason that the BS is far away from both users and suffers serious feedback delay. To comprehensively address the channel uncertainties, two major design criteria are adopted, which are the outage-based constraint design and the worst-case based optimization. Then, our aim is to maximize the strong user’s data rate, by optimally designing the robust transmit beamforming and PS ratio, while guaranteeing the correct decoding of the weak user. With two different channel uncertainty models respectively incorporated, the proposed formulations yield to challenging nonconvex optimization problems. For the outage-based constrained optimization, we first conservatively approximate the probabilistic constraints with the Bernstein-type inequalities, which are then globally solved by two-dimensional exhaustive search. To further reduce the complexity, an efficient low-complexity algorithm is then proposed with the aid of successive convex approximation (SCA). For the worst-case based scenario, we firstly apply semidefinite relaxation (SDR) method to relax the quadratic terms and prove the rank-one optimality. Then the nonconvex max-min optimization problem is readily transformed into convex approximations based on S-procedure and SCA. Simulation results show that for both channel uncertaint

Published
2019

142. Sb3+‐Doping in Cesium Zinc Halides Single Crystals Enabling High‐Efficiency Near‐Infrared Emission.

Author

Su, Binbin, Li, Mingze, Song, Enhai, and Xia, Zhiguo

Subjects
*ZINC halides, *LIGHT sources, *SINGLE crystals, *METAL halides, *NEAR infrared radiation, *CESIUM, *LUMINESCENCE
Abstract

Luminescent metal halide materials with flexible crystallography/electronic structures and tunable emission have demonstrated broad application prospects in the visible light region. However, designing near‐infrared (NIR) light‐emitting metal halides remains a challenge. Here, an enlightening prototype is proposed to explore the high‐efficiency broadband NIR emission in metal halide systems by incorporating Sb3+ into the Cs2ZnCl4 matrix. Combined experimental analysis and density functional theory calculations reveal a modified self‐trapped excitons model to elaborate the NIR emission. The high photoluminescence quantum yield of 69.9% peaking at 745 nm and large full width at half maximum of 175 nm, along with excellent air/thermal stability, show the unique advantages of lead‐free metal halide Cs2ZnCl4:Sb3+ as the NIR light source. The substitution of Cl− by Br− further enables the red‐shift of emission peak from 745 to 823 nm. The NIR light‐emitting diode device based on Cs2ZnCl4:Sb3+ demonstrates potential as a non‐visible light source in night vision. This study puts forward an effective strategy to design the novel eco‐friendly and high‐efficiency NIR emissive materials and provides guidance for expanding the application scope of luminescent metal halides. [ABSTRACT FROM AUTHOR]

Published
2021
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143. The trends of mental disorders mortality in China over a 33-year period.

Author

Luo, Yanan, Wu, Yu, Su, Binbin, Chen, Chen, and Zheng, Xiaoying

Abstract

We investigated the variations in the trends of mental disorders mortality by age, period, and cohort, over a 33-year period from 1987 to 2020, to reveal the relationship between age, period, cohort, and mental disorders mortality, as well as providing guidance for resource allocation to prevent mental disorders-related deaths in vulnerable target populations. The data of mental disorders mortality 1987–2020 were from five administrative organizations, which collected from the National Health Commission in China with national monitoring by sex and age, covering 31 provinces in China. The International Classification of Diseases (ICD), its 9th Revision (ICD-9) and its 10th Revision were used to code the mental diseases. From 1987–2002, ICD-9 was used, and ICD-10 was used from 2003 to 2020. The age standardized mortality rates (ASMRs) were calculated using the World Standard Population as the reference. We used joinpoint models to assess the trends of mental disorders mortality in China for the period 1987–2020. And the age-period-cohort models were employed to estimate the age-period-cohort effect on mental disorders mortality. The age-standardized overall mental disorders mortality rate (ASMR) showed a downward trend from 1987 to 2020. Further, the ASMR of individuals in urban was higher than that in rural from 1987 to 2001, but, post-2002, this urban-rural disparity in ASMR showed a less clear pattern, with urban areas occasionally surpassing rural areas and vice versa. ASMR is less prevalent among females compared to males overall. The contribution of age effects to mental disorders mortality gradually increases with advancing age, the period effects of mental disorders mortality gradually decrease over time. The cohort effect's contribution to mental disorders mortality decreases in the newly born population, while in the older birth cohorts, the cohort effect's contribution to the mortality rate of mental disorders increases. The ASMR exhibits a decreasing trend from 1987 to 2020, and these change trend showed urban-rural and sex differences. The primary factors contributing to this overall decline are period effects and cohort effects. Our results provide valuable information for shaping mental health policies, designing targeted interventions, and preparing for future changes in disease mortality rates. The focus on different demographic factors allows for a nuanced and tailored approach to mental health promotion and intervention strategies. • In this nationwide study, we assessed mental disorders mortality trends in China by urban and rural residents over a 33-year period from 1987-2020. • The age-standardized overall mental mortality rate (ASMR) and sex differences has declined during the past three decades, which may due to the period effects and cohort effects of ASMR. • More attention should be paid to mental disorders prevention in urban areas to prevent the excess mortality or premature for mental disorders. [ABSTRACT FROM AUTHOR]

Published
2024
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