14,322 results on '"WU Qiong"'
Search Results
2. Crystal structure of 1-(4-chlorophenyl)-4-(2-furoyl)-3-phenyl-1H-pyrazol-5-ol, C20H13ClN2O3
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Wu Qiong, Zhang Wei-Ya, Zhang Zi-Mo, Zhang Heng-Qiang, and Jin Tong-Yin
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2343323 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C20H13ClN2O3, monoclinic, P21/n (no. 14), a = 6.2256(5) Å, b = 18.3976(15) Å, c = 14.5582(15) Å, β = 90.555(8)°, V = 1667.4(3) Å3, Z = 4, R gt(F) = 0.0443, wR ref(F 2) = 0.1003, T = 153(2) K.
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- 2024
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3. Crystal structure of 1-(p-tolylphenyl)-4-(2-furoyl)-3-methyl-1H-pyrazol-5-ol, C16H14N2O3
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Wu Qiong, Huang Zhen-Yu, Liu Yun-Xin, Zhang Heng-Qiang, Jin Tong-Yin, Xue Yu-Na, and Liu Chang
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2331007 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C16H14N2O3, monoclinic, P21/n (no. 14), a = 4.9621(10) Å, b = 11.828(2) Å, c = 23.727(5) Å, β = 94.527(4)°, V = 1388.2(5) Å3, Z = 4, Rgt(F) = 0.0551, wRref(F2) = 0.1847, T = 296(2) K.
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- 2024
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4. Construction and Validation of A Predictive Model Including TCM Pathogenic Syndrome for Short-term Efficacy of PD-1 Inhibitors in Advanced Non-small Cell Lung Cancer
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MA Junyan, WU Qiong, DONG Liang, LI Chunyang, and WANG Zhiwu
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advanced non-small cell lung cancer ,pd-1 inhibitor ,traditional chinese medicine syndrome elements ,short-term efficacy ,predictive model ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model. Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group. Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance. Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.
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- 2023
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5. The Nature of Ideological and Political Education in Colleges and Universities Based on Deep Learning Models and Its Development
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Wu Qiong
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disambiguation algorithms ,character sequences ,feature sparsity ,corpus ,word-level n-grams ,97u10 ,Mathematics ,QA1-939 - Abstract
In this paper, first of all, after extracting the character sequence information, a certain scale of the corpus is obtained by using a crawler, and a corpus in the field of ideological and political education, as well as a participle system, is constructed. Then, the sequence decoding problem is solved by combining the idea of dynamic programming, based on the word level N-gram language algorithm, to design and implement an efficient solution method and calculate the final result of the participle. Finally, the keywords of related literature are classified, the essential dimensions of ideological and political education can be derived, and the essence of ideological and political education is explored and analyzed by using the word division algorithm. The results show that the most important essence of ideological and political education is “educating people’s feelings” with a weight value of 0.46, and among the 24 secondary nodes, there are 19 items with coefficients of variation less than or equal to 0.3, which shows that the degree of consistency is high, indicating that the degree of importance of the essence of ideological and political education is also high.
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- 2024
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6. Prediction Model of Treatment Effect of Anlotinib on Extensive-stage Small Cell Lung Cancer Based on Combination of Disease and Syndrome Information
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WU Qiong, MA Junyan, DONG Liang, LI Chunyang, and WANG Zhiwu
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anlotinib ,small cell lung cancer ,traditional chinese medicine syndrome elements ,predictive model ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Objective To construct a nomogram prediction model for the treatment effect of anlotinib with the participation of traditional Chinese medicine syndrome elements on the patients with extensive-stage small cell lung cancer (ES-SCLC) who previously received multiple lines of chemotherapy. Methods The clinical data of 127 patients with ES-SCLC who received at least two cycles of anlotinib treatment were retrospectively studied. Kaplan-Meier method was used to analyze the relationship between each factor and the overall survival time. Cox regression analysis was applied to screen the independent influencing factors of the prognosis of patients with ES-SCLC. R language was employed to build a nomogram prediction model, C-index was used to evaluate the model, and calibration curve was adopted to verify the accuracy of the model. Results Age, PS score, brain metastases, qi deficiency syndrome, yin deficiency syndrome, and blood stasis syndrome were related risk factors for ES-SCLC treated with anlotinib. PS score, brain metastasis, and blood stasis syndrome were independent prognostic factors. On the basis of these three independent influencing factors, a nomogram model was established to predict the prognosis of patients with ES-SCLC treated with anlotinib. The predicted risk was close to the actual risk, showing a high degree of coincidence. Conclusion The nomogram model established with PS score, blood stasis syndrome elements, and brain metastasis as independent factors can predict the prognosis of patients with ES-SCLC receiving second- and third-line treatment of anlotinib.
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- 2023
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7. A simulation modeling methodology considering random multiple shots for shot peening process
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Gao Hanjun, Lin Minghui, Guo Jing, Yang Liang, Wu Qiong, Ran Ziliang, and Xue Nianpu
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shot peening ,random multiple ,residual stress ,roughness ,deformation ,Technology ,Chemical technology ,TP1-1185 - Abstract
Shot peening (SP) process is a typical surface strengthening process for metal and metal matrix composites, which can significantly improve the fatigue life and strength. The traditional SP simulation model falls short as it only takes into account one or a few shots, proving insufficient for accurately simulating the entire impact process involving hundreds of shots. In this study, a random multiple shots simulation modeling methodology with hundreds of random shots is proposed to simulate the impact process of SP. In order to reduce the simulation error, the random function Rand of MATLAB is used to generate the shot distributions many times, and the shot distribution closest to the average number is selected and the three-dimension parametric explicit dynamics numerical simulation model is built using ABAQUS software. Orthogonal experiments are carried out to investigate the influences of shot diameter, incident impact velocity, and angle on the residual stress distribution, roughness, and specimen deformation. Results showed that the average relative errors of maximum residual compressive stress, roughness, and deformation of specimen between simulation model and experimental value are 30.99, 16.14, and 16.73%, respectively. The primary factors affecting residual stress and deformation is shot diameter, and the main factor affecting roughness is impact velocity.
