Linkage Relationships Research Articles

The Additional Linkage Relationships In The Poetry Of Hazem Rushak Al-Tamimi (Linguistic study)

Semantic relationships have an active role in textual study, as they are an essential tool through which the text is constructed, this is done through the sequence of sentences of saying, and these relationships lead to the growth and continuation of the subject of the text, and then linking speech, which in turn achieves textual harmony. The aim of this study is to highlight the importance of these relationships and to elaborate on them, this research dealt with two additional linkage relationships: (equivalent additional relationship), and (different additional relationship), with a breakdown of each of them, and then the inclusion of poetic applications from the texts of the poet Hazem Rushak Al-Tamimi, showing the extent of the harmony, plot and coherence of these texts.

Land price dynamics in response to high-speed rail network characteristics: An empirical analysis

High-speed railway (HSR) has become the backbone of China's rapid and comprehensive urban transportation network system. However, the impact of the network characteristics of HSR on urban land prices has not been studied in sufficient detail. To fill this gap, this study focuses on both the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration and explores the linkage relationship between the HSR network and urban land prices. For this analysis, social network analysis and fixed effects model are used. The results show that the overall HSR network density of these two urban agglomerations still has room for improvement. Affected by regional proximity and the direction of HSR lines, HSR presents a spatial pattern of a multi-center–multi-tier network. The “siphon effect” is the main effect of the HSR network characteristics on land prices. However, the weakening degree of spatial spillover effect on siphon effect of the HSR network in the Yangtze River Delta urban agglomeration is stronger than that in the Beijing-Tianjin-Hebei urban agglomeration. Local governments should fully utilize the spatial effects of HSR and enhance the level of coordinated development among urban agglomerations.

Ecosystem Services’ Supply–Demand Assessment and Ecological Management Zoning in Northwest China: A Perspective of the Water–Food–Ecology Nexus

The coordinated development of the water–food–ecology (WFE) nexus is a practical issue that has to be addressed urgently for northwest China’s (WTL) sustainable development. Optimizing the linkage relationship and accomplishing the rational distribution of resources from the perspective of the supply and demand for ecosystem services (ESSD) are imperative. Thus, in this study, a numerical indicator system for ESSD from the perspective of the WFE nexus was constructed with the incorporation of the water and carbon footprint. Based on this premise, the ecological management zoning method was enhanced by integrating supply and demand risks, and optimization suggestions were proposed for various zones. The results showed that (1) carbon sequestration (CS), food production (FP), and water yield (WY) supply and demand significantly increased between 2000 and 2021. High ESSDs were concentrated in the west side of northwest China. Maize, wheat, cotton, vegetables, and garden fruits had a higher demand for ecosystem services (ESs). (2) The three ESSDs were bound in a synergistic relationship. The synergy between supply exhibited significant spatial heterogeneity, while the synergies between demand showed similar distribution patterns. (3) Regarding quantity matching, the supply for FP and CS surpassed demand, while the WY supply could not meet the demand. The three ESs’ supply and demand deficits rose. Ecological supply–demand ratio (ESDR) regional differentiation for the three ESs was apparent. Regarding spatial matching, FP and CS concentrated on low supply–low demand, while WY focused on high supply–high demand. FP risk was concentrated in Qaidam Basin, whereas WY risk was mostly in Hexi inland river basin (HX), the Yellow River Basin area (HH), and both sides of the “Qice line”. (4) The ecological management zones were formed by integrating WTL’s three dominant weak functional zones, four categorized strategy regions, and four governance models. This study can serve as a scientific benchmark for regional ecological management, which is significant in ensuring northwest China’s water, food, and ecological safety.

