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Book ; Online: Narrating China's Governance

People's Daily, Department of Commentary

Stories in Xi Jinping's Speeches

2020

Author's details	by Department of Commentary People's Daily
Keywords	China—History ; Ethnology—Asia ; Political science ; Xi, Jinping ; China
Subject code	951
Language	English
Size	1 Online-Ressource (XXV, 266 p. 13 illus., 1 illus. in color)
Edition	1st ed. 2020
Publisher	Springer Singapore ; Imprint: Springer
Publishing place	Singapore
Document type	Book ; Online
HBZ-ID	HT020299921
ISBN	978-981-32-9178-2 ; 9789813291775 ; 9789813291799 ; 9789813291805 ; 981-32-9178-8 ; 981329177X ; 9813291796 ; 981329180X
DOI	10.1007/978-981-32-9178-2
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Generating Datasets for Real-Time Scheduling on 5G New Radio

Xi Jin / Haoxuan Chai / Changqing Xia / Chi Xu

Entropy, Vol 25, Iss 1289, p

2023 Volume 1289

Abstract: A 5G system is an advanced solution for industrial wireless motion control. However, because the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to ... ...

Abstract	A 5G system is an advanced solution for industrial wireless motion control. However, because the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to improve the 5G performance. This results in NR resources not being fully available for industrial systems. Supervised learning has been widely used to solve complicated problems, and its advantages have been demonstrated in multiprocessor scheduling. One of the main reasons why supervised learning has not been used for 5G NR scheduling is the lack of training datasets. Therefore, in this paper, we propose two methods based on optimization modulo theories (OMT) and satisfiability modulo theories (SMT) to generate training datasets for 5G NR scheduling. Our OMT-based method contains fewer variables than existing work so that the Z3 solver can find optimal solutions quickly. To further reduce the solution time, we transform the OMT-based method into an SMT-based method and tighten the search space of SMT based on three theorems and an algorithm. Finally, we evaluate the solution time of our proposed methods and use the generated dataset to train a supervised learning model to solve the 5G NR scheduling problem. The evaluation results indicate that our SMT-based method reduces the solution time by <semantics> 74.7 % </semantics> compared to existing ones, and the supervised learning algorithm achieves better scheduling performance than other polynomial-time algorithms.
Keywords	5G new radio ; real-time scheduling ; dataset ; optimization modulo theories ; satisfiability modulus theories ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Computational-Intelligence-Based Scheduling with Edge Computing in Cyber–Physical Production Systems

Changqing Xia / Xi Jin / Chi Xu / Peng Zeng

Entropy, Vol 25, Iss 12, p

2023 Volume 1640

Abstract: Real-time performance and reliability are two critical indicators in cyber–physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve ... ...

Abstract	Real-time performance and reliability are two critical indicators in cyber–physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve considerable redundant resources for unexpected jobs before production. However, traditional job-shop methods are difficult to apply under dynamic conditions due to the uncertain time cost of transmission and computation. Edge computing offers an efficient solution to this issue. By deploying edge servers around the equipment, smart factories can achieve localized decisions based on computational intelligence (CI) methods offloaded from the cloud. Most works on edge computing have studied task offloading and dispatching scheduling based on CI. However, few of the existing methods can be used for behavior-level control due to the corresponding requirements for ultralow latency (10 ms) and ultrahigh reliability (99.9999% in wireless transmission), especially when unexpected computing jobs arise. Therefore, this paper proposes a dynamic resource prediction scheduling (DRPS) method based on CI to achieve real-time localized behavior-level control. The proposed DRPS method primarily focuses on the schedulability of unexpected computing jobs, and its core ideas are (1) to predict job arrival times based on a backpropagation neural network and (2) to perform real-time migration in the form of human–computer interaction based on the results of resource analysis. An experimental comparison with existing schemes shows that our DRPS method improves the acceptance ratio by <semantics> 25.9 % </semantics> compared to the earliest deadline first scheme.
Keywords	manufacturing ; CPPS ; edge computing ; real-time ; scheduling ; Science ; Q ; Astrophysics ; QB460-466 ; Physics ; QC1-999
Subject code	670
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
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Article ; Online: Accurate and Interpretable Dipole Interaction Model-Based Machine Learning for Molecular Polarizability.