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- 2023
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8. Research on cleaning scheme of photovoltaic power plant based on pollutant composition analysis
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Cheng Wenji, Wang Shujuan, Zhao Lei, Yang Bo, Niu Kai, and Wu Qiong
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Environmental sciences ,GE1-350 - Abstract
Solar photovoltaic (PV) modules, as an important component of the photovoltaic conversion process within a PV system, are closely related to the efficiency and service life of the PV system’s overall work performance. Dust in the atmosphere is affected by rainfall, electrostatic forces, van der Waals forces and gravity, and will gather together and settle on the surface of the PV module to form dust accumulation, which reduces the amount of radiation and light energy absorption. Therefore, it is really necessary to carry out the research on the pollutant composition and treatment measures program of the surface pollution index of PV modules.
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- 2024
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9. Application of DNA barcoding technology in the identification of adulterated ingredients of edible blood products
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LI Jiong, WU Qiong, JIANG Hai, HU Ming-jie, and XU Xin-yi
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dna mini-barcoding ,edible blood products ,cytochrome coxidase subunit i ,adulteration ,Food processing and manufacture ,TP368-456 - Abstract
Objective: A COI sequence-based full-length DNA barcoding technology was established to identify adulteration of seven animal-derived components (including pigs, cows, sheep, chickens, ducks, geese and rabbits) in edible blood products. Methods: After the blood products were extracted and purified by DNA extraction and PCR amplification, the clone sequencing results were submitted to the DNA barcode local database of the 7 blood products for sequence comparison. The pretreatment method, DNA extraction method and PCR amplification conditions of the blood products were also selected and optimized, and various common blood product adulteration models were established to study the lowest adulteration detection rate of the models. Results: The results showed that the DNA amplification efficiency of seven edible blood products was 100%, and the minimum adulteration detection rate in various adulteration models was 5%; 25 batches of commercially available edible blood products were identified as adulterated using the method, and 21 batches were found to be adulterated with other animal-derived ingredients. Conclusion: The method is simple, reliable and can be used as a testing method for daily quality supervision of edible blood products.
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- 2023
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10. {6,6′-((1E,1′E)-((2,2-dimethylpropane-1,3-diyl)bis(azaneylylidene))bis(methaneylylidene))bis(2-bromo-4-chlorophenolate)-κ4N,N′,O,O′}copper(II), C19H16Br2Cl2CuN2O2
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Wu Qiong, Yu Guojun, Yang Jiao, and Yang Qiuling
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2178589 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C19H16Br2Cl2CuN2O2, monoclinic, P21/n (no. 14), a = 12.4679(2)Å, b = 8.61760(10) Å, c = 19.7854(3) Å, β = 97.1610(10)°, V = 2109.23(5) Å3, Z = 4, R gt(F) = 0.0325, wR ref(F 2) = 0.0865, T = 170.0 K.
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- 2022
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11. [5-Bromo-2-(2-(dimethylamino)ethyliminomethyl)phenolato-κ3 N,N′,O]-isothiocyanato-nickel(II), C12H14BrN3NiOS
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Huang Meifen, Chen Zhizheng, Xu Jiajun, and Wu Qiong
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2178805 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C12H14BrN3NiOS, monoclinic, P21/c (no. 14), a = 7.0191(5) Å, b = 10.8476(7) Å, c = 19.0237(12) Å, β = 95.564(2), V = 1441.65(17) Å3, Z = 4, R gt(F) = 0416, wR ref(F 2) = 0.1007, T = 148 K.
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- 2022
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12. Crystal structure of 2-chloro-6-formylphenolato-κ2O,O′-(6,6′-(((2,2-dimethylpropane-1,3-diyl)bis(azaneylylidene))bis(methaneylylidene))bis(2-chlorophenolato)κ4 N,N,O,O′)cobalt(III), C26H22Cl3CoN2O4
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Lan Qiaoling, Huang Meifen, Xu Jiajun, and Wu Qiong
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2178582 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C26H22Cl3CoN2O4, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 11.1835(5) Å, b = 11.6782(5) Å, c = 11.9295(5) Å, α = 83.900(3)°, β = 66.911(4)°, γ = 64.232(4)°, V = 1286.83(11) Å3, Z = 2, R gt(F) = 0375, wR ref(F 2) = 0.0899, T = 213.0 K.
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- 2022
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13. {2-(((2-aminoethyl)imino)methyl)-6-bromophenolato-κ3 N,N′,O}iron(III) nitrate, C18H20Br2FeN5O5
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Yu Guojun, Yang Jiao, Yang Qiuling, and Wu Qiong
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2178589 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C18H20Br2FeN5O5, monoclinic, P21/c (no. 14), a = 12.4534(3) Å, b = 10.8106(3) Å, c = 16.0339(4) Å, β = 94.0450(10)°, V = 2153.25(10) Å3, Z = 4, R gt(F) = 0276, wR ref(F 2) = 0.0644, T = 170.0 K.