GCphase: an SNP phasing method using a graph partition and error correction algorithm

BackgroundThe utilization of long reads for single nucleotide polymorphism (SNP) phasing has become popular, providing substantial support for research on human diseases and genetic studies in animals and plants. However, due to the complexity of the linkage relationships between SNP loci and sequencing errors in the reads, the recent methods still cannot yield satisfactory results.ResultsIn this study, we present a graph-based algorithm, GCphase, which utilizes the minimum cut algorithm to perform phasing. First, based on alignment between long reads and the reference genome, GCphase filters out ambiguous SNP sites and useless read information. Second, GCphase constructs a graph in which a vertex represents alleles of an SNP locus and each edge represents the presence of read support; moreover, GCphase adopts a graph minimum-cut algorithm to phase the SNPs. Next, GCpahse uses two error correction steps to refine the phasing results obtained from the previous step, effectively reducing the error rate. Finally, GCphase obtains the phase block. GCphase was compared to three other methods, WhatsHap, HapCUT2, and LongPhase, on the Nanopore and PacBio long-read datasets. The code is available from https://github.com/baimawjy/GCphase.ConclusionsExperimental results show that GCphase under different sequencing depths of different data has the least number of switch errors and the highest accuracy compared with other methods.

An evaluation of a Stenotrophomonas maltophilia outbreak due to commercial arterial blood gas collection kit

BackgroundStenotrophomonas maltophilia is a gram-negative bacterium that can cause hospital infections and outbreaks within hospitals. This study aimed to evaluate an outbreak of Stenotrophomonas maltophilia, caused by ready-to-use commercial syringes containing liquid lithium and heparin for arterial blood gas collection in a university hospital.MethodsUpon detecting an increase in Stenotrophomonas maltophilia growth in blood cultures between 15.09.2021 and 19.11.2021, an outbreak analysis and a case-control study (52 patients for the case group, 56 patients for the control group) were performed considering risk factors for bacteremia. Samples from possible foci for bacteremia were also cultured. Growing bacteria were identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The genetic linkage and clonal relationship isolates were investigated with pulsed-field gel electrophoresis (PFGE) in the reference laboratory.ResultsIn the case-control study, the odds ratio for the central venous catheter [3.38 (95% confidence interval [CI]: 1.444, 8.705 ; p = 0.006)], for surgery [3.387 (95% confidence interval [CI]: 1.370, 8.373 ; p = 0.008)] and for arterial blood gas collection history [18.584 (95% confidence interval [CI]:4.086, 84.197; p < 0.001)] were identified as significant risk factors. Stenotrophomonas maltophilia growth was found in ready-to-use commercial syringes used for arterial blood gas collection. Molecular analysis showed that the growths in the samples taken from commercial syringes and the growths from blood cultures were the same. It was decided that the epidemic occurred because the method for sterilization of heparinized liquid preparations were not suitable. After discontinuing the use of the kits with this lot number, the outbreak was brought under control.ConclusionsAccording to our results, disposable or sterile medical equipment should be included as a risk factor in outbreak analyses. The method by which injectors containing liquids, such as heparin, are sterilized should be reviewed. Our study also revealed the importance of the cooperation of the infection control team with the microbiology laboratory.

An AFD-based adaptive impulse response model of time series

In this paper, an adaptive impulse response model of time series is proposed based on adaptive Fourier decomposition (AFD), which characterizes the data-driven impulse response model with “energy-resolution” and is applied to the effect analysis of shocks on time series. First, according to the observed time series, the frequency points of impulse response function are estimated, and the approximate expression of impulse response function in frequency domain is given. Second, AFD is carried out on the approximate impulse response function level by level, and the poles and coefficients of basis function of each level are obtained adaptively by using the criterion of energy maximization. Third, when the goodness of fit is greater than a certain threshold, all decomposition levels are determined and the adaptive impulse response model is established. Each decomposition level depicts the energy-based resolution characteristics of the impulse response model. Based on gold price return and US dollar index return in daily frequency from 2017 to 2022, this paper carried out AFD-based effect analysis with energy-resolution year by year. Results show that the proposed AFD-based adaptive impulse response model in this paper can describe the persistence effect of shocks of gold price on the US dollar index, and further measure the plainness of linkage relationship between these two time series. Decomposing impulse response to each decomposition level reveals consistence, convergency, and sufficiency of the impulse response model. The AFD-based adaptive data-driven model provides a new way for the analysis of time series in time-frequency domain.

