Aiming at the problems of model security and user data disclosure caused by the deep learning model in the Internet of Vehicles scenario, which can be stolen by malicious roadside units or base stations and other attackers through knowledge distillation and other techniques, this paper proposes a scheme to strengthen prevent against distillation. The scheme exploits the idea of model reinforcement such as model self-learning and attention mechanism to maximize the difference between the pre-trained model and the normal model without sacrificing performance. It also combines local differential privacy technology to reduce the effectiveness of model inversion attacks. Our experimental results on several datasets show that this method is effective for both standard and data-free knowledge distillation, and provides better model protection than passive defense.

Privacy Protection

10.4108/eetel.v8i3.3318

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Venous Air Embolism during Surgery, Especially Cesarean Delivery

Chang Seok Kim Jia Liu Ja‐Young Kwon Seokyung Shin Ki Jun Kim

Venous air embolism (VAE) is the entrapment of air or medical gases into the venous system causing symptoms and signs of pulmonary vessel obstruction. The incidence of VAE during cesarean delivery ranges from 10 to 97% depending on surgical position or diagnostic tools, with a potential for life-threatening events. We reviewed extensive literatures regarding VAE in detail and herein described VAE during surgery including cesarean delivery from background and history to treatment and prevention. It is intended that present work will improve the understanding of VAE during surgery.

Cesarean delivery

Air embolism

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Essays on Return and Volatility on World Stock Markets

Jia Liu

Stock (firearms)

Source

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Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Sustainability (2024)

Sicheng Wan Yibo Wang Youshuang Zhang Beibei Zhu Huakun Huang

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

10.3390/su16166903

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The mechanism investigation of mutation genes in liver and lung metastasis of colorectal cancer by using NGS technique

Critical Reviews in Oncology/Hematology (2023)

Kai Liu Yunlong Cui Hua Li Jiahui Mi Hailong Wang

We analyzed the somatic mutation distributions as well as pathways associated with liver/lung metastasis of CRC using next-generation sequencing panel.We detected the somatic SNV/indel mutations of 1126 tumor-related genes in CRC, liver/lung metastasis of CRC and liver /lung cancer. We combined the MSK and GEO datasets to identified the genes and pathways related to the metastasis of CRC.We identified 174 genes related to liver metastasis of CRC, 78 genes related to lung metastasis of CRC, and 57 genes related to both liver and lung metastasis in two datasets. The genes related to liver and lung metastasis were collectively enriched in various pathways. Finally we found that IRS1, BRCA2, EphA5, PTPRD, BRAF, and PTEN could be prognosis-related genes in CRC metastasis.Our finding may help clarify the pathogenesis of CRC metastasis more clearly and provide new perspectives for the diagnosis and treatment of CRC metastasis.

10.1016/j.critrevonc.2023.104057

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Acupuncture therapy for the treatment of stable angina pectoris: An updated meta-analysis of randomized controlled trials

Complementary Therapies in Clinical Practice (2018)

Yuan Liu Haoyu Meng Mohammad Reeaze Khurwolah Jia Liu Heng Tang

Stable angina

Acupuncture therapy

10.1016/j.ctcp.2018.12.012

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Citations (36)

Incidence and risk factors associated with atrioventricular block in the general population: the atherosclerosis risk in communities study and Cardiovascular Health Study

BMC Cardiovascular Disorders (2024)

Jingwei Zhang Jia Liu Min Ye Ming Zhang Fengjuan Yao

To identify risk factors correlated with atrioventricular block (AVB) in the general population.

Angiology

Population Health

Atrioventricular block

Cardiovascular Health

10.1186/s12872-024-04163-9

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On 2-metric resolvability in rotationally-symmetric graphs

Journal of Intelligent & Fuzzy Systems (2021)

Humera Bashir Zohaib Zahid Agha Kashif Sohail Zafar Jia Liu

The 2-metric resolvability is an extension of metric resolvability in graphs having several applications in intelligent systems for example network optimization, robot navigation and sensor networking. Rotationally symmetric graphs are important in intelligent networks due to uniform rate of data transformation to all nodes. In this article, 2-metric dimension of rotationally symmetric plane graphs Rn, Sn and Tn is computed and found to be independent of the number of vertices.

Metric Dimension

10.3233/jifs-210040

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Citations (4)