Structural Characteristics and Influencing Factors of Carbon Emission Spatial Association Network:A Case Study of Yangtze River Delta City Cluster,China

City cluster is an effective platform for encouraging regionally coordinated development.Coordinated reduction of carbon emissions within city cluster via the spatial association network between cities can help coordinate the regional carbon emission management,realize sustainable development,and assist China in achieving the carbon peaking and carbon neutrality goals.This paper applies the improved gravity model and social network analysis(SNA)to the study of spatial correlation of carbon emissions in city clusters and analyzes the structural characteristics of the spatial correlation network of carbon emissions in the Yangtze River Delta(YRD)city cluster in China and its influencing factors.The results demonstrate that:1)the spatial association of carbon emissions in the YRD city cluster exhibits a typical and complex multi-threaded network structure.The network association number and density show an upward trend,indicating closer spatial association between cities,but their values remain generally low.Meanwhile,the network hierarchy and network efficiency show a downward trend but remain high.2)The spatial association network of carbon emissions in the YRD city cluster shows an obvious‘core-edge’distribution pattern.The network is centered around Shanghai,Suzhou and Wuxi,all of which play the role of‘bridges’,while cities such as Zhoushan,Ma'anshan,Tongling and other cities characterized by the remote location,single transportation mode or lower economic level are positioned at the edge of the network.3)Geographic proximity,varying levels of economic development,different industrial structures,degrees of urbanization,levels of technological innovation,energy intensities and environmental regulation are important influencing factors on the spatial association of within the YRD city cluster.Finally,policy implications are provided from four aspects:government macro-control and market mechanism guidance,structural characteristics of the‘core-edge’network,reconfiguration and optimization of the spatial layout of the YRD city cluster,and the application of advanced technologies.

An insight into the role of the association equations of states in gas hydrate modeling:a review

Encouraged by the wide spectrum of novel applications of gas hydrates,e.g.,energy recovery,gas separation,gas storage,gas transportation,water desalination,and hydrogen hydrate as a green energy resource,as well as CO2 capturing,many scientists have focused their attention on investigating this important phenomenon.Of course,from an engineering viewpoint,the mathematical modeling of gas hydrates is of paramount importance,as anticipation of gas hydrate stability conditions is effective in the design and control of industrial processes.Overall,the thermodynamic modeling of gas hydrate can be tackled as an equilibration of three phases,i.e.,liquid,gas,and solid hydrate.The inseparable component in all hydrate systems,water,is highly polar and non-ideal,necessitating the use of more advanced equation of states(EoSs) that take into account more intermolecular forces for thermodynamic modeling of these systems.Motivated by the ever-increasing number of publications on this topic,this study aims to review the application of associating EoSs for the thermodynamic modeling of gas hydrates.Three most important hydrate-based models available in the literature including the van der Waals-Platteeuw(vdW-P) model,Chen-Guo model,and Klauda-Sandler model coupled with and SAFT EoSs were investigated and compared with cubic EoSs.It was concluded that the CPA and SAFT EoSs gave very accurate results for hydrate systems as they take into account the association interactions,which are very crucial in gas hydrate systems in which water,methanol,glycols,and other types of associating compounds are available.Moreover,it was concluded that the CPA EoS is easier to use than the SAFT-type EoSs and our suggestion for the gas hydrate systems is the CPA EoS.

Management of Associations in the IRBAC 2000 Model

Secure interaction and interoperability between two or more administrative domains is a major concern. The IRBAC 2000 model accomplishes secure interaction and interoperability by flexibly dynamic inter-domain role translations. Associations are the key element of the IRBAC 2000 model, which have a great impact on security and efficiency of dynamic role translations. Therefore, it is a crucial problem how to manage the associations in the IRBAC 2000 model. There are two cases under which some matters will emerge. One is where conflicting associations may result in a security hazard. Another is where redundant associations may reduce the efficiency of dynamic role translations and increase the difficulty of management of associations. The formal definitions on conflicting associations and redundant associations are given, and the methods are discusses to judge whether there are conflicting associations or redundant associations in IRBAC 2000 model. The protective mechanism is presented, which utilizes prerequisite conditions to prevent conflicting or redundant associations from appearing in IRBAC 2000 model.