Feng, Chaoqiang / Xi, Jin / Zhang, Yaolong / Jiang, Bin / Zhou, Yong

Journal of chemical theory and computation

2023 Volume 19, Issue 4, Page(s) 1207–1217

Abstract: Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although ... ...

Abstract	Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields.
Language	English
Publishing date	2023-02-08
Publishing country	United States
Document type	Journal Article
ISSN	1549-9626
ISSN (online)	1549-9626
DOI	10.1021/acs.jctc.2c01094
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Prognostic Evaluation for Oral Squamous Cell Carcinoma: A Novel Method Based on m

Xu, Li / Yu, Cheng / Du, Xi-Jin

Current medical science

2022 Volume 42, Issue 4, Page(s) 841–846

Abstract: Objective: This study aimed to examine a novel method for prognostic evaluation of patients with oral squamous cell carcinoma (OSCC) based on the expression of heterogeneous nuclear ribonucleoprotein C (HNRNPC), YTH domain-binding protein 2 (YTHDF2), ... ...

Abstract	Objective: This study aimed to examine a novel method for prognostic evaluation of patients with oral squamous cell carcinoma (OSCC) based on the expression of heterogeneous nuclear ribonucleoprotein C (HNRNPC), YTH domain-binding protein 2 (YTHDF2), and methyltransferase 14 (METTL14). Methods: We obtained the RNA sequence and clinical information of OSCC patients from The Cancer Genome Atlas database. An optical method was established by the least absolute shrinkage and selection operator Cox regression algorithm, which was used to calculate the risk score of every sample. In addition, all samples (n=239) were classified into high-risk (n=119) and low-risk (n=120) groups, and the overall survival (OS) time and clinical characteristics were compared between groups. Moreover, bioinformatics analysis was carried out. Gene set enrichment analysis was performed to investigate the signaling pathways of HNRNPC, YTHDF2, and METTL14. Results: The two groups showed significantly different OS time, tumor grades, tumor stages, and pathologic T stages (P<0.05). The receiver operating characteristic analysis identified that our method was effective and it was more accurate than use of age, gender, tumor grade, tumor stage, pathologic T stage, and pathologic N stage in OSCC prognostic prediction. Gene set enrichment analysis revealed that HNRNPC, YTHDF2, and METTL14 were mainly associated with ubiquitin-mediated proteolysis, cell cycle, RNA degradation, and spliceosome signaling pathways. Conclusion: The method based on the expression of HNRNPC, YTHDF2, and METTL14 can predict the prognosis of patients with OSCC independently, and its prognostic value is better than that of clinicopathological characteristic indicators.
MeSH term(s)	Carcinoma, Squamous Cell/genetics ; Carcinoma, Squamous Cell/pathology ; Head and Neck Neoplasms ; Humans ; Methylation ; Methyltransferases/genetics ; Methyltransferases/metabolism ; Mouth Neoplasms/genetics ; Mouth Neoplasms/pathology ; Prognosis ; Squamous Cell Carcinoma of Head and Neck ; Transcription Factors/metabolism
Chemical Substances	Transcription Factors ; Methyltransferases (EC 2.1.1.-)
Language	English
Publishing date	2022-08-13
Publishing country	China
Document type	Journal Article
ZDB-ID	2931065-9
ISSN	2523-899X ; 2096-5230
ISSN (online)	2523-899X
ISSN	2096-5230
DOI	10.1007/s11596-022-2611-7
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Article ; Online: Genetically Predicted Higher Educational Attainment Decreases the Risk of COVID-19 Susceptibility and Severity

Zhongyu Jian / Menghua Wang / Xi Jin / Xin Wei

Frontiers in Public Health, Vol

A Mendelian Randomization Study

2021 Volume 9

Abstract: Background: Prior observational studies indicated that lower educational attainment (EA) is associated with higher COVID-19 risk, while these findings were vulnerable to bias from confounding factors. We aimed to clarify the causal effect of EA on COVID- ... ...

