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1. Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

Author

He, Jiayi, Luo, Xiaofeng, Kang, Jiawen, Du, Hongyang, Xiong, Zehui, Chen, Ci, Niyato, Dusit, and Shen, Xuemin

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security
Abstract

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)-based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy., Comment: 8 pages, 3 figures

Published
2024

2. Pretraining-finetuning Framework for Efficient Co-design: A Case Study on Quadruped Robot Parkour

Author

Chen, Ci, Yu, Jiyu, Lu, Haojian, Gao, Hongbo, Xiong, Rong, and Wang, Yue

Subjects
Computer Science - Robotics
Abstract

In nature, animals with exceptional locomotion abilities, such as cougars, often possess asymmetric fore and hind legs. This observation inspired us: could optimizing the leg length of quadruped robots endow them with similar locomotive capabilities? In this paper, we propose an approach that co-optimizes the mechanical structure and control policy to boost the locomotive prowess of quadruped robots. Specifically, we introduce a novel pretraining-finetuning framework, which not only guarantees optimal control strategies for each mechanical candidate but also ensures time efficiency. Additionally, we have devised an innovative training method for our pretraining network, integrating spatial domain randomization with regularization methods, markedly improving the network's generalizability. Our experimental results indicate that the proposed pretraining-finetuning framework significantly enhances the overall co-design performance with less time consumption. Moreover, the co-design strategy substantially exceeds the conventional method of independently optimizing control strategies, further improving the robot's locomotive performance and providing an innovative approach to enhancing the extreme parkour capabilities of quadruped robots.

Published
2024

4. Failure-aware Policy Learning for Self-assessable Robotics Tasks

Author

Xu, Kechun, Chen, Runjian, Zhao, Shuqi, Li, Zizhang, Yu, Hongxiang, Chen, Ci, Wang, Yue, and Xiong, Rong

Subjects
Computer Science - Robotics
Abstract

Self-assessment rules play an essential role in safe and effective real-world robotic applications, which verify the feasibility of the selected action before actual execution. But how to utilize the self-assessment results to re-choose actions remains a challenge. Previous methods eliminate the selected action evaluated as failed by the self-assessment rules, and re-choose one with the next-highest affordance~(i.e. process-of-elimination strategy [1]), which ignores the dependency between the self-assessment results and the remaining untried actions. However, this dependency is important since the previous failures might help trim the remaining over-estimated actions. In this paper, we set to investigate this dependency by learning a failure-aware policy. We propose two architectures for the failure-aware policy by representing the self-assessment results of previous failures as the variable state, and leveraging recurrent neural networks to implicitly memorize the previous failures. Experiments conducted on three tasks demonstrate that our method can achieve better performances with higher task success rates by less trials. Moreover, when the actions are correlated, learning a failure-aware policy can achieve better performance than the process-of-elimination strategy.

Published
2023

5. Real-World Treatment Patterns and Outcomes of Cemiplimab in Patients with Advanced Cutaneous Squamous Cell Carcinoma Treated in US Oncology Practices

Author

Ge W, Wu N, Chen CI, Inocencio TJ, LaFontaine PR, Seebach F, Fury M, Harnett J, and Ruiz ES