Construction and validation of a severity prediction model for metabolic associated fatty liver disease

Objective:To analyze the independent risk factors for the occurrence of moderate-to-severe metabolic-associated fatty liver disease(MAFLD),to construct a prediction model for moderate-to-severe MAFLD,and to verify the validity of the model.Methods:In the first part,278 medical examiners who were diagnosed with MAFLD in Medical Examination Center at the Second Affiliated Hospital of Hainan University from January to May 2022 were taken as the study subjects(training set),and they were divided into mild MAFLD group(200)and moderate-severe MAFLD group(78)based on ultrasound results.Demographic data and laboratory indexes were collected,and risk factors were screened by univariate and multifactor analysis.In the second part,a dichotomous logistic regression equation was used to construct a prediction model for moderate-to-severe MAFLD,and the model was visualized in a line graph.In the third part,the MAFLD population(200 people in the external validation set)from our physical examination center from November to December 2022 was collected as the moderate-to-severe MAFLD prediction model,and the risk factors in both groups were compared.The receiver operating characteristic(ROC)curves,calibration curves,and clinical applicability of the model were plotted to represent model discrimination for internal and external validation.Results:The risk factors of moderate-to-severe MAFLD were fasting glucose(FPG),blood uric acid(UA),triglycerides(TG),triglyceride glucose index(TyG),total cholesterol(CHOL),and high-density lipoprotein(HDL-C).UA[OR=1.021,95%CI(1.015,1.027),P<0.001]and FPG[OR=1.575,95%CI(1.158,2.143),P=0.004]were independent risk factors for people with moderate to severe MAFLD.The visualized line graph model showed that UA was the factor contributing more to the risk of moderate to severe MAFLD in this model.The ROC curves showed AUC values of 0.8701,0.8686 and 0.7991 for the training set,internal validation set and external validation set,respectively.The curves almost coincided with the reference line after calibration of the model calibration degree with P>0.05 in Hosmer-Lemeshow test.The decision curve analysis(DCA)plotted by the clinical applicability of the model was higher than the two extreme curves,predicting that patients with moderate to severe MAFLD would benefit from the prediction model.Conclusion:The prediction model constructed by combining FPG with UA has higher accuracy and better clinical applicability,and can be used for clinical diagnosis.

Correlation of ^1H NMR Chemical Shift for Aqueous Solutions by Statistical Associating Fluid Theory Association Model

^1H NMR chemical shifts of binary aqueous mixtures of acylamide, alcohol, dimethyl sulphoxide (DMSO), and acetone are correlated by statistical associating fluid theory (SAFT) association model. The comparison between SAFT association model and Wilson equation shows that the former is better for dealing with aqueous solutions. Finally, the specialties of both models are discussed.

Asymptotic normality of error density estimator in stationary and explosive autoregressive models

In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.

Mining multilevel spatial association rules with cloud models

The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.

Decomposition of Independence Using the Logit Uniform Association Model and Equality of Concordance and Discordance for Two-Way Classifications

For two-way contingency tables with ordered categories, the present paper gives a theorem that the independence model holds if and only if the logit uniform association model holds and equality of concordance and discordance for all pairs of adjacent rows and all dichotomous collapsing of the columns holds. Using the theorem, we analyze the cross-classification of duodenal ulcer patients according to operation and dumping severity.

Homology modeling and functional annotation of bubaline pregnancy associated glycoprotein 2

Background： Pregnancy associated glycoproteins form a diverse family of glycoproteins that are variably expressed at different stages of gestation. They are probably involved in immunosuppression of the dam against the fetomaternal placentome. The presence of the products of binucleate cells in maternal circulation has also been correlated with placentogenesis and placental re-modeling. The exact structure and function of the gene product is unknown due to limitations on obtaining purified pregnancy associated glycoprotein preparations.Results： Our study describes an in silico derived 3D model for bubaline pregnancy associated glycoprotein 2. Structure-activity features of the protein were characterized, and functional studies predict bubaline pregnancy associated glycoprotein 2 as an inducible, extra-cellular, non-essential, N-glycosylated, aspartic pro-endopeptidase that is involved in down-regulation of complement pathway and immunity during pregnancy. The protein is also predicted to be involved in nutritional processes, and apoptotic processes underlying fetal morphogenesis and remodeling of feto-maternal tissues.Conclusion： The structural and functional annotation of buPAG2 shall allow the designing of mutants and inhibitors for dissection of the exact physiological role of the protein.

Mucosal-associated invariant T cells from induced pluripotent stem cells:A novel approach for modeling human diseases

Mice have frequently been used to model human diseases involving immune dysregulation such as autoimmune and inflammatory diseases.These models help elucidatethe mechanisms underlying the disease and in the development of novel therapies.However,if mice are deficient in certain cells and/or effectors associated with human diseases,how can their functions be investigated in this species?Mucosal-associated invariant T(MAIT)cells,a novel innate-like T cell family member,are a good example.MAIT cells are abundant in humans but scarce in laboratory mice.MAIT cells harbor an invariant T cell receptor and recognize nonpeptidic antigens vitamin B2metabolites from bacteria and yeasts.Recent studies have shown that MAIT cells play a pivotal role in human diseases such as bacterial infections and autoimmune and inflammatory diseases.MAIT cells possess granulysin,a human-specific effector molecule,but granulysin and its homologue are absent in mice.Furthermore,MAIT cells show poor proliferation in vitro.To overcome these problems and further our knowledge of MAIT cells,we have established a method to expand MAIT cells via induced pluripotent stem cells(iP SCs).In this review,we describe recent advances in the field of MAIT cell research and our approach for human disease modeling with iP SCderived MAIT cells.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

Determination of Residual Feed Intake and Its Associations with Single Nucleotide Polymorphism in Chickens

Marker assisted selection （MAS） for residual feed intake （RFI） is considered to be one of the powerful means to improve feed conversion efficiency, and therefore reduce production costs. To test the inner relationship among body compositions, growth traits and RFI, four models were proposed to assess the extensively explanatory variables accounting for partial variables in feed intake besides metabolic body weight and growth rate. As a result, the original model （Koch＇s model） had the lowest R2 （80.78%） and the highest Bayesian information criterion （1 323.3） value among the four models. Moreover, the effects on RFI caused by single nucleotide polymorphisms （SNPs） were assessed in this study. Twelve SNPs from 7 candidate genes were genotyped in 2 Chinese native strains, rs14743490 of RPLP2 gene showed suggestively significant association with initial body weight in both strains （P〈0.10）. rs15047274 of TAF15 was significantly associated with growth weight, final weight, and feed intake （P〈0.05） in N301 strain, in contrast, it was only suggestively significant associated with feed intake （P〈0.10） in N414 strain, rs15869967 was significantly associated with RFI in N414 strain but not in N301 strain. This study has identified potential genetic markers suitable for MAS in improving the above mentioned traits, but these associations need to be rectified in other larger populations in future.

Study on data association algorithm of multi-passive-sensor location system

Aiming at three-passive-sensor location system, a generalized 3-dimension （3-D） assignment model is constructed based on property information, and a multi-target programming model is proposed based on direction-finding and property fusion information. The multi-target programming model is transformed into a single target programming problem to resolve, and its data association result is compared with the results which are solved by using one kind of information only. Simulation experiments show the effectiveness of the multi-target programming algorithm with higher data association accuracy and less calculation.

Genetic diversity and population structure of Pisum sativum accessions for marker-trait association of lipid content

Field pea(Pisum sativum L.) is an important protein-rich pulse crop produced globally. Increasing the lipid content of Pisum seeds through conventional and contemporary molecular breeding tools may bring added value to the crop. However, knowledge about genetic diversity and lipid content in field pea is limited. An understanding of genetic diversity and population structure in diverse germplasm is important and a prerequisite for genetic dissection of complex characteristics and marker-trait associations. Fifty polymorphic microsatellite markers detecting a total of 207 alleles were used to obtain information on genetic diversity, population structure and marker-trait associations. Cluster analysis was performed using UPGMA to construct a dendrogram from a pairwise similarity matrix. Pea genotypes were divided into five major clusters. A model-based population structure analysis divided the pea accessions into four groups. Percentage lipid content in 35 diverse pea accessions was used to find potential associations with the SSR markers. Markers AD73, D21, and AA5 were significantly associated with lipid content using a mixed linear model(MLM) taking population structure(Q) and relative kinship(K) into account. The results of this preliminary study suggested that the population could be used for marker-trait association mapping studies.

