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Bo Yin

Publications and source records attributed to Bo Yin.

28 records · Page 2Linked to original sources
arXiv2024

Cohort profile: the Northwest China Real-world and Population-based Cohort

The Northwest China Real-World and Population-based cohort is an ongoing prospective cohort with more than 25 million population, covering almost all residents across approximately 1.66 million square kilometers in northwest China; The cohort integrates data from various sources, including health profiles, examination records, electronic health records, mortality records, statistical yearbooks, and environmental datasets, covering comprehensive health-related factors such as demographics, lifestyle factors, family medical history, living conditions, enrollment in national public health services, physical examinations, blood assay tests, diagnostic assessments, disease outcomes, and cause-specific mortality. This real-world dataset can evaluate clinical treatment effectiveness and prognosis, assess impact of health policy, and investigate the health effects of multiple risk factors . From January 2019 to December 2023, the cohort has included 13,634,481 participants, accumulating 47,050,707 person-years of follow-up, with 13,598,407 medical diagnosis records and 881,114 recorded deaths. Cohort data are available upon request. De-identified and anonymized data are stored on local servers and accessed through a data-sharing platform, enabling users to utilize the data without direct access to the raw information. A description of the proposed research can be sent to Yining Yang & Qian Di.

cs.CY↗
arXiv2024

Kinetic development of low-temperature propane oxidation in a repetitively-pulsed nanosecond discharge

The kinetics of plasma assisted low temperature oxidation of C3H8O2Ar mixtures have been studied in a wide specific deposition energy with the help of nanosecond repetitively pulsed discharge. Two types of nanosecond pulsed plasma sources, the nanosecond capillary discharge (nCD) and dielectric barrier discharge (DBD) combined with the synchrotron photoionization mass spectrometry are investigated. The electron impact reaction rate of propane dissociation and some combustion chemical reactions rate constants are updated according to the nCD and DBD experiment results,and uncertainty of the reactions are analyzed in detail. Compared to the existing model, the updated model's prediction accuracy has great improvement in species H2O, CO, CO2, CH4, CH2O, CH3OH, C2H2, C2H4, C2H6, C2H5OH, C2H5OOH, C3H4-A, C3H4-P, C2H5CHO, i-C3H7OH and C3H7OOH. The propane oxidation processes assisted by DBD and nCD were compared under different single pulse deposition energy (SPDE) conditions while maintaining the same total deposition energy. The reduced electric field in nCD is concentrated at 150-200 Td and 450-500 Td, whereas in DBD it ranges from 0-25 Td and 50-250 Td. Notably, SPDE shows minimal influence on the propane oxidation process, which is primarily controlled by total deposition energy and demonstrates little dependence on the discharge type (DBD or nCD).

physics.plasm-ph↗
arXiv2022

Deep Boosting Robustness of DNN-based Image Watermarking via DBMark

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DBMARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low (LL) sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression.

cs.CV↗
arXiv2021

Topology Aware Deep Learning for Wireless Network Optimization

Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology information that directly impacts the network optimization results. Directly operating on simple representations, e.g., adjacency matrices, results in poor generalization performance as the learned results depend on specific ordering of the network elements in the training data. To address this issue, we propose a two-stage topology-aware machine learning framework (TALF), which trains a graph embedding unit and a deep feed-forward network (DFN) jointly. By propagating and summarizing the underlying graph topological information, TALF encodes the topology in the vector representation of the optimization instance, which is used by the later DFN to infer critical structures of an optimal or near-optimal solution. The proposed approach is evaluated on a canonical wireless network flow problem with diverse network typologies and flow deployments. In-depth study on trade-off between efficiency and effectiveness of the inference results is also conducted, and we show that our approach is better at differentiate links by saving up to 60% computation time at over 90% solution quality.

cs.NI↗
arXiv2020

Bi2Te3/Si thermophotovoltaic cells converting low temperature radiation into electricity