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- 2022
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14. Research on security access model of coal mine safety supervision cloud data based on blockchain
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TAN Liangjie, LI Yongfei, and WU Qiong
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coal mine safety informatization ,coal mine safety supervision data ,cloud data ,blockchain ,access control ,authority management ,intelligent contract ,Mining engineering. Metallurgy ,TN1-997 - Abstract
The management and control of coal mine safety supervision cloud data is very strict, and the design of access authority should satisfy the requirements of classification and security. At present, coal mine safety supervision cloud data has the problems of unclear classification and hierarchy and weak confidentiality in the security management and contrd dimension. And the existing cloud data management and control models are difficult to meet the security requirements of coal mine safety supervision data. In order to solve the above problems, the security access model of coal mine safety supervision cloud data based on blockchain is designed, including access authority model and access control model. Based on the analysis of the access attributes and access objects of coal mine safety supervision cloud data, an access authority model based on user hierarchy and data attributes is designed. The model realizes the classification and hierarchy management and control of cloud data and dynamic generation of authority. Based on the advantages of distributed realization, full transparency and tamper-proof of blockchain, the cloud data access control model is constructed. The model realizes distributed access control, ensures the security of access control by intelligent contract, and enhances the security protection of authority information by encryption technology. The comparative analysis results shows that compared with the common role-based access control(RBAC) model and attribute-based access control(ABAC) model, the access authority model based on user hierarchy and data attributes realizes the fine-grained access authority division for the coal mine safety supervision cloud data. The user authority is intuitive, the authority rules are simple to generate. The access authority model meets the security requirements of the coal mine safety supervision cloud data. Compared with the access control model based on the third party, the access control model based on the blockchain uses the intelligent contract for access control. The model can enhance the security of the coal mine safety supervision cloud data, provide a new solution for the cloud data security problem, and meets the needs of data security access in more scenarios.
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- 2022
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15. Study on stress distribution of SiC/Al composites based on microstructure models with microns and nanoparticles
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Gao Zi-han, Gao Han-jun, Zhang Yi-du, Wu Qiong, Chen Shu-guang, and Zhou Xin
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micro-stress distribution ,multi-shaped particles ,microns and nanoparticles ,representative volume element ,sub-model boundary conditions ,Technology ,Chemical technology ,TP1-1185 ,Physical and theoretical chemistry ,QD450-801 - Abstract
The simulation model represents the microstructure that can accurately analyze and predict composites’ micro-stresses and mechanical properties. The conventional representative volume element (RVE) model can only contain one single-particle form. It makes that all the particles in the simulation model have the same shape, which is significantly different from actual particles. In the present study, four typical particle-modeling methods were adopted to establish geometric models to analyze the particle morphology and RVE size selection rules. Particles with the same granularity and similar volume were selected to generate RVE models with randomly distributed particles to predict the mechanical properties and analyze the micro-stress. The micro-stress distribution of the matrix and particles conformed to the rule of normal distribution, while the stress of the interphase does not conform to this law. The particle morphology has a negligible effect on the stress distribution of the matrix; however, it has a significant influence on the stress distribution of particles and interphases, especially during plastic deformation. Furthermore, the micro-stress of composites containing nanoparticles also conforms to the above law, but the stress of the interphase is more minor, and the stress of particles is more dispersed than composites with micron particles.
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- 2022
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16. Research on process optimization and rapid prediction method of thermal vibration stress relief for 2219 aluminum alloy rings
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Chen Shuguang, Gao Hanjun, Lin Minghui, Wu Shaofeng, and Wu Qiong
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2219 aluminum alloy ,thermal vibration stress relief ,process optimization ,genetically optimized neural networks ,Technology ,Chemical technology ,TP1-1185 - Abstract
2219 aluminum alloy rings are important part of liquid cryogenic rocket fuel tanks. Residual stress is inevitably introduced in the forming process of the rings due to the nonlinear thermomechanical coupling conditions, which will affect its mechanical properties, fatigue properties, corrosion resistance, and dimensional stability. Thermal vibratory stress relief (TVSR) has great potential in reducing residual stress, and process optimization of TVSR is necessary to further improve its application, but it is rarely reported. In this study, process optimization of roll formed 2219 aluminum alloy rings is conducted. The influence of vibration amplitude, vibration time, vibration frequency, heating time, holding time, and cooling time on TVSR treatment are investigated. Results show that the maximum equivalent residual stress of 2219 aluminum alloy rings can be reduced by 93.6% after optimized TVSR treatment. With the increase in vibration time, heating time, holding time, and cooling time, the maximum equivalent stress decreases. However, the increase in the vibration amplitude results in an increase in the maximum equivalent stress. Further, a genetically optimized artificial neural network intelligent optimization algorithm is applied to quickly predict the TVSR effect of 2219 aluminum alloy rings.
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- 2022
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17. Crystal structure of (Z)-4-(((4-bromophenyl)amino)(furan-2-yl)methylene)-2,5-diphenyl-2,4-dihydro-3H-pyrazol-3-one, C26H18BrN3O2
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Li Zhang, Wu Qiong, Zhang Ya-Zhai, Zhang Wei-Ya, Chen Hong-Li, and Zhang Heng-Qiang
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2167367 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C26H18BrN3O2, monoclinic, P21/n (no. 14), a = 9.5191(14) Å, b = 23.056(3) Å, c = 10.0556(15) Å, β = 95.470(3)°, V = 2196.9(6) Å3, Z = 4, Rgt(F) = 0.0640, wRref(F2) = 0.1645, T = 296(2) K.
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- 2022
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18. Crystal structure of (Z)-4-[(p-tolylamino)(furan-2-yl)methylene]-3-phenyl-1-1-p-tolyl-1H-phenyl-1H-pyrazol-5(4H)-one, C28H23N3O2
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Wei Wei, Wu Qiong, Jin Tong-Yin, Zhang Ya-Zhai, Xuan Zhao-Kun, and Zhang Heng-Qiang
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2164536 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C28H23N3O2, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 7.6590(7) Å, b = 11.8598(12) Å, c = 12.4954(14) Å, α = 92.027(9)°, β = 99.837(9)°, γ = 93.597(8)°, V = 1114.9(2) Å3, Z = 2, R gt(F) = 0.0508, wR ref(F 2) = 0.1317, T = 293(2) K.