A NEW DATA ASSOCIATION ALGORITHM USING PROBABILITY HYPOTHESIS DENSITY FILTER

Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper,we proposed a new data association algorithm using GMPHD filter,which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.

Modeling diseases of noncoding unstable repeat expansions using mutant pluripotent stem cells

Pathogenic mutations involving DNA repeat expansions are responsible for over 20 different neuronal and neuromuscular diseases. All result from expanded tracts of repetitive DNA sequences(mostly microsatellites) that become unstable beyond a critical length whentransmitted across generations. Nearly all are inherited as autosomal dominant conditions and are typically associated with anticipation. Pathologic unstable repeat expansions can be classified according to their length, repeat sequence, gene location and underlying pathologic mechanisms. This review summarizes the current contribution of mutant pluripotent stem cells(diseased human embryonic stem cells and patient-derived induced pluripotent stem cells) to the research of unstable repeat pathologies by focusing on particularly large unstable noncoding expansions. Among this class of disorders are Fragile X syndrome and Fragile X-associated tremor/ataxia syndrome, myotonic dystrophy type 1 and myotonic dystrophy type 2, Friedreich ataxia and C9 related amyotrophic lateral sclerosis and/or frontotemporal dementia, Facioscapulohumeral Muscular Dystrophy and potentially more. Common features that are typical to this subclass of conditions are RNA toxic gain-of-function, epigenetic loss-of-function, toxic repeat-associated non-ATG translation and somatic instability. For each mechanism we summarize the currently available stem cell based models, highlight how they contributed to better understanding of the related mechanism, and discuss how they may be utilized in future investigations.

A Clique-Based Approach to the Identification of Common Gene Association Sub-Networks

We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.

Group A streptococcal antigen exposed rat model to investigate neurobehavioral and cardiac complications associated with post-streptococcal autoimmune sequelae

Background:The neuropsychiatric disorders due to post-streptococcal autoimmune complications such as Sydenham's chorea(SC)are associated with acute rheumatic fever and rheumatic heart disease(ARF/RHD).An animal model that exhibits char-acteristics of both cardiac and neurobehavioral defects in ARF/RHD would be an important adjunct for future studies.Since age,gender,strain differences,and geno-types impact on the development of autoimmunity,we investigated the behavior of male and female Wistar and Lewis rat strains in two age cohorts(6 weeks and 12 weeks)under normal husbandry conditions and following exposure to group A streptococcus(GAS).Methods:Standard behavioral assessments were performed to determine the impair-ments in fine motor control(food manipulation test),gait and balance(beam walk-ing test),and obsessive-compulsive behavior(grooming and marble burying tests).Furthermore,electrocardiography,histology,and behavioral assessments were per-formed on male and female Lewis rats injected with GAS antigens.Results:For control Lewis rats there were no significant age and gender dependent differences in marble burying,food manipulation,beam walking and grooming be-haviors.In contrast significant age-dependent differences were observed in Wistar rats in all the behavioral tests except for food manipulation.Therefore,Lewis rats were selected for further experiments to determine the effect of GAS.After ex-posure to GAS,Lewis rats demonstrated neurobehavioral abnormalities and cardiac pathology akin to SC and ARF/RHD,respectively.Conclusion:We have characterised a new model that provides longitudinal stability of age-dependent behavior,to simultaneously investigate both neurobehavioral and cardiac abnormalities associated with post-streptococcal complications.

The Books Recommend Service System Based on Improved Algorithm for Mining Association Rules

The Apriori algorithm is a classical method of association rules mining.Based on analysis of this theory,the paper provides an improved Apriori algorithm.The paper puts foward with algorithm combines HASH table technique and reduction of candidate item sets to enhance the usage efficiency of resources as well as the individualized service of the data library.