Subjects
cutaneous squamous cell carcinoma, cemiplimab, immune checkpoint inhibitor, real-world study, skin cancer, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Wenzhen Ge,1 Ning Wu,1 Chieh-I Chen,1 Timothy J Inocencio,1 Patrick R LaFontaine,2 Frank Seebach,3 Matthew Fury,4 James Harnett,1 Emily S Ruiz5 1Department of Health Economics and Outcomes Research, Regeneron Pharmaceuticals, Inc, Tarrytown, NY, USA; 2Department of Global Health Economics & Outcomes Research, Sanofi, Cambridge, MA, USA; 3Department of Regulatory Affairs, Regeneron Pharmaceuticals, Inc, Tarrytown, NY, USA; 4Department of Clinical Sciences, Oncology, Regeneron Pharmaceuticals, Inc, Tarrytown, NY, USA; 5Department of Dermatology, Dana-Farber Cancer Institute, Boston, MA, USACorrespondence: Wenzhen Ge, Regeneron Pharmaceuticals, Inc, 1 Rockwood Road, Sleepy Hollow, NY, 10591, Tel +1 914-847-7000, Email wenzhen.ge@regeneron.comBackground: Prior to the Food and Drug Administration approval of cemiplimab in 2018, the median overall survival (OS) for adult patients with advanced CSCC receiving systemic therapy was approximately 8 to 15 months. Limited real-world data are available on cemiplimab for this indication in the US.Patients and Methods: This retrospective cohort study included US patients with advanced CSCC initiating cemiplimab monotherapy in a real-world database (2018– 2021). A clinical trial–like sub-cohort was identified using select criteria. Time to treatment discontinuation (TTD), time to next treatment (TTNT), and OS were estimated using Kaplan–Meier methods. Cox proportional hazard models were used to examine prognostic factors associated with OS in the main cohort.Results: The main cohort included 622 patients (n = 240 in the trial-like cohort). In the main cohort, the median age was 78 years, 77.8% were male, 21.4% were immunocompromised/immunosuppressed, and 63.8% had metastatic CSCC. Median (95% CI) TTD and TTNT were 8.0 (6.6– 9.0) months and 16.4 (13.3– 21.0) months, respectively, in the main cohort. Median (95% CI) OS was 24.8 (21.8– 29.1) months in the main cohort (not reached in the trial-like cohort). In multivariable analyses, age < 60 years (hazard ratio [HR], 0.37), Eastern Cooperative Oncology Group performance status < 3– 4 (HR range, 0.13– 0.57), and primary CSCC location in the head and neck only versus extremities only (HR, 0.59) were associated with better OS. Similar OS was observed between patients who had immunosuppressing/immunocompromising conditions and those without.Conclusion: These findings confirm the effectiveness of cemiplimab among a heterogenous, real-world advanced CSCC patient population and substantiate the efficacy of cemiplimab observed in clinical trials.Keywords: cutaneous squamous cell carcinoma, cemiplimab, immune checkpoint inhibitor, real-world study, skin cancer

Published
2024

7. C^2:Co-design of Robots via Concurrent Networks Coupling Online and Offline Reinforcement Learning

Author

Chen, Ci, Xiang, Pingyu, Lu, Haojian, Wang, Yue, and Xiong, Rong

Subjects
Computer Science - Robotics
Abstract

With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, the dual-network framework utilizes data collected by individual networks under a specific morphology to train a population network that provides a surrogate function for morphology optimization. This approach replaces the traditional evaluation of a diverse set of candidates, thereby speeding up the training. Despite considerable results, the online training of both networks impedes their performance. To address this issue, we propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods. By leveraging the behavior cloning term in a flexible manner, we achieve an effective combination of both networks. We conducted multiple sets of comparative experiments in the simulator and found that the proposed method effectively addresses issues present in the dual-network framework, leading to overall algorithmic performance improvement. Furthermore, we validated the algorithm on a real robot, demonstrating its feasibility in a practical application.

Published
2022

11. Learning the continuous-time optimal decision law from discrete-time rewards

Author

Chen Ci, Xie Lihua, Xie Kan, Lewis Frank Leroy, Liu Yilu, and Xie Shengli

Subjects
continuous-time state and action, decision law learning, discrete-time reward, dynamical systems, reinforcement learning, Science, Engineering (General). Civil engineering (General), TA1-2040
Abstract

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement learning. In this work, we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws. We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards. We apply this finding to solve output-feedback design problems in power systems. The results reveal that our approach removes an intermediate stage of identifying dynamical models. Our work suggests that the discrete-time reward is efficient in search of the desired decision law, which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

Published
2024
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14. Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems

Author

Chen, Ci, Xie, Lihua, Jiang, Yi, Xie, Kan, and Xie, Shengli

Subjects
Mathematics - Dynamical Systems
Abstract

In this paper, we investigate the optimal output tracking problem for linear discrete-time systems with unknown dynamics using reinforcement learning and robust output regulation theory. This output tracking problem only allows to utilize the outputs of the reference system and the controlled system, rather than their states, and differs from most existing tracking results that depend on the state of the system. The optimal tracking problem is formulated into a linear quadratic regulation problem by proposing a family of dynamic discrete-time controllers. Then, it is shown that solving the output tracking problem is equivalent to solving output regulation equations, whose solution, however, requires the knowledge of the complete and accurate system dynamics. To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output. By introducing re-expression error and analyzing the rank condition of the parameterization matrix, we ensure the uniqueness of the proposed RL based optimal control via output feedback.