Research on quality assessment methods for cybersecurity knowledge graphs

In light of the continuous evolution of cyber threats and the escalating frequency of cyberattacks, cybersecurity knowledge graphs have emerged as vital tools for comprehending and countering cyber threats. Nonetheless, the quality of these knowledge graphs is of paramount significance for their effectiveness in cybersecurity applications. This research proposes a Parameter-Efficient Knowledge Graphs Quality Assessment model (PEKGQA), aimed at evaluating the quality of cybersecurity knowledge graphs. We partition the knowledge graph into different communities and select reserved entities based on degree centrality within each community, and we only learn the embeddings of these reserved entities, significantly reducing the model's parameter size. Moreover, we leverage linkage relationships information and K-nearest neighbor information to encode all entities. Then we employ graph attention networks for the joint iterative update of entity and relation embeddings. This approach not only reduces the parameter count but also achieves excellent quality assessment results. Experimental outcomes demonstrate that the PEKGQA model-based approach significantly outperforms traditional methods in evaluation metrics such as F1 value and accuracy, providing an efficient and accurate solution for quality assessment in the field of cybersecurity knowledge graphs.

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

Remote Sensing Ecological Quality and Its Response to the Rocky Desertification in the World Heritage Karst Sites

Clarifying the spatial and temporal evolution characteristics of the ecological environment quality of World Heritage Karst Sites (WHKSs) and its response to different rocky desertification grades at spatial scales is crucial for the monitoring and protection of WHKSs as well as the implementation of ecological and environmental policies in karst regions. The ecological evaluation model of Remote Sensing Ecological Index (RSEI) was used to evaluate the ecological environment of Libo–Huanjiang World Heritage Karst site and Shibing World Heritage Karst site, and then the spatial autocorrelation and geo-detection model was used to further analyze the ecological environment, and final spatial overlay of RSEI and rocky desertification by year to analyze the linkage relationship between RSEI and rocky desertification. The results showed that (1) in the three-phase ecological environmental quality evaluation of the two heritage sites, the RSEI in 2010, 2016, and 2022 reached 0.60, 0.67, and 0.64 for the Libo–Huanjiang heritage site, and RSEI in 2010, 2016, and 2022 for the Shibing heritage site reached 0.60, 0.74, and 0.70, respectively; (2) the RSEI of both heritage sites show a gradually increasing positive spatial correlation, and has significant spatial aggregation characteristics, with both heritage sites dominated by the high-high and low-low spatial aggregation categories; (3) both heritage sites have the highest degree of explanation of changes in ecological quality by the NDBSI factor, indicating that this factor plays a key role in changes in ecological quality at heritage sites; (4) the response of the RSEI mean value of Libo–Huanjiang in each grade of rocky desertification area is, from high to low, no rocky desertification, non-karst, potential rocky desertification, mild rocky desertification, moderate rocky desertification, intensive rocky desertification, and extreme intensity rocky desertification, and the response of the RSEI mean value of Shibing is, from high to low, non-karst, no rocky desertification, potential rocky desertification, mild rocky desertification, and moderate rocky desertification. The spatial superposition analysis of the RSEI index and rocky desertification index can quantitatively study the changing status of the ecological environment in different rocky desertification areas, and the results of the study can provide theoretical references for the environmental monitoring and the prevention and control of rocky desertification in the karst areas and WHKSs.