The thermophotovoltaic cells which convert the low temperature radiation into electricity are of significance due to their potential applications in many fields. In this work, Bi2Te3/Si thermophotovoltaic cells which work under the radiation from the blackbody with the temperature of 300 K-480 K are presented. The experimental results show that the cells can output electricity even under the radiation temperature of 300 K. The band structure of Bi2Te3/Si heterojunctions and the defects in Bi2Te3 thin films lower the conversion efficiency of the cells. It is also demonstrated that the resistivity of Si and the thickness of Bi2Te3 thin films have important effects on Bi2Te3/Si thermophotovoltaic cells. Although the cells' output power is small, this work provides a possible way to utilize the low temperature radiation.

physics.app-ph↗
arXiv2019

Bi2Te3/Sb2Te3 Heterojunction and Thermophotovoltaic Cells Absorbing the Radiation from Room-temperature Surroundings

The thermophotovoltaic cells which can convert the infrared radiation from room-temperature surroundings into electricity are of significance due to their potential applications in many fields. In this work, narrow bandgap Bi2Te3/Sb2Te3 thin film thermophotovoltaic cells were fabricated, and the formation mechanism of Bi2Te3/Sb2Te3 p-n heterojunctions was investigated. During the formation of the heterojunctions at room temperature, both electrons and holes diffuse in the same direction from n-type Bi2Te3 thin films to p-type Sb2Te3 thin films rather than conventional bi-directional diffusion. Because the strong intrinsic excitation generates a large number of intrinsic carriers which weaken the built-in electric field of the heterojunctions, their I-V curves become similar to straight lines. It is also demonstrated that Bi2Te3/Sb2Te3 thermophotovoltaic cells can output electrical power under the infrared radiation from a room-temperature heat source. This work proves that it is possible to convert the infrared radiation from dark and room-temperature surroundings into electricity through narrow bandgap thermophotovoltaic cells.

physics.app-ph↗
arXiv2019

Diffie-Hellman in the Air: A Link Layer Approach for In-Band Wireless Pairing

Key establishment is one fundamental issue in wireless security. The widely used Diffie-Hellman key exchange is vulnerable to the man-in-the-middle attack. This paper presents a novel in-band solution for defending the man-in-the-middle attack during the key establishment process for wireless devices. Our solution is based on the insight that an attacker inevitably affects the link layer behavior of the wireless channel, and this behavior change introduced by the attacker can be detected by the legitimate users. Specifically, we propose a key exchange protocol and its corresponding channel access mechanism for the protocol message transmission, in which the Diffie-Hellman parameter is transmitted multiple times in a row without being interrupted by other data transmission on the same wireless channel. The proposed key exchange protocol forces the MITM attacker to cause multiple packet collisions consecutively at the receiver side, which can then be monitored by the proposed detection algorithm. The performance of the proposed solution is validated through both theoretical analysis and simulation: the proposed solution is secure against the MITM attack and can achieve an arbitrarily low false positive ratio. This proposed link layer solution works completely in-band, and can be easily implemented on off-the-shelf wireless devices without the requirement of any special hardware.

cs.CR↗
arXiv2014

Secure Key Establishment for Device-to-Device Communications

With the rapid growth of smartphone and tablet users, Device-to-Device (D2D) communications have become an attractive solution for enhancing the performance of traditional cellular networks. However, relevant security issues involved in D2D communications have not been addressed yet. In this paper, we investigate the security requirements and challenges for D2D communications, and present a secure and efficient key agreement protocol, which enables two mobile devices to establish a shared secret key for D2D communications without prior knowledge. Our approach is based on the Diffie-Hellman key agreement protocol and commitment schemes. Compared to previous work, our proposed protocol introduces less communication and computation overhead. We present the design details and security analysis of the proposed protocol. We also integrate our proposed protocol into the existing Wi-Fi Direct protocol, and implement it using Android smartphones.

cs.CR↗
arXiv2014

SINAP surface preparation processing for 500MHz superconducting cavity

This paper illustrates the design, fabrication and experiment results of surface preparation system for 500MHz superconducting cavity at Shanghai Institute of Applied Physic (SINAP). The SINAP established a set of clean room, buffered chemical polishing equipment, and high pressure ultra-pure water rinsing facility. The whole surface preparation procedure has been operated successfully and verified by the successful vertical tests of 500MHz single cell superconducting cavity.

physics.acc-ph↗