Published
2021

16. Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

Author

Chen, Ci, Wan, Yuanfang, Li, Baowei, Wang, Chen, Xie, Guangming, and Jiang, Huanyu

Subjects
Computer Science - Robotics
Abstract

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs' vast view scope. However, due to UAVs flight altitude limitations, it may be impossible to observe the global map, and motion planning in the local map is a POMDP (Partially Observable Markov Decision Process) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned system under partial observation from UAV without reconstruction of global maps, which consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV's perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate.

Published
2020

17. The Scattering of Dirac Spinors in Rotating Spheroids

Author

Gao, Zhi Fu, Chen, Ci Xing, and Wang, Na

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Quantum Physics
Abstract

There are many stars that are rotating spheroids in the Universe, and studying them is of very important significance. Since the times of Newton, many astronomers and physicists have researched gravitational properties of stars by considering the moment equations derived from Eulerian hydrodynamic equations. In this paper we study the scattering of spinors of the Dirac equation, and in particular investigate the scattering issue in the limit case of rotating Maclaurin spheroids. Firstly we give the metric of a rotating ellipsoid star, then write the Dirac equation under this metric, and finally derive the scattering solution to the Dirac equation and establish a relation between differential scattering cross-section, $\sigma$, and stellar matter density, $\mu$. It is found that the sensitivity of $\sigma$ to the change in $\mu$ is proportional to the density $\mu$. Because of weak gravitational field and constant mass density, our results are reasonable. The results can be applied to white dwarfs, main sequence stars, red giants, supergiant stars and so on, as long as their gravitational fields are so weak that they can be treated in the Newtonan approximations, and the fluid is assumed to be incompressible. Notice that we take the star's matter density to be its average density and the star is not taken to be compact. Obviously our results cannot be used to study neutron stars and black holes. In particular, our results are suitable for white dwarfs, which have average densities of about $10^{5}-10^{6}$\,g~cm$^{-3}$, corresponding to a range of mass of about $0.21-0.61 M_{\bigodot}$ and a range of radius of about $6000-10000$\,km., Comment: This version exactly corresponds to the published version with 12.pages

Published
2020
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24. Online Policy Iteration Algorithms for Linear Continuous-Time H-Infinity Regulation With Completely Unknown Dynamics

Author

Chen, Yuzhe, Chen, Ci, Xie, Kan, and Lewis, Frank L.

Abstract

This paper proposes two online policy-iteration (PI) algorithms for solving linear continuous-time $H_{\infty} $ regulation problems with unknown dynamics. Our results are completely learning-orientated in the sense that prior model knowledge of initial stabilizing control policies arising from solving the Game Algebraic Riccati Equation (GARE) associated with the $H_{\infty} $ regulation problem is now removed, which thereby resolves a long-standing challenge in the existing PI works to achieve model-free learning. To this end, two offline PI algorithms, consisting of the single-looped and the double-looped, are first proposed by nesting a homotopy-based initialization to solve a series of Lyapunov equations associated with the GARE. Then, two online PI algorithms are further proposed by utilizing the system data to avoid the model requirement for online solving the GARE. The single-looped PI algorithm has the feature of simultaneously learning control and disturbance policies, while a double-looped PI updates control policies before carrying out a series of learning disturbance policies. These two online PI algorithms work in a model-free manner and do not require prior knowledge of the system matrices over the whole learning period including the control policy initialization and can lead to the desired control policy with satisfactory system performance. We demonstrate the effectiveness of the proposed learning algorithms with an example of a power system. Note to Practitioners—Solving the $H_{\infty} $ regulation problem for linear continuous-time systems can be achieved by finding the Nash equilibrium of the two-player zero-sum game. However, it is a challenge for control practitioners to design the $H_{\infty} $ regulation controller with completely unknown dynamics due to the fact that it is nontrivial to obtain precise prior knowledge of models/dynamics for many engineering systems. The current methods usually utilized system data to solve the Nash equilibrium solution by offline or online iterative computation, but most of them still needed prior knowledge of the system dynamics for policy seeking such as stabilizing/admissible control or disturbance policies in the initialization. To address such a challenge, this paper develops two homotopy-based online PI algorithms that solve the $H_{\infty} $ regulation problem in a fully model-free manner. It is shown that the developed algorithms can find the Nash equilibrium solution by online measuring the system data, and overcomes the difficulty of finding an initial stabilizing control policy with unknown system dynamics. The validity of the algorithms is illustrated through a simulation study.