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- 2022
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19. Crystal structure of (Z)-4-(((4-fluorophenyl)amino)(furan-2-yl)methylene)-5-methyl-2-phenyl-2,4-dihydro-3H-pyrazol-3-one
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Wu Qiong, Zhang Li, Gao Jun-Jie, Yang Liu, Zhang Heng-Qiang, Fan Li-Li, and Zhang Ya-Zhai
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2170694 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C22H16FN3O2, triclinic, P1‾$\overline{1}$ (no. 2), a = 8.7335(18) Å, b = 9.967(2) Å, c = 11.968(3) Å, α = 70.677(4)°, β = 86.259(4)°, γ = 65.759(4) Å, V = 893.4(3) Å3, Z = 2, Rgt(F) = 0.0543, wRref (F2) = 0.1677, T = 296(2) K.
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- 2022
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20. Gate-tuned graphene meta-devices for dynamically controlling terahertz wavefronts
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Li Qiushi, Cai Xiaodong, Liu Tong, Jia Min, Wu Qiong, Zhou Haoyang, Liu Huanhuan, Wang Qianqian, Ling Xiaohui, Chen Cong, Ding Fan, He Qiong, Zhang Yuanbo, Xiao Shiyi, and Zhou Lei
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coupled mode theory ,graphene ,metasurfaces ,terahertz ,wavefront manipulations ,Physics ,QC1-999 - Abstract
Dynamical controls on terahertz (THz) wavefronts are crucial for many applications, but available mechanism requests tunable elements with sub-micrometer sizes that are difficult to find in the THz regime. Here, different from the local-tuning mechanism, we propose an alternative approach to construct wavefront-control meta-devices combining specifically designed metasurfaces and globally tuned graphene layers. Coupled-mode-theory (CMT) analyses reveal that graphene serves as a tunable loss to drive the whole meta-device to transit from one functional phase to another passing through an intermediate regime, exhibiting distinct far-field (FF) reflection wavefronts. As a proof of concept, we design/fabricate a graphene meta-device and experimentally demonstrate that it can reflect normally incident THz wave to pre-designed directions with different polarizations under appropriate gating voltages. We finally design a graphene meta-device and numerically demonstrate that it can generate vectorial THz beams with continuously varying polarization distributions upon gating. These findings pave the road to realizing a wide range of THz applications, such as sensing, imaging, and wireless communications.
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- 2022
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21. Residual stress relief mechanisms of 2219 Al–Cu alloy by thermal stress relief method
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Song Hechuan, Gao Hanjun, Wu Qiong, and Zhang Yidu
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2219 al–cu alloys ,thermal stress relief ,residual stresses ,dislocations ,mechanisms ,Technology ,Chemical technology ,TP1-1185 - Abstract
Monolithic thin-wall components of 2219 Al–Cu alloy are widely used in aerospace and military fields, and usually treated with solution and quenching to improve their comprehensive performance. However, a high magnitude residual stress is introduced into the components during the quenching process, which is unfavorable to the subsequent manufacturing process and service performance. Therefore, residual stress relief is essential to enhance the performance of the components. A conventional effective method is thermal stress relief (TSR). However, the underlying mechanisms of TSR still remain unclear and lack a quantitative interpretation. In the present work, the evolution and distribution laws of the residual stresses, tensile properties, Vickers hardness, dislocations, precipitated phases, and metallography during TSR were investigated. Based on the experimental results, dislocation theory and strengthening mechanisms were applied to reveal the underlying mechanisms of the residual stress relief by TSR. The results showed that the circumferential and axial residual stress relief rates can reach 86.37 and 85.77% after TSR, respectively. The residual stress relief after TSR is attributed to the dynamic evolution of dislocation configuration and density. The improvement in the mechanical properties mainly depends on the precipitated phases and is also affected by the stress orientation effect caused by the residual stress.
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- 2022
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22. Relationship between cognitive impairment induced by local radiation and morphological changes of hippocampal microglia
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ZHANG Yuan, LIN Yi, WU Qiong, LIU Zhenghai, LI Cai, and HE Jie
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irradiation ,mice ,hippocampal ,cognitive impairment ,microglia ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 ,Medical physics. Medical radiology. Nuclear medicine ,R895-920 - Abstract
To investigate the role of microglia in the process of cognitive impairment caused by local ionizing radiation, eighteen male mice from the institute of cancer research (ICR) were randomly divided into a control group (Control) and a head 10 Gy irradiation group (Irradiation, 10 Gy-IR). The head ionization irradiation group underwent X-ray (Siemens linear accelerator) single head irradiation illuminated to an absorbed dose of 10 Gy. Fifty-six days later, the new and old object recognition test and the new and old place recognition test were used to test the cognitive function of the mice; immunohistochemistry was used to detect the mouse hippocampal microglia marke (IBA-1) change; RT-PCR was used to detect changes at the mRNA level of hippocampal M1/M2 microglia markers (CD16, INOS, CD68, CXCL10, Arg1, CD206). The results showed that compared with the control group, the time discrimination index of new things (p
- Published
- 2021
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23. The optimization of friction disc gear-shaping process aiming at residual stress and machining deformation
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Gao Hanjun, Li Xin, Wu Qiong, Zhang Wanhao, Dai Guowen, and Zhang Guohong
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friction disc ,machining deformation ,residual stress ,Technology ,Chemical technology ,TP1-1185 - Published
- 2021
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24. DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
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Gu, Xueying, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, and Letaief, Khaled B.
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Networking and Internet Architecture - Abstract
Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL., Comment: This paper has been submitted to Digital Communications and Networks. The source code has been released at: https://github.com/qiongwu86/Federated-SSL-task-offloading-and-resource-allocation
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- 2024
25. DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
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Gu, Xueying, Wu, Qiong, Fan, Pingyi, Fan, Qiang, Cheng, Nan, Chen, Wen, and Letaief, Khaled B.
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to Base Station (BS), which poses a risk of privacy leakage. Simplified Contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the Central Processing Unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/DRL-BFSSL
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- 2024
26. Mobility-Aware Federated Self-supervised Learning in Vehicular Network
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Gu, Xueying, Wu, Qiong, Fan, Pingyi, and Fan, Qiang
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Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU). This enables FL to handle scenarios with sensitive or widely distributed data. However, in these fields, it is well known that the labeling costs can be a significant expense, and models relying on labels are not suitable for these rapidly evolving fields especially in vehicular networks, or mobile internet of things (MIoT), where new data emerges constantly. To handle this issue, the self-supervised learning paves the way for training without labels. Additionally, for vehicles with high velocity, owing to blurred images, simple aggregation not only impacts the accuracy of the aggregated model but also reduces the convergence speed of FL. This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment. Simulation results demonstrate that the proposed algorithm exhibits fast and stable convergence., Comment: This paper has been submitted to urban lifeline. The source code has been released at: The source code has been released at: https://github.com/qiongwu86/FLSimCo
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- 2024
27. Age of Information Analysis for Multi-Priority Queue and NOMA Enabled C-V2X in IoV
- Author
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Zhang, Zheng, Wu, Qiong, Fan, Pingyi, and Xiong, Ke
- Subjects
Computer Science - Networking and Internet Architecture ,Computer Science - Performance - Abstract
As development Internet-of-Vehicles (IoV) technology and demand for Intelligent Transportation Systems (ITS) increase, there is a growing need for real-time data and communication by vehicle users. Traditional request-based methods face challenges such as latency and bandwidth limitations. Mode 4 in Connected Vehicle-to-Everything (C-V2X) addresses latency and overhead issues through autonomous resource selection. However, Semi-Persistent Scheduling (SPS) based on distributed sensing may lead to increased collision. Non-Orthogonal Multiple Access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Moreover, the concept of Age of Information (AoI) is introduced as a comprehensive metric reflecting reliability and latency performance, analyzing the impact of NOMA on C-V2X communication system. AoI indicates the time a message spends in both local waiting and transmission processes. In C-V2X, waiting process can be extended to queuing process, influenced by packet generation rate and Resource Reservation Interval (RRI). The transmission process is mainly affected by transmission delay and success rate. In C-V2X, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collision rates with increased number of vehicles. SW is generally equal to RRI, which not only affects AoI in queuing process but also AoI in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. This work aims to gain a better performance of C-V2X system comparing with some known algorithms., Comment: This paper has been submitted to WCSP 2024. The source code has been released at: https://github.com/qiongwu86/Analysis-of-the-Impact-of-Multi-Priority-Queue-and-NOMA-on-Age-of-Information-in-C-V2X
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- 2024
28. Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models
- Author
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Wu, Qiong, Ke, Zhaoxi, Zhou, Yiyi, Luo, Gen, Sun, Xiaoshuai, and Ji, Rongrong
- Subjects
Computer Science - Multimedia - Abstract
Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multi-modal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic expert path in an already exist MLLM and show that a standard MLLM can be also a mixture of experts. To approach this target, we propose a novel dynamic expert scheme for MLLMs, termed Routing Experts (RoE), which can achieve example-dependent optimal path routing without obvious structure tweaks. Meanwhile, a new regularization of structure sparsity is also introduced to enforce MLLMs to learn more short-cut inference, ensuring the efficiency. In addition, we also realize the first attempt of aligning the training and inference schemes of MLLMs in terms of network routing. To validate RoE, we apply it to a set of latest MLLMs, including LLaVA-1.5, LLaVA-HR and VILA, and conduct extensive experiments on a bunch of VL benchmarks. The experiment results not only show the great advantages of our RoE in improving MLLMs' efficiency, but also yield obvious advantages than MoE-LLaVA in both performance and speed, e.g., an average performance gain of 3.3% on 5 benchmarks while being faster.
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- 2024
29. Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
- Author
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Qi, Kangwei, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, and Letaief, Khaled B.
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of Reconfigurable Intelligent Surfaces (RIS), which provide alternative communication pathways to assist vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider a RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient (TD3) and some typical stochastic schemes., Comment: This paper has been submitted to IEEE Journal. The source code has been released at https://github.com/qiongwu86/DDPG-RIS-MADDPG-POWER. arXiv admin note: text overlap with arXiv:2406.11318
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- 2024
30. Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
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Xie, Yu, Wu, Qiong, and Fan, Pingyi
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Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
With the increasing demand for multiple applications on internet of vehicles. It requires vehicles to carry out multiple computing tasks in real time. However, due to the insufficient computing capability of vehicles themselves, offloading tasks to vehicular edge computing (VEC) servers and allocating computing resources to tasks becomes a challenge. In this paper, a multi task digital twin (DT) VEC network is established. By using DT to develop offloading strategies and resource allocation strategies for multiple tasks of each vehicle in a single slot, an optimization problem is constructed. To solve it, we propose a multi-agent reinforcement learning method on the task offloading and resource allocation. Numerous experiments demonstrate that our method is effective compared to other benchmark algorithms., Comment: This paper has been submitted to ICICSP 2024. The source code has been released at:https://github.com/qiongwu86/Digital-Twin-Vehicular-Edge-Computing-Network_Task-Offloading-and-Resource-Allocation
- Published
- 2024
31. Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
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Zhang, Cui, Zhang, Wenjun, Wu, Qiong, Fan, Pingyi, Fan, Qiang, Wang, Jiangzhou, and Letaief, Khaled B.
