Translation is the use of one language to another language;this is expressed by the contents of the re-express.For the English-Chinese translation,the attributive clause translation is the most important.However,the E...Translation is the use of one language to another language;this is expressed by the contents of the re-express.For the English-Chinese translation,the attributive clause translation is the most important.However,the English attributive clause is divided into restrictive and non-restrictive attributive clauses.In the translation process,it should be based on its structure and meaning.To take appropriate translation,get rid of restrictions on sentence structure so that the translation is equal to the original as much as possible.In this paper,the translation skills and samples have been used to further express the attributive clause to the readers.展开更多
The attributive clause plays a significant role in E-C translation.Due to the stylistic difference in Chinese and English(Chinese is paratactic while English is hypotactic),complex English attributive clauses cannot b...The attributive clause plays a significant role in E-C translation.Due to the stylistic difference in Chinese and English(Chinese is paratactic while English is hypotactic),complex English attributive clauses cannot be translated literally as awkwardness may occur.One possible approach to coping with the problem is the adoption of‘Division’in translating long English attributive clauses.This essay provides three steps in translating complex English attributive clauses into Chinese(clarifying meaning units,restructuring and polishing)and the accompanied techniques such as amplification and omission to make the translation more readable and natural to Chinese readers.展开更多
Attributive clauses in EST often consist of complex structures and deliver a multiplicity of meanings. It will be one of challenges facing the translators to transfer the attributive clauses in EST faithfully, complet...Attributive clauses in EST often consist of complex structures and deliver a multiplicity of meanings. It will be one of challenges facing the translators to transfer the attributive clauses in EST faithfully, completely and smoothly into Chinese so that the understanding and response of the target language readers to the information produced in the translation are almost the same as that of the original language readers to the information contained in the original attributive clauses in EST, thus achieving functional equivalence of the translation. Under the direction of Nida's functional equivalence theory, this paper is dedicated to discussing the translation techniques of EST attributive clauses, and hope it benefits the translation of EST in the future.展开更多
Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and...Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.展开更多
As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data i...As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data is sequentially collected and used for online statistical modeling.For modeling sequential data,we propose a sequential linear regression method that extracts essential information from historical data.This carefully selected information is then utilized to update a model according to a sequential estimation scheme.With this technique,the earlier data no longer needs to be stored,and the sequential updating is computationally efficient in speed and storage.A weighted strategy is introduced on the current model to determine the impact of data from different periods.When compared with estimation methods that use historical data,our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.展开更多
BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AI...BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.展开更多
Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the mul...Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the multifactorial nature and the deficiency of available disease-modifying treatments.The application of mesenchymal stem/stromal cells(MSCs)as therapeutic drugs has provided novel treatment options for diverse degenerative and chronic diseases including KOA.However,the complexity and specificity of the“live”cells have posed challenges for MSC-based drug development and the concomitant scale-up preparation from laboratory to industrialization.For instance,despite the considerable progress in ex vivo cell culture technology for fulfilling the robust development of drug conversion and clinical trials,yet significant challenges remain in obtaining regulatory approvals.Thus,there’s an urgent need for the research and development of MSC drugs for KOA.In this review,we provide alternative solution strategies for the preparation of MSC drugs on the basis of the principle of quality by design,including designing the cell production processes,quality control,and clinical applications.In detail,we mainly focus on the quality by design method for MSC manufacturing in standard cell-culturing factories for the treatment of KOA by using the Quality Target Product Profile as a starting point to determine potential critical quality attributes and to establish relationships between critical material attributes and critical process parameters.Collectively,this review aims to meet product performance and robust process design,and should help to reduce the gap between compliant products and the production of compliant good manufacturing practice.展开更多
Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level ...Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level simulation model based on comparative risk assessment to estimate population attributable fractions and avoidable liver cancer deaths. Risk factor prevalence data were obtained from national surveys and epidemiological estimates. Three intervention scenarios for each risk factor were projected:elimination(Scenario 1), ambitious reduction(Scenario 2), and manageable targets aligned with national/global goals(Scenario 3). The impact of secondary prevention through liver cancer screening at different coverage was evaluated.Results: Between 2021 and 2050, liver cancer deaths in China are projected to reach 9.44 million in males and4.29 million in females. Eliminating hepatitis B virus and hepatitis C virus could prevent 65.62%(57.47%-73.77%)and 28.47%(24.93%-32.00%) of liver cancer deaths, respectively. Achieving manageable targets in reducing the prevalence of smoking and alcohol drinking could prevent 6.57%(5.75%-7.38%) and 0.85%(0.75%-0.96%) of liver cancer deaths, with a more pronounced effect observed in males. Eliminating high body mass index(BMI)could avert 45,000 male and 25,000 female deaths annually by 2050, while diabetes elimination could prevent60,000 male and 21,000 female deaths. Secondary prevention through liver cancer screening with 80% coverage could reduce liver cancer deaths by 3.59%(3.14%-4.04%) for the total population. Combining all interventions under Scenario 1 could prevent up to 88.39%(76.65%-99.81%) of male and 77.80%(67.42%-87.88%) of female liver cancer deaths by 2050.Conclusions: Comprehensive risk factor control could prevent over 80% of liver cancer deaths in China by2050. Secondary prevention through screening may offer modest additional benefits. These findings provide strong quantitative support for targeted, evidence-based interventions and underscore the need for policy action to address key risk factors.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increase...This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.展开更多
Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to ...Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.展开更多
Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph aug...Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.展开更多
Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon reg...Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.展开更多
Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic varia...Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic variables.In this study,we analyze the proton structure function F_(2)employing the analytical solution of the Balitsky–Kovchegov equation,with a focus on the high Q^(2)regime and small x domains.Our results indicate that as Q^(2)increases,the slope parameterλ,which characterizes the growth rate of F_(2),exhibits a gradual decrease,approaching a limiting value ofλ≈0.41±0.01 for large Q^(2).We suggest that this behavior ofλmay be attributed to mechanisms such as gluon overlap and the suppression of phase space growth.To substantiate these conclusions,further high-precision electron–ion collision experiments are required,encompassing a broad range of Q^(2)and x.展开更多
Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological el...Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological element trends were analyzed using theβ-z-h three-parameter indication method.The Mann-Kendall,Pettitt,moving T,and Yamamoto methods were used to test the mutation point of hydrological elements.The Budyko framework was used to quantitatively assess the impacts of climate change and multiple human activities on runoff reduction.The results showed that(1):Precipitation(PRE),potential evapotranspiration(E0),and temperature(TEM)showed increasing trends;runoff in the Huangfuchuan,Gushanchuan,Kuye River,Tuwei River,Wuding River,Qingjian River,and Yanhe River catchments showed decreasing trends(HFC,GSC,KYR,TWR,WDR,QJR,YR);whereas runoff in the Jialu River(JLR)catchment showed a“V-shaped”trend from 1980 to2020.(2)Runoff was positively correlated with PRE and negatively correlated with E0and the subsurface index(n),with the elasticity coefficients of PRE,E0,and n showing an increasing trend in the change period.(3)Human activities were a key factor in runoff reduction,although the impact of different human activities showed spatial variations.This study provides a scientific foundation for achieving the sustainable development of water resources in mining areas.展开更多
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
Despite the availability of effective antibacterial drugs,the increasing prevalence of antibiotic-resistant bacteria is primarily attributed to their excessive and inappropriate utilization.Antimicrobial resistance(AM...Despite the availability of effective antibacterial drugs,the increasing prevalence of antibiotic-resistant bacteria is primarily attributed to their excessive and inappropriate utilization.Antimicrobial resistance(AMR)represents one of the most concerning global health and development threats[1].Thