250 research outputs found
On Factors Affecting Industrial Development Growth RatesâA Discussion with Comrade Zhu Jiaming
No full textIn the past few months, the unabating high rate of industrial development has, in an acute fashion, presented to theoretical circles the question of how to view the current high growth rate. In his article published in the second issue of the >i>Forum of Young Economists>/i>, Comrade Zhu Jiaming declares that China "already has the preliminary material preconditions for high-speed growth," and that "since 1978, some indexes of economic growth have shown that the period of high-speed growth has already come." This author, however, holds that the problem cannot be explained by looking only at indexes of a few years, and that in order to determine whether or not China has entered a period of high-speed growth, it is necessary to analyze the factors that affect the rate of industrial development and the trend of their changes. The present article is written to invite comments and corrections by Comrade Zhu Jiaming and others.
Data from: Partitioning net ecosystem exchange (NEE) of CO2 using Solar-Induced chlorophyll Fluorescence (SIF)
No full textPlease cite this dataset as: Oz Kira, Christine Y.Y. Chang, Lianhong Gu, Jiaming Wen, Zhiming Hong, Ying Sun. (2021) Data from: Partitioning net ecosystem exchange (NEE) of CO2 using Solar-Induced chlorophyll Fluorescence (SIF). [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/a41b-mn61Accurate partitioning of net ecosystem exchange (NEE) of CO2 to gross primary production (GPP) and ecosystem respiration (Reco) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long-standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. Solar-Induced chlorophyll Fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF-based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and Reco, especially under high VPD. For field measurements, it results in lower daytime GPP and Reco than conventional approaches. This study made the first attempt to demonstrate SIF’s potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real-world environmental conditions
CCDC 1557570: Experimental Crystal Structure Determination
No full textBIKMEQ02 : catena-[tris(μ-1,3-bis(4-carboxylatophenyl)benzene)-bis(μ-hydroxo)-bis(μ-oxo)-tri-zirconium(iv) N,N-dimethylformamide solvate hemihydrate] Space Group: R 3 (148), Cell: a 30.487(4)Å b 30.487(4)Å c 15.094(3)Å, α 90.00° β 90.00° γ 120.00° Related Article: Xiaodong Sun, Xu Li, Shuo Yao, Rajamani Krishna, Jiaming Gu, Guanghua Li, Yunling Liu|2020|J.Mater.Chem.A|8|17106|doi:10.1039/D0TA04778
The effect of natural and engineered hydraulic conditions on river-floodplain connectivity using hydrodynamic modeling and particle tracking analysis
No full textHydraulic conditions and water resources management projects can significantly alter river-floodplain connectivity, which in turn can alter hydrologic and biogeochemical processes in river corridors. In this study, the hydrodynamics of river-floodplain connectivity under different flood conditions and the effect of the Nanchang Water Resources Project Group (NWRPG) in the middle branch of the Ganjang River were investigated using a combination of two-dimensional hydrodynamic simulations and particle tracking. The hydrodynamic model was calibrated and validated using data from several gauging stations and field measurements. Floods in the Ganjang River can be limited to the river itself (“River Flood”, flood with normal lake level) or further extended to the Poyang Lake (“Lake Flood”, flood with high lake level). The results show that compared with “River Flood” scenarios, “Lake Flood” scenarios increased the water level flooding a larger area. The flow velocity decreased and the residence time (RT) of particles increased. The particle travel distance (PTD) of “River Flood” was larger than that of “Lake Flood”. The larger the flood, the greater the transboundary flux between the river and the floodplain, and the shorter the RT and PTD. The effect of NWRPG was the permanent flooding of part of the river floodplain, causing some habitat loss. Due to the increase in discharge, the implementation of the NWRPG results in a shorter RT with a smaller standard deviation, which has little effect on the PTD distribution. These findings can facilitate river connectivity restoration efforts in the Ganjang River and also provide a reference for assessing the impact of barrage projects
Illi Racecar: A small-scale platform for autonomous driving
No full textThis thesis proposes a safety-critical 1/10 scale autonomous driving platform called Illi Racecar. Sensors, including three cameras, A laser scanner, an inertial measurement unit (IMU), two encoders, and an E-stop button, were installed on the platform for environmental perception. Three levels of computer module were equipped for data processing and control. A servo motor and a DC motor with the Ackermann steering chassis were utilized for motion control. A self-designed PCB board was placed on the vehicle supporting the electronic system. The Illi Racecar was built based on a Real-Time Operating System (RTOS) with a high-low level controller framework. The new generation of Robot Operating System, ROS2, was installed on the main computer station with its real-time features to ensure the platform's reliability. A real-time Drive-by-Wire (DBW) control module with an industry-standard Controller Area Network (CAN) bus was implemented to replace the less reliable ROS serial communication interface. Based on the parameter of the Illi Racecar, two geometric path trackers, namely the Pure pursuit controller and the Stanley controller were simulated using Simulink. After the low-level control programming and sensing system calibration of the platform, real car tests were conducted based on the parameters tuned by the simulation. The program for the real car test was also built in Simulink and generated into C for faster development. After comparing the simulation results and the real car evaluation of different controllers, several factors that influenced the results were determined. The Illi Racecar was the first application of ROS2 on a 1/10 scale Ackermann steering platform and in using Simulink modeling for rapid control system prototyping.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01The student, Jiaming Zhang, accepted the attached license on 2021-07-21 at 14:19.The student, Jiaming Zhang, submitted this Thesis for approval on 2021-07-21 at 14:25.This Thesis was approved for publication on 2021-07-22 at 14:45.DSpace SAF Submission Ingestion Package generated from Vireo submission #17038 on 2022-01-12 at 12:55:33Made available in DSpace on 2022-01-12T22:35:20Z (GMT). No. of bitstreams: 2
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How Advanced Is Nanomedicine for Atherosclerosis?