- Subjects
Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient Quantization-for-Federated-Learning-Enabled-Vehicle-Edge-Computing
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- 2024
32. Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning
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Song, Shulin, Zhang, Zheng, Wu, Qiong, Fan, Qiang, and Fan, Pingyi
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Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Autonomous driving may be the most important application scenario of next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancement in cellular V2X (C-V2X) with improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur, and thus degrade the age of information (AOI). Therefore, a interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation interval (RRI), higher-frequency transmissions take ore energy to reduce AoI. Hence, it is important to jointly consider AoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations have demonstrated the performance of our proposed algorithm., Comment: This paper has been accepted by sensors. The source code has been released at: https://github.com/qiongwu86/Joint-Optimization-of-AoI-and-Energy-Consumption-in-NR-V2X-System-based-on-DRL
- Published
- 2024
33. A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
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Semnani, Parastoo, Bogojeski, Mihail, Bley, Florian, Zhang, Zizheng, Wu, Qiong, Kneib, Thomas, Herrmann, Jan, Weisser, Christoph, Patcas, Florina, and Müller, Klaus-Robert
- Subjects
Physics - Chemical Physics ,Computer Science - Machine Learning - Abstract
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catalyst design has long relied on trial-and-error, a costly and labor-intensive process leading to scarce data that is heavily biased towards undesired, low-yield catalysts. Despite the rise of ML in this field, most efforts have not focused on dealing with the challenges presented by such experimental data. To address these challenges, we introduce a robust machine learning and explainable AI (XAI) framework to accurately classify the catalytic yield of various compositions and identify the contributions of individual components. This framework combines a series of ML practices designed to handle the scarcity and imbalance of catalyst data. We apply the framework to classify the yield of various catalyst compositions in oxidative methane coupling, and use it to evaluate the performance of a range of ML models: tree-based models, logistic regression, support vector machines, and neural networks. These experiments demonstrate that the methods used in our framework lead to a significant improvement in the performance of all but one of the evaluated models. Additionally, the decision-making process of each ML model is analyzed by identifying the most important features for predicting catalyst performance using XAI methods. Our analysis found that XAI methods, providing class-aware explanations, such as Layer-wise Relevance Propagation, identified key components that contribute specifically to high-yield catalysts. These findings align with chemical intuition and existing literature, reinforcing their validity. We believe that such insights can assist chemists in the development and identification of novel catalysts with superior performance.
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- 2024
34. Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network
- Author
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Xie, Yu, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, Wang, Jiangzhou, and Letaief, Khaled B.
- Subjects
Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles. However, maintaining the vehicle DT model requires ongoing attention from the VEC server, which also needs to offer computing services for the vehicles. Therefore, effective allocation and scheduling of VEC server resources are crucial. This study focuses on a general VEC network with a single VEC service and multiple vehicles, examining the two types of delays caused by twin maintenance and computational processing within the network. By transforming the problem using satisfaction functions, we propose an optimization problem aimed at maximizing each vehicle's resource utility to determine the optimal resource allocation strategy. Given the non-convex nature of the issue, we employ multi-agent Markov decision processes to reformulate the problem. Subsequently, we propose the twin maintenance and computing task processing resource collaborative scheduling (MADRL-CSTC) algorithm, which leverages multi-agent deep reinforcement learning. Through experimental comparisons with alternative algorithms, it demonstrates that our proposed approach is effective in terms of resource allocation., Comment: This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network
- Published
- 2024
35. Enhancing Robustness and Security in ISAC Network Design: Leveraging Transmissive Reconfigurable Intelligent Surface with RSMA
- Author
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Liu, Ziwei, Chen, Wen, Wu, Qingqing, Li, Zhendong, Zhu, Xusheng, Wu, Qiong, and Cheng, Nan
- Subjects
Computer Science - Information Theory ,Electrical Engineering and Systems Science - Signal Processing - Abstract
In this paper, we propose a novel transmissive reconfigurable intelligent surface transceiver-enhanced robust and secure integrated sensing and communication network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise, while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent-based second-order cone programming algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein's inequality and successive convex approximation are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency and the Cram\'{e}r-Rao boundary.
- Published
- 2024
36. Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications
- Author
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Ji, Maoxin, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, Wang, Jiangzhou, and Letaief, Khaled B.
- Subjects
Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture - Abstract
In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments., Comment: 14 pages, 11 figures. This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/GNN-and-DRL-Based-Resource-Allocation-for-V2X-Communications
- Published
- 2024
37. Channel Characterization of IRS-assisted Resonant Beam Communication Systems
- Author
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Fang, Wen, Chen, Wen, Wu, Qingqing, Zhu, Xusheng, Wu, Qiong, and Cheng, Nan
- Subjects
Electrical Engineering and Systems Science - Systems and Control - Abstract
To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized by dynamically controlling the beam-splitting ratio between the two channels according to the loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.
- Published
- 2024
38. Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
- Author
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Wang, Wenhua, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, Wang, Jiangzhou, and Letaief, Khaled B.
- Subjects
Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Multiagent Systems ,Computer Science - Networking and Internet Architecture - Abstract
With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning
- Published
- 2024
39. Crystal structure of azido-k1 N-{6,6′-((((methylazanediyl)bis(propane-3,1-diyl))bis(azanylylidene))bis(methanylylidene))bis(2,4-dibromophenolato)k5 N,N′,N″,O,O′}cobalt(III)-methanol (1/1)), C21H23Br4CoN6O3
- Author
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Duan Rui, Tian Hui, Guojun Yu, and Wu Qiong
- Subjects
2112998 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C21H23Br4CoN6O3, monoclinic, P21/c (no. 14), a = 16.2300(4) Å, b = 20.9661(6) Å, c = 7.8269(2) Å, β = 97.2250(11)°, V = 2642.19(12) Å3, Z = 4, R gt(F) = 0.0336, wR ref(F 2) = 0.0981, T = 150.0 K.