Published
2024
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25. Deep Reinforcement Learning Based Co- Optimization of Morphology and Gait for Small-Scale Legged Robot

Author

Chen, Ci, Xiang, Pingyu, Zhang, Jingyu, Xiong, Rong, Wang, Yue, and Lu, Haojian

Abstract

Small-scale legged robots have found widespread utilization in various industrial and biomedical applications due to their compact size and superior locomotion capabilities. Reducing the number of actuators is often desirable to decrease the robot's size and weight, which comes at the expense of the robot's workspace. Our study proposes a method to enhance the mobility of small-scale legged robots with limited degrees of actuators (DoAs) by co-optimizing both morphology parameters and control policy. The co-optimization is formulated as a bi-level optimization problem, where the control policy is designed using deep reinforcement learning algorithms and central pattern generators (CPGs) at the lower level. The inclusion of CPGs significantly speeds up training and enables the application of simulation results in real-world scenarios. At the upper level, morphology optimization is achieved through Bayesian optimization based on dual-networks. This approach eliminates the need to train a policy for each morphology candidate from scratch, leveraging previous experience to enhance efficiency. Through simulation and physical experiments, the effectiveness of our proposed approach is demonstrated, showcasing its ability to discover optimal morphology and gait for small-scale legged robots with limited DoAs. These findings have potential long-term impacts on small-scale legged robot design and locomotion control.

Published
2024
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26. Adaptive Output Synchronization With Designated Convergence Rate of Multiagent Systems Based on Off-Policy Reinforcement Learning

Author

Huang, Chengjie, Chen, Ci, Xie, Kan, Li, Zhenni, and Xie, Shengli

Abstract

In this article, an optimal output synchronization solution to the $H_{\infty }$ optimization of linear discrete-time (DT) multiagent systems is investigated. Compared with current approaches, the issue of designated convergence rate is handled with system optimality, while less computation cost is required. Specifically, the internal model principle is employed to derive a cooperative regulation problem of DT systems, wherein no explicit solution to output regulation equations is needed for learning. Then, we introduce a convergence rate parameter to construct a group of auxiliary cooperative systems, based on which the zero-sum game in $H_{\infty }$ optimization is formulated. The data-efficient off-policy reinforcement learning and output-feedback technique are applied to solve the enhanced Bellman equations with a designated convergence rate. This results in an online optimal synchronization solution learning from only the input-output data along the system trajectories. It is shown that the proposed optimal synchronization protocol achieves asymptotic synchronization for the original systems with the consensus error converging to zero at a designated rate. The effectiveness of the proposed approach is verified by the simulation results.

Published
2024
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28. Event-Triggered Fractional PID-Based Load Frequency Control in Islanded Microgrids Under Cloud-Edge Collaborative Framework.

Author

Zheng, Min, Chen, Ci, Zhang, Yajian, Ruan, Mengfan, and Li, Peike

Subjects
*MICROGRIDS, *FREQUENCY stability, *DEGREES of freedom
Abstract

To address the issue of frequency stability in microgrids with limited communication resources and bandwidth, this paper proposed an event-triggered control method based on a cloud-edge collaborative architecture. Firstly, to address the limited communication resources, event-triggered detector is set up at the edge to upload state variables and update control instructions only when the frequency deviation exceeds a pre-set threshold. Secondly, a fractional-order PI–PD controller is designed at the cloud control center, which achieves higher control degrees of freedom and stronger robustness than traditional integer-order PID control schemes. Additionally, to obtain better control parameters, an optimized design method based on an improved Big Bang-Big Crunch method (BB-BC3) is proposed for the fractional-order PI–PD controller. At last, a simulation example is given to prove the validity of the proposed method. Simulation results show that compared to the integer-order PI–PD controller, the fractional-order PI–PD controller based on the BB-BC3 algorithm proposed in this paper reduces the peak value of the response curve by 5%, and shortens the setting time by 0.14 s. [ABSTRACT FROM AUTHOR]

Published
2024
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31. The experience of Chinese trainees in Japan : the dynamics of exclusion and inclusion

Author

Chen, Ci

Subjects
331.6
Abstract

In response to the labour shortage caused by low fertility and an ageing population, some Japanese labour-intensive companies recruit foreign labourers through the Technical Intern Training Programme. Chinese temporary migrant workers employed through this programme are called 'Chinese trainees'. According to the Japan International Training Cooperation Organisation (JITCO), 534,742 Chinese trainees were recruited through JITCO between 1992 and 2010, representing approximately 74 percent of all foreign trainees. While Chinese trainees contribute positively to the nation's economy, conflicts and dissatisfaction are also created and voiced by them, which calls for a re-examination of the current policy. A deeper understanding of and strategies for integration of the trainees into Japanese society are important issues associated with the programme and this research. There is a dearth of knowledge in understanding the experiences of international temporary migrant workers in the host countries from their viewpoint. Therefore, the concepts of social exclusion and inclusion are utilised to construct a multi-dimensional theoretical framework to understand and analyse the experiences and perspectives of Chinese trainees during their time in Japan. The effectiveness and suitability of the training policy as a means to recruit low-skilled foreign workers is examined. Forty-five in-depth, semi-structured interviews were carried out with former Chinese trainees who worked for three years in Japan; key stakeholders such as government and NGO representatives; academics; and Japanese employers and administers in trade associations. Thematic analysis was conducted to identify and organise key themes on the experiences of Chinese trainees, and to seek the meaning of their life stories. The findings highlight the importance of trainees' individual initiatives, support and care from the receiving communities, and the knowledge achieved within a particular social context. A body of evidence also reveals that the current policy practices have negative impacts on trainees' experiences, and need to be improved.

Published
2015

32. The Clinical Implication of Vitamin D Nanomedicine for Peritoneal Dialysis-Related Peritoneal Damage

Author

Lee YC, Huang CT, Cheng FY, Hung SY, Lin TM, Tsai YC, Chen CI, Wang HK, Lin CW, Liou HH, Chang MY, Wang HH, and Chiou YY

Subjects
peritoneal dialysis, nanoliposome, vitamin d, fibrosis, Medicine (General), R5-920
Abstract

Yi-Che Lee,1–3 Chih-Ting Huang,4 Fong-Yu Cheng,5 Shih-Yuan Hung,3,6 Tsun-Mei Lin,7 Yen-Chang Tsai,4 Chih-I Chen,8 Hao-Kuang Wang,9 Chi-Wei Lin,10 Hung-Hsiang Liou,11 Min-Yu Chang,3 Hsi-Hao Wang,3 Yuan-Yow Chiou4,12 1Division of Nephrology, Department of Internal Medicine, E-DA Dachang Hospital/ I-Shou University, Kaohsiung, Taiwan; 2School of Medicine, College of Medicine; 3Division of Nephrology, Department of Internal Medicine, E-DA Hospital/ I-Shou University, Kaohsiung, Taiwan; 4Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 5Department of Chemistry, Chinese Culture University, Taipei, Taiwan; 6School of Medicine for International Students; 7Department of Laboratory Medicine; 8Division of Colorectal Surgery, Department of Surgery; 9Department of Neurosurgery; 10Department of Medical Education, E-DA Hospital/ I-Shou University, Kaohsiung, Taiwan; 11Division of Nephrology, Department of Medicine, Hsin-Jen Hospital, New Taipei City, Taiwan; 12Department of Pediatrics, National Cheng Kung University Hospital, Tainan, TaiwanCorrespondence: Yi-Che LeeDivision of Nephrology, Department of Internal Medicine, E-DA Hospital/I-Shou University, No.1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan, ROCTel +886-7-6150011 ext. 5121Email pipijer@gmail.com Yuan-Yow ChiouInstitute of Clinical Medicine, Department of Pediatrics, National Cheng Kung University Hospital, Medical College, No. 138, Sheng-Li Road, Tainan City 701, Taiwan, ROCTel +886-6-2353535 ext. 4184Fax +886-6-2753083Email yuanyow@mail.ncku.edu.twPurpose: Vitamin D is a novel potential therapeutic agent for peritoneal dialysis (PD)-related peritoneal fibrosis, but it can induce hypercalcemia and vascular calcification, which limits its applicability. In this study, we create nanotechnology-based drug delivery systems to investigate its therapeutics and side effects.Materials and methods: 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N- [amino-(polyethylene glycol)2000] (DSPE-PEG) and L-α-phosphatidylcholine (PC), which packages with 1α,25(OH)2D3, were used to construct vitamin D nanoliposomes. To confirm the function and safety of vitamin D nanoliposomes, peritoneal mesothelial cells were treated with TGF-β1 and the reverse was attempted using vitamin D nanoliposomes. Antibodies (Ab) against the peritoneum-glycoprotein M6A (GPM6A) Ab were conjugated with vitamin D nanoliposomes. These particles were implanted into mice by intraperitoneal injection and the animals were monitored for the distribution and side effects induced by vitamin D.Results: Vitamin D nanoliposomes were taken up by the mesothelial cells over time without cell toxicity and it also provided the same therapeutic effect in vitro. In vivo study, fluorescent imaging showed vitamin D nanoliposomes allow specific peritoneum target effect and also ameliorate vitamin D side effect.Conclusion: Nanoliposomes vitamin D delivery systems for the prevention of PD-related peritoneal damage may be a potential clinical strategy in the future.Keywords: peritoneal dialysis, nanoliposome, vitamin D, fibrosis