Abstract	Background: Prior observational studies indicated that lower educational attainment (EA) is associated with higher COVID-19 risk, while these findings were vulnerable to bias from confounding factors. We aimed to clarify the causal effect of EA on COVID-19 susceptibility, hospitalization, and severity using Mendelian randomization (MR).Methods: We identified genetic instruments for EA from a large genome-wide association study (GWAS) (n = 1,131,881). Summary statistics for COVID-19 susceptibility (112,612 cases and 2,474,079 controls), hospitalization (24,274 cases and 2,061,529 controls), and severity (8,779 cases and 1,001,875 controls) were obtained from the COVID-19 Host Genetics Initiative. We used the single-variable MR (SVMR) and the multivariable MR (MVMR) controlling intelligence, income, body mass index, vigorous physical activity, sedentary behavior, smoking, and alcohol consumption to estimate the total and direct effects of EA on COVID-19 outcomes. Inverse variance weighted was the primary analysis method. All the statistical analyses were performed using R software.Results: Results from the SVMR showed that genetically predicted higher EA was correlated with a lower risk of COVID-19 susceptibility [odds ratio (OR) 0.86, 95% CI 0.84–0.89], hospitalization (OR 0.67, 95% CI 0.62–0.73), and severity (OR 0.67, 95% CI 0.58–0.79). EA still maintained its effects in most of the MVMR.Conclusion: Educational attainment is a predictor for susceptibility, hospitalization, and severity of COVID-19 disease. Population with lower EA should be provided with a higher prioritization to public health resources to decrease the morbidity and mortality of COVID-19.
Keywords	COVID-19 ; Mendelian randomization ; susceptibility ; severity ; educational attainment ; Public aspects of medicine ; RA1-1270
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protocol for Drosophila sleep deprivation using single-chip board

Xi Jin / Pengyu Gu / Junhai Han

STAR Protocols, Vol 2, Iss 4, Pp 100827- (2021)

2021

Abstract: Summary: Sleep behavior is characterized by long-term quiescence and increased arousal threshold, and it is homeostatically regulated. The sleep rebound after deprivation is utilized to verify the abilities to maintain homeostasis. This protocol shows ... ...

Abstract	Summary: Sleep behavior is characterized by long-term quiescence and increased arousal threshold, and it is homeostatically regulated. The sleep rebound after deprivation is utilized to verify the abilities to maintain homeostasis. This protocol shows how to build a programmed mechanic oscillation system and detailed procedures to conduct sleep deprivation in Drosophila. This deprivation system is featured by its programming flexibility. The knowledge of electronic circuits and a certain level of programming are both required to fulfill this protocol.For complete details on the use and execution of this protocol, please refer to Jin et al. (2021).
Keywords	Bioinformatics ; Biophysics ; Model Organisms ; Neuroscience ; Cognitive Neuroscience ; Behavior ; Science (General) ; Q1-390
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A new formulation of dezocine, Cyc-dezocine, reduces oxycodone self-administration in female and male rats.

Schmidt, Heath D / Zhang, Yafang / Xi, Jin / Zanni, Giulia / Liu, Renyu / Barr, Gordon A

Neuroscience letters

2023 Volume 815, Page(s) 137479

Abstract: Dezocine is a partial mu opioid receptor agonist previously used as an analgesic for perioperative acute pain in the US and is now the most used perioperative analgesic in China. In general, dezocine is well-tolerated, with relatively minimal risk of ... ...

Abstract	Dezocine is a partial mu opioid receptor agonist previously used as an analgesic for perioperative acute pain in the US and is now the most used perioperative analgesic in China. In general, dezocine is well-tolerated, with relatively minimal risk of fatal respiratory depression. To our knowledge, there are no reports of dezocine addiction, which suggests that the abuse liability of dezocine is low. The overarching goal of this study was to determine the efficacy of a novel formulation of dezocine (Cyc-dezocine), developed for intraperitoneal or intranasal administration, to reduce voluntary opioid taking in rats. One cohort of male rats self-administered intravenous oxycodone on a fixed-ratio 5 schedule of reinforcement. Once oxycodone taking stabilized, rats were pretreated with systemic injections of vehicle or Cyc-dezocine. Cyc-dezocine dose-dependently reduced intravenous oxycodone self-administration. A second cohort of male and female rats self-administered oral oxycodone from drinking water. Once oxycodone taking stabilized, rats were pretreated with intra-nasal Cyc-dezocine. Consistent with the effects of i.p. Cyc-dezocine in our intravenous oxycodone studies, intra-nasal Cyc-dezocine attenuated oral oxycodone self-administration. Together, these findings support the need for further studies investigating the therapeutic potential of Cyc-dezocine for treating opioid use disorder.
MeSH term(s)	Humans ; Rats ; Male ; Female ; Animals ; Oxycodone/pharmacology ; Oxycodone/therapeutic use ; Analgesics, Opioid ; Tetrahydronaphthalenes/pharmacology ; Analgesics/pharmacology ; Dose-Response Relationship, Drug ; Receptors, Opioid, mu/agonists
Chemical Substances	Oxycodone (CD35PMG570) ; dezocine (VHX8K5SV4X) ; Analgesics, Opioid ; Tetrahydronaphthalenes ; Analgesics ; Receptors, Opioid, mu
Language	English
Publishing date	2023-09-14
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	194929-9
ISSN	1872-7972 ; 0304-3940
ISSN (online)	1872-7972
ISSN	0304-3940
DOI	10.1016/j.neulet.2023.137479
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Article ; Online: Rearrangement of 2-(benzofuran-2-yl)-3-phenylpyridines