- Published
- 2022
- Full Text
- View/download PDF
40. Crystal structure of [2,2′-{azanediyl)bis[(propane-3,1-diyl)(azanylylidene)methylylidene]} bis(3,5-dichlorophenolato)-κ2O,O′]-isothiocyanato-κN-iron(III), C21H19Cl4FeN4O2S
- Author
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Duan Rui, Guojun Yu, Tian Hui, Wang Man, and Wu Qiong
- Subjects
2111977 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C21H19Cl4FeN4O2S, monoclinic, P21/c (no. 14), a = 16.304(2) Å, b = 11.1675(15) Å, c = 14.1041(16) Å, β = 109.322(4)°, V = 2423.3(5) Å3, Z = 4, R gt(F) = 0.0802, wR ref(F 2) = 0.1769, T = 150.0 K.
- Published
- 2022
- Full Text
- View/download PDF
41. Crystal structure of 6,6′-((pentane-1,3-diylbis(azaneylylidene))bis(methaneylylidene))bis(2,4-dibromolphenolato-κ4 N,N′,O,O′)copper(II),) C19H16Br4CuN2O2
- Author
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Duan Rui, Man Wang, Meifen Huang, Ma Xun, and Wu Qiong
- Subjects
2113000 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C19H16Br4CuN2O2: monoclinic, P21/c (no. 14), a = 10.2649(3) Å, b = 10.1903(3) Å, c = 21.2494(6) Å, β = 100.522(1)°, V = 2185.36(11) Å3, Z = 4, wR 2 = 0.0730, T = 169.0 K.
- Published
- 2022
- Full Text
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42. [2,2′-{Ethane-1,2-diylbis[(azanylylidene)methanylylidene]}bis(3-bromo-2-hydroxyphenyl)]iron(III) nitrate, C20H12Br2CuN2O2
- Author
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Yang Jiao, Xu Jiajun, Liao Nichan, and Wu Qiong
- Subjects
2178590 ,Physics ,QC1-999 ,Crystallography ,QD901-999 - Abstract
C20H12Br2CuN2O2, orthorhombic, Pbca (no. 61), a = 7.7117(2) Å, b = 18.7735(4) Å, c = 24.8518(4) Å, V = 3597.93(13) Å3, Z = 8, Rgt (F) = 0399, wRref (F 2) = 0.1125, T = 213 K.
- Published
- 2022
- Full Text
- View/download PDF
43. Study of the Effect of the Driving Force on the Kinetics of CO2 Hydrate Growth in Coal Particles
- Author
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Liu Chuanhai, Chen Ran, Zhang Baoyong, Wu Qiang, Zhang Qiang, and WU Qiong
- Subjects
CO2 hydrates ,gas consumption ,growth rate ,heat of decomposition ,coal and gas outburst ,General Works - Abstract
Coal and gas outbursts are geological disasters occurring in the process of coal mining that can cause serious casualties and economic losses, among which CH4 is the main component of coal mine gas. However, there are still many coal seams around the world that are dominated by CO2. Although the frequency of CO2 gas outburst accidents is relatively low, CO2 outbursts are very violent, notably difficult to control and highly dangerous. The application of hydration curing technology to reduce the pressure and gradient of CO2 gas in the coal can effectively reduce the occurrence of coal and CO2 outburst. Accordingly, in this paper, experimental studies on the growth kinetics of CO2 hydrate with three driving forces (2, 2.5, 3 MPa) were carried out under four different coal particle sizes (C1: 0.425–0.850 mm, C2: 0.250–0.425 mm, C3: 0.180–0.250 mm, C4: 0–0.180 mm) to obtain kinetic parameters such as gas consumption, growth rate, and heat of decomposition during the synthesis of CO2 hydrate. The results show that the hydrate nucleation time in the same particle size system does not follow the same decreasing trend with increasing driving force. Gas consumption of CO2 hydrates in the same particle size system increased with increasing driving force, and there exists a critical value regarding the effect of the driving force on CO2 hydrate generation in coal particles with the particle size. Under the same temperature conditions, increasing the driving force in the particle size system could increase the CO2 hydrate growth rate. With decreasing coal particle size and increasing driving force, the promoting effect gradually exceed the inhibiting effect, which promote CO2 hydrate formation. Through linear fitting, an equation of the average growth rate of CO2 hydrates versus the driving force for the C1-C4 systems is fitted to provide a reference to predict the average CO2 hydrate growth rate. In the same medium, with increasing driving force, more heat is required for complete decomposition, which remains relatively stable, and the heat of decomposition of CO2 hydrates is the highest in the C1 medium, indicating that the presence of CO2 hydrates in the C1 system represents the most stable state.
- Published
- 2022
- Full Text
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44. Development of methods for detecting the fate of mesenchymal stem cells regulated by bone bioactive materials
- Author
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Jiang Le, Liu Zhongqun, Wang Zhaoyan, Su Yijun, Wang Yingjin, Wei Yaojie, Jiang Yanan, Jia Zhanrong, Ma Chunyang, Gang Fangli, Xu Nan, Zhao Lingyun, Wang Xiumei, Wu Qiong, Lu Xiong, and Sun Xiaodan
- Subjects
Bone bioactive materials ,High-throughput technology ,Transcriptomics ,Single cell RNA-seq ,Proteomics ,Materials of engineering and construction. Mechanics of materials ,TA401-492 ,Biology (General) ,QH301-705.5 - Abstract
The fate of mesenchymal stem cells (MSCs) is regulated by biological, physical and chemical signals. Developments in biotechnology and materials science promoted the occurrence of bioactive materials which can provide physical and chemical signals for MSCs to regulate their fate. In order to design and synthesize materials that can precisely regulate the fate of MSCs, the relationship between the properties of materials and the fate of mesenchymal stem cells need to be clarified, in which the detection of the fate of mesenchymal stem cells plays an important role. In the past 30 years, a series of detection technologies have been developed to detect the fate of MSCs regulated by bioactive materials, among which high-throughput technology has shown great advantages due to its ability to detect large amounts of data at one time. In this review, the latest research progresses of detecting the fate of MSCs regulated by bone bioactive materials (BBMs) are systematically reviewed from traditional technology to high-throughput technology which is emphasized especially. Moreover, current problems and the future development direction of detection technologies of the MSCs fate regulated by BBMs are prospected. The aim of this review is to provide a detection technical framework for researchers to establish the relationship between the properties of BMMs and the fate of MSCs, so as to help researchers to design and synthesize BBMs better which can precisely regulate the fate of MSCs.