The Nexus of Tourism and Economic Growth in Botswana During the Period of 1995 – 2019

This study examined the casual linkages between tourism and economic growth in the Republic of Botswana from 1995 to 2019. This was inspired by the highly claimed potential of tourism on GDP, against a background where literature cautions that causal linkage relationships are not always direct and obvious, but rather country specific and most likely to change over time. Co-integration and Granger causality were used to assess the causal nexus between the variables. The findings supported a unidirectional causal relationship between GDP and tourism (GDP → TOR) as well as a long-run co-integration between tourism and economic growth. The study findings imply that the country’s rich natural resources may be insufficient to propel tourism growth and drive GDP in the absence of a supportive economic environment. Therefore, the study suggests that efforts to support policy and investments should concentrate more on overall economic development than at tourism per se. This would create an environment conducive for attracting and retaining visitors to the country, and thus boost tourism.

Strategies for improving food security under the impact of external grain price fluctuations: evidence from China

ABSTRACT The purpose of this study is to explore strategies for improving food security under the impact of external food price fluctuations. Using impulse response function, complex network analysis, and other methods, we studied the linkage between CBOT prices of soybeans, rice, wheat, corn, and Chinese futures prices from 2009 to 2022. This article analyzes the prominent price linkage relationships, network evolution characteristics, and development trends. The results show that CBOT soybean and China soybean futures prices, CBOT wheat and China soybean futures prices, and CBOT corn and China wheat futures prices are three prominent price linkage relationships. The three pairs of price linkage relationships are constantly changing over time and show a gradual weakening trend. The price of domestic soybean futures and CBOT soybean futures have a strong association. The sudden food crisis will strengthen the connection between international and Chinese grain futures prices. The above results demonstrate the importance of strengthening the independence of China’s futures markets. Based on the above analysis results, the paper put forward policy suggestions such as improving soybean yield levels, utilizing the futures market for risk management, and taking measures to respond to possible changes in Chinese wheat prices based on international corn futures prices.

Blockchain Data Mining With Graph Learning: A Survey.

Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

The Linkage Relationship between Knowledge Creation and Business Sustainability in Cross-Sectional Banking Services: The Key Mediating Role of Technological Innovation Capability

The Linkage Relationship between Knowledge Creation and Business Sustainability in Cross-Sectional Banking Services: The Key Mediating Role of Technological Innovation Capability

Research on the Impact of Digital Currency on the Operation Mechanism of Traditional Financial Markets and Countermeasures

Abstract Both digital currencies and traditional financial markets are affected by inflation, interest rates, money supply, etc. In terms of price volatility factors, there are cross-asset linkages and risk transfer problems. In this paper, we use the DCCGARCH model to calculate the time-varying nonlinear correlations between different financial markets during different periods. Firstly, we construct a GARCH model for univariate return series and derive standardized residual estimates to determine the conditional insured value and spillover risk that financial markets encounter when the risk materializes. The linkage and shock relationships of BTC with the stock and spot gold markets over ten years are analyzed, as well as the impact of unanticipated events on financial markets. It is found that the short-term impact of digital currency price volatility on the stock market is much larger than the short-term impact of stocks on the gold market and that risks in the digital currency market may be rapidly transmitted to the stock market in the short term. Bitcoin and stocks have a dynamic correlation coefficient that fluctuates between -0.075 and 0.085, with little correlation. Stocks and Bitcoin have a positive correlation of 0.021, meaning that when the price of Bitcoin rises, it can lead to a rise in the price of stocks to some extent. The dynamic correlation coefficient between bitcoin and gold, on the other hand, fluctuates in the range of -0.275 to 0.175, which may produce large fluctuations. During the Cyprus crisis, gold and bitcoin demonstrated a significant investment transfer effect, and there was a contagion and linkage between market risk, the UK’s exit from the European Union, and the new crown epidemic. This paper provides a useful exploration of cross-market financial risk regulation and professional investor risk control.

What kind of support can promote academic entrepreneurial intention: An analysis of the Qualitative Comparative Analysis configuration effect of multiple support

AbstractThrough one‐way analysis of variance (ANOVA), it is found that monthly salary and academic entrepreneurial intention have a “U‐shaped” relationship. In qualitative comparative analysis (QCA), single‐factor necessity analysis found that self‐emotional support and family emotional support constitute the necessary conditions. Through multifactor configuration analysis, four paths are summarized as follows: “internal drive type,” “resource utilization type,” “relationship infection type,” and “opportunity capture type.” Through cross‐configuration comparative analysis, it is found that different support subjects have a substitution effect, the strong linkage relationship between self and family becomes a necessary condition, and emotional support is better than instrumental support.