No full textXiang Gu,1,* Lixin Du,1,* Ruifang Lin,2 Zehui Ding,2 Zhihua Guo,2 Jiaming Wei,2 Ya Li1 1School of Pharmacy, Hunan University of Chinese Medicine, Changsha, 410208, China; 2School of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208, China*These authors contributed equally to this workCorrespondence: Jiaming Wei; Ya Li, Email [email protected]; [email protected]: Advances in nanotechnology have opened new avenues for precision therapy, personalized medicine, and multifunctional theranostics in atherosclerosis (AS). This review provides a comprehensive overview of the role of nanoparticles (NPs) in precision medicine for AS, discussing their applications, challenges, and future prospects. The review first analyzes the current treatment landscape of AS and outlines potential biological targets for therapy. Various nanocarriers, including organic, inorganic, and hybrid systems, are evaluated for their therapeutic potential, with a focus on targeted drug delivery, anti-inflammatory therapy, vascular repair, plaque stabilization, and lipid clearance. Additionally, the review explores NP preparation methods, emphasizing strategies to enhance drug loading, stability, and controlled release. Finally, the translational challenges of NP-based therapies, including biocompatibility, large-scale production, regulatory hurdles, and clinical implementation, are critically analyzed. Future directions highlight the importance of interdisciplinary collaboration and technological innovation in advancing nanoparticle-based precision medicine for AS.Keywords: nanoparticles, nanocarrier, nanomedicine, atherosclerosis, precision medicin
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Robust Optimization of Generalized Eigenvalue Problem for Positive Semi-definite Matrices
No full textIn this paper, we propose novel algorithms for solving the worst-case robust optimization of the generalized eigenvalue problem with positive semi-definite constraint, a highly non-convex problem that has eluded traditional optimization solvers. We consider the rank-one case and the general-rank case saparately. For the rank-one case, we first present a relaxation of the KKT system, transforming the problem into a more tractable nonlinear system of equations. We then prove the tightness of the relaxation at the optimal point and introduce two algorithms for solving the relaxed system of equations. Our approach is the first to guarantee finding the global optimal solution for the problem at hand. For the general-rank case, we first solve the KKT system with one variable fixed. We then describe an algorithm searching for the variable. We then showed that our algorithm converges to a stationary point of the problem. We showcase the potential applications of our algorithms in robust adaptive beamforming and semi-supervised Fisher discriminant analysis. This work contributes significantly to the field by providing a globally optimal solution to a highly non-convex problem with broad applicability in various disciplines
Integrated optimization method for plastic injection molding
No full textThis thesis presents a integrated optimization method to find the optimized operational parameters in Plastic Injection Molding (PIM), such as flow rate, melt temperature, mold temperature, pressure holding time and packing pressure that will minimize the shrinkage under the constraints of injection pressure and cooling time. Design of Experiments (DOE) is used to reduce the computational cost for simulations. Furthermore, the possibility value (P-value) is adopted to identify the significant factors among all design variables with respect to each functions. Monotonicity Analysis is then employed to detect the active constraints and to reduce the complexity of the original optimization problem so that the problem can be easily solved by a simple regression. Finally, the responses obtained by the simulation with the optimized operational parameters are used to validate our solutions. Two design examples are presented in this paper. For both examples, twenty-five initial samples are evaluated using Solidworks Plastic based on the orthogonal array from the DOE with five variables. There are two constraints on injection pressure and cooling time. P-value shows that packing pressure is not a significant factor for shrinkage and two constraints in both examples, then it can be moved out in later optimization. The exact value of flow rate and pressure holding time can be found out by Monotonicity Analysis. Finally, by solving the regression equations with melt temperature and mold temperature, the optimal parameters combination will be solved. Using the optimized parameters in simulation, the shrinkage for first example and second example are 0.3988mm and 0.0768mm, both of the shrinkage results are smaller than that in initial samples which can satisfy the constraints.M.S.Includes bibliographical referencesby Jiaming L
Design and construction of the ReciPlyDome, a lightweight modular reciprocal dome