Xi, Jin / Wang, Ding / Hu, Jinxia / Shen, Huan / Wang, Tao / Zhang, Zunting

Organic & biomolecular chemistry

2023 Volume 21, Issue 35, Page(s) 7188–7193

Abstract: By photoinduced 6π-electrocyclization of 2-(benzofuran-2-yl)-3-phenylpyridine derivatives 1, a method for the synthesis ... ...

Abstract	By photoinduced 6π-electrocyclization of 2-(benzofuran-2-yl)-3-phenylpyridine derivatives 1, a method for the synthesis of
Language	English
Publishing date	2023-09-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2097583-1
ISSN	1477-0539 ; 1477-0520
ISSN (online)	1477-0539
ISSN	1477-0520
DOI	10.1039/d3ob00883e
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Article ; Online: Evaluation of different rat models intrauterine adhesion models and improvement of the technique for their establishment.

Xi, Jin / Pan, Yan / Jin, Chunchun / Liu, Jingyu / Cheng, Jie / Xu, Bin

Experimental animals

2023 Volume 72, Issue 2, Page(s) 274–284

Abstract: Intrauterine adhesion (IUA), a leading cause of uterine infertility, is characterized by endometrial fibrosis. Implementing an appropriate animal model is essential for the research on the mechanisms of IUA. In the present study, we established and ... ...

Abstract	Intrauterine adhesion (IUA), a leading cause of uterine infertility, is characterized by endometrial fibrosis. Implementing an appropriate animal model is essential for the research on the mechanisms of IUA. In the present study, we established and evaluated different intrauterine adhesion modeling procedures in rats to provide a reference for researchers. Rat IUA models were established by mechanical injury, 95% ethanol injection, and dual (mechanical injury with infection) injury. After two estrus cycles, the female rats were mated with sexually mature male rats, and uterine tissues were obtained on the 5th day of pregnancy. Hematoxylin and eosin staining and immunohistochemical detection of cytokeratin 19 and vimentin were performed to assess the morphology of the endometrium. Masson's trichrome staining and the expression of transforming growth factor-β1 and collagen I were used to assess the endometrium fibrosis. The expression of integrin avβ3, leukemia inhibitory factor, and homeobox gene A10 in the rat endometrium was used to evaluate the endometrial receptivity. In addition, the efficiency of embryo implantation was examined in the uterus on the 8th day of pregnancy. Our study found that mechanical injury caused by a curette can be completely repaired after two estrus cycles. However, dual injury and 95% ethanol injection can be used to establish an IUA rat model, and the dual injury is closer to the clinicpathological characteristics of IUA.
MeSH term(s)	Male ; Pregnancy ; Humans ; Rats ; Female ; Animals ; Uterine Diseases/metabolism ; Uterine Diseases/pathology ; Endometrium/injuries ; Endometrium/metabolism ; Endometrium/pathology ; Uterus ; Tissue Adhesions/genetics ; Tissue Adhesions/metabolism ; Tissue Adhesions/pathology ; Disease Models, Animal
Language	English
Publishing date	2023-01-16
Publishing country	Japan
Document type	Journal Article
ZDB-ID	2088228-2
ISSN	1881-7122 ; 1341-1357 ; 0007-5124
ISSN (online)	1881-7122
ISSN	1341-1357 ; 0007-5124
DOI	10.1538/expanim.22-0153
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