Breeding design in wheat by combining the QTL information in a GWAS panel with a general genetic map and computer simulation

A large amount of genome-wide association study (GWAS) panels together with quantitative-trait locus (QTL) information associated with breeding-targeted traits have been described in wheat (Triticum aestivum L.). However, the application of mapping results from a GWAS panel to conventional wheat breeding remains a challenge. In this study, we first report a general genetic map which was constructed from 44 published linkage maps. It permits the estimation of genetic distances between any two genetic loci with physical map positions, thereby unifying the linkage relationships between QTL, genes, and genomic markers from multiple genetic populations. Second, we describe QTL mapping in a wheat GWAS panel of 688 accessions, identifying 77 QTL associated with 12 yield and grain-quality traits. Because these QTL have known physical map positions, they could be mapped onto the general map. Finally, we present a design approach to wheat breeding by using known QTL information and computer simulation. Potential crosses between parents in the GWAS panel may be evaluated by the relative frequency of the target genotype, trait correlations in simulated progeny populations, and genetic gain of selected progenies. It is possible to simultaneously improve yield and grain quality by suitable parental selection, progeny population size, and progeny selection scheme. Applying the design approach will allow identifying the most promising crosses and selection schemes in advance of the field experiment, increasing predictability and efficiency in wheat breeding.

Public participation and outgoing audit of natural resources: Evidence from tripartite evolutionary game in China

Public participation is essential to the success of ecological civilization. Whether public participation can effectively play a role in the outgoing audit of natural resources (OANR) is an important issue that remains to be explored. This paper uses the tripartite evolutionary game to explore the mechanism of the audit subjects, the leading cadres, and the public in the OANR. The research finds that there is a two-way linkage relationship between the audit subjects and the leading cadres. The audit subjects and the leading cadres affect the behavior strategies of the public in the indirect way and direct way, respectively. However, the public lacks the path to directly affect the other two subjects. The tripartite ideal audit model of “the audit subjects conduct due diligence audits, leading cadres perform duties, the public participate” cannot be realized. The external effect of the public's strategic choice is not enough to make the profit or loss of leading cadres change structurally and then change their behaviors. This paper demonstrates the reasons why the public cannot effectively participate in the OANR at the current stage from three aspects, which are the interpretation of the equations for replication dynamics, the particularity of the audit system, and the effectiveness of public participation. Three suggestions are put forward which are encouraging citizens' indirect participation in the OANR, disclosing information about the OANR, and improving citizens' awareness of the OANR. This paper has important guiding significance for other developing countries to promote public participation in natural resource auditing.

Analyzing Social Media Data Using Sentiment Mining and Bigram Analysis for the Recommendation of YouTube Videos

In this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming, which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts, and sentiment.

Analysis of the Linkage Relationship between the Mainland Stock Market and the Taiwan Stock Market during the Post -epidemic Period Based on the VAR

This article use The Shanghai Composite Index, Shenzhen component Index and the Taiwan weighted stock index from January 4, 2021 to January 20 , 2023 as the sample. Establish an analysis of the connected relationship between mainland stock markets and Taiwan stock markets during the post-epidemic period. Conduct empirical analysis through stable test, Grandie Cause and effect testing, pulse response analysis, variance decomposition and other methods.The empirical results show that Taiwan's weighted stock price index is the reason for the Shanghai Composite Index and the Shenzhen Stock Exchange. The mainland stock market has a significant guiding role in the Taiwan stock market in the short term. Compared with the Shanghai Composite Index, the Shenzhen Stock Exchange Index has a closer relationship with the Taiwan's weighted stock price index.