Published
2019

33. Dirac spinor scattering states with positive‐energy in rotating spheroid models.

Author

Gao, Zhi‐Fu, Chen, Ci‐Xing, Wang, Na, Zhao, Xin‐Jun, and Wang, Zhao‐Jun

Subjects
*DIRAC equation, *DIFFERENTIAL cross sections, *QUANTUM field theory, *SCATTERING amplitude (Physics), *STARS, *PHYSICISTS
Abstract

There are many rotating spheroids in the universe, and many astronomers and physicists have used theoretical methods to study the characteristics of stellar gravity since Newton's time. This paper derives the solutions of eight scattering states (ϕ(0),χ(0),ϕ(1),χ(1),ϕ(2)$$ \Big({\phi}^{(0)},{\chi}^{(0)},{\phi}^{(1)},{\chi}^{(1)},{\phi}^{(2)} $$,χ(2),ϕ(3)$$ {\chi}^{(2)},{\phi}^{(3)} $$, andχ(3))$$ {\chi}^{(3)}\Big) $$ for the Dirac equation with positive‐energy E=im$$ E= im $$, and establishes the relationship between the differential scattering cross section σi(p,θ,φ)$$ {\sigma}_i\left(p,\theta, \varphi \right) $$ and the stellar density μ$$ \mu $$. It is found that: (1) For the eight scattering states, their average scattering cross‐sections σi‾$$ \overline{\sigma_i} $$ are proportional to μ2$$ {\mu}^2 $$, and depend on the star's radius, and the higher the stellar density μ$$ \mu $$, the greater the sensitivity of σi‾$$ \overline{\sigma i} $$ to the change of μ$$ \mu $$; (2) For the four scattering states χ(i),i=0,1,2,3$$ {\chi}^{(i)},i=0,1,2,3 $$, their average scattering amplitudes f‾(p,θ)$$ \overline{f}\left(p,\theta \right) $$ and σ‾(p,θ)$$ \overline{\sigma}\left(p,\theta \right) $$ depend on the mass m$$ m $$ of the particles; while for the other four scattering states ϕ(i)$$ {\phi}^{(i)} $$, i=0,1,2,3$$ i=0,1,2,3 $$, then f‾$$ \overline{f} $$ and σ‾$$ \overline{\sigma} $$ are independent of m$$ m $$. This study links the gravitational characteristics of stars with the scattering cross section, creating a new method for studying the gravitational characteristics, which helps to reveal the mystery of the gravity of rotating ellipsoidal stars. [ABSTRACT FROM AUTHOR]

Published
2024
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