- Published
- 2021
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45. CAT: Interpretable Concept-based Taylor Additive Models
- Author
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Duong, Viet, Wu, Qiong, Zhou, Zhengyi, Zhao, Hongjue, Luo, Chenxiang, Zavesky, Eric, Yao, Huaxiu, and Shao, Huajie
- Subjects
Computer Science - Machine Learning - Abstract
As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simply this process. CAT does not have to require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can explain model predictions through high-level concepts that human can understand.
- Published
- 2024
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46. Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)
- Author
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Bryan-Kinns, Nick, Ford, Corey, Zheng, Shuoyang, Kennedy, Helen, Chamberlain, Alan, Lewis, Makayla, Hemment, Drew, Li, Zijin, Wu, Qiong, Xiao, Lanxi, Xia, Gus, Rezwana, Jeba, Clemens, Michael, and Vigliensoni, Gabriel
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Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction ,Computer Science - Multimedia ,Computer Science - Sound ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 16th ACM Conference on Creativity and Cognition (C&C 2024), Chicago, USA., Comment: Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)
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- 2024
47. Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
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Qi, Kangwei, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Fan, Qiang, and Wang, Jiangzhou
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Computer Science - Multiagent Systems ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Machine Learning ,Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme., Comment: This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/RIS-VEC-MARL.git
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- 2024
48. Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks
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Qi, Kangwei, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Chen, Wen, Wang, Jiangzhou, and Letaief, Khaled B.
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model. Therefore, with the objective of minimizing the AoI of V2I links and prioritizing transmission of V2V links payload, we construct this optimization problem as an Markov decision process (MDP) problem in which the BS serves as an agent to allocate resources and control phase-shift for the vehicles using the soft actor-critic (SAC) algorithm, which gradually converges and maintains a high stability. A AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme based on SAC algorithm is proposed and simulation results show that its convergence speed, cumulative reward, AoI performance, and payload transmission probability outperforms those of proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3) and stochastic algorithms., Comment: This paper has been submitted to IEEE Journal. The source code has been released at https://github.com/qiongwu86/RIS-RB-AoI-V2X-DRL.git
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- 2024
49. IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
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Huang, Guangjing, Wu, Qiong, Li, Jingyi, and Chen, Xu
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Computer Science and Game Theory - Abstract
Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the heterogeneous data quality among clients, artificial intelligence-generated content (AIGC) can be leveraged as a novel data synthesis technique for FL model performance enhancement. Due to various costs incurred by AIGC-empowered FL (e.g., costs of local model computation and data synthesis), however, clients are usually reluctant to participate in FL without adequate economic incentives, which leads to an unexplored critical issue for enabling AIGC-empowered FL. To fill this gap, we first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained using a blend of authentic and AI-generated data samples. We then propose a data quality-aware incentive mechanism to encourage clients' participation. In light of information asymmetry incurred by clients' private multi-dimensional attributes, we investigate clients' behavior patterns and derive the server's optimal incentive strategies to minimize server's cost in terms of both model accuracy loss and incentive payments for both complete and incomplete information scenarios. Numerical results demonstrate that our proposed mechanism exhibits highest training accuracy and reduces up to 53.34% of the server's cost with real-world datasets, compared with existing benchmark mechanisms., Comment: The paper has been accepted by IEEE Transactions on Mobile Computing
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- 2024
50. Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
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Shao, Zhiyu, Wu, Qiong, Fan, Pingyi, Cheng, Nan, Fan, Qiang, and Wang, Jiangzhou
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Computer Science - Networking and Internet Architecture ,Electrical Engineering and Systems Science - Signal Processing - Abstract
This letter proposes a semantic-aware resource allocation (SARA) framework with flexible duty cycle (DC) coexistence mechanism (SARADC) for 5G-V2X Heterogeneous Network (HetNets) based on deep reinforcement learning (DRL) proximal policy optimization (PPO). Specifically, we investigate V2X networks within a two-tiered HetNets structure. In response to the needs of high-speed vehicular networking in urban environments, we design a semantic communication system and introduce two resource allocation metrics: high-speed semantic transmission rate (HSR) and semantic spectrum efficiency (HSSE). Our main goal is to maximize HSSE. Additionally, we address the coexistence of vehicular users and WiFi users in 5G New Radio Unlicensed (NR-U) networks. To tackle this complex challenge, we propose a novel approach that jointly optimizes flexible DC coexistence mechanism and the allocation of resources and base stations (BSs). Unlike traditional bit transmission methods, our approach integrates the semantic communication paradigm into the communication system. Experimental results demonstrate that our proposed solution outperforms traditional bit transmission methods with traditional DC coexistence mechanism in terms of HSSE and semantic throughput (ST) for both vehicular and WiFi users., Comment: This paper has been submitted to IEEE Letter.The source code has been released at: https://github.com/qiongwu86/Semantic-Aware-Resource-Allocation-Based-on-Deep-Reinforcement-Learning-for-5G-V2X-HetNets
- Published
- 2024
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