No full textIn the event sector, where there is a search for architectural constructions with an innovative morphology, reuse is key to strive towards more sustainable events. Designing modular structures and detailing them for easy disassembly and re-assembly is an ideal way to encourage and facilitate reuse. This way a longer lifespan is assured for the used components. However, temporary (event) structures are often hard to assemble, which can compromise their reusability. The difficulties of assembly are usually induced by the morphology of the modules or by using certain types of connections. Therefore these structures require optimization in terms of assembly while remaining resource efficient. The main objective of this research is to reimagine a developed structure, the ReciPlyDome, and optimize it in terms of assembly. The ReciPlyDome is a reciprocal dome structure based on a rhombic triacontahedron, whereby all elements are identical (except for the five elements that touch the ground). During the assembly phase of the first version of the ReciPlyDome, torsion in the components appeared to hinder efficient construction. To eliminate this, the dome was reviewed, which led to the development of a new connection system and an improved shape for the beams. A new full-scale version of the dome has been built, showing the positive effect of the improved connection system and the optimised beam position. In-situ measurements were made after construction, illustrating good correspondence between the digital and built model. Further research will focus on the covering of this modular reciprocal dome for outdoor use.Architectural Technolog
Automated taxonomy discovery and exploration
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Previous issue date: 2021-12-02Embargo set by: Seth Robbins for item 123360
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I OnlySubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-12-01The student, Jiaming Shen, accepted the attached license on 2021-12-02 at 09:21.The student, Jiaming Shen, submitted this Dissertation for approval on 2021-12-02 at 09:23.This Dissertation was approved for publication on 2021-12-02 at 11:28.DSpace SAF Submission Ingestion Package generated from Vireo submission #17342 on 2022-04-06 at 17:17:40In an era of information explosion, people are inundated with vast amounts of text data. Every day, there are thousands of scientific papers, tens of thousands of news articles, corporate reports, and millions of social media posts produced and shared worldwide. Turning those massive text data into actionable knowledge is an essential research issue in data science and lays the foundation for realizing machine intelligence.
The goal of my research is to unleash hidden knowledge buried in unstructured text. To bring this vision to reality, I propose to first structure raw text using taxonomies and then analyze structured text in a more fine-grained and semantic way. Due to the diversity of application scenarios, different corpora or different use cases may call for different taxonomies. For example, one analyst aiming to find experts in different scientific areas may want a field-of-study taxonomy, while another analyst who studies the technology readiness may call for a taxonomy capturing technology dependencies. Moreover, even within one taxonomy, we also enable users to organize concepts at their will, such as with different levels containing concepts of different categories. For instance, in a computer science taxonomy, top levels could be about the field of studies, intermediate levels may discuss research tasks, and the bottom levels can cover evaluation metrics. Asking human experts to manually curate those taxonomies, one for every possible application, is time-consuming, costly, and unscalable. Therefore, we propose to automatically discover and explore taxonomies based on the datasets and applications, with critical but minimal human guidance.
This thesis outlines a data-driven approach that automatically constructs, enriches, and applies taxonomies for unleashing knowledge from massive unstructured text. Particularly, we investigate four areas of research, including:
(1) Identifying Concept Sets. To obtain concept nodes in the taxonomy, we first develop a collection of concept set expansion methods [1, 2] to extract concepts from text corpora by expanding a small set of seed concepts into a complete list of concepts that belong to the same semantic class.
(2) Recognizing Taxonomic Relations. To organize the above-identified concepts into a hierarchical structure, we propose a set of taxonomy construction methods [3, 4] to discover taxonomic relations among concepts by analyzing example relation instances (i.e., concept pairs indicating the target relation semantics) and utilizing distant supervision from existing, open-domain knowledge bases.
(3) Enriching Existing Taxonomies. As human knowledge is constantly growing, a static taxonomy may fail to capture emerging user needs. Thus, a taxonomy enrichment step would be essential to keep our taxonomies up-to-date in real-world applications. We facilitate this process by expanding the taxonomy to incorporate new concepts [5, 6, 7].
(4) Empowering Knowledge-centric Applications. After an up-to-date taxonomy is obtained, we develop principled methods to distill knowledge from taxonomies for downstream applications such as text categorization [8, 9] and intelligent literature search [10, 11].
Finally, we explore how to incorporate event knowledge into the taxonomy by automatically detecting event types from a given corpus. Together, these pieces constitute an integrated framework for leveraging taxonomies to convert massive text data into actionable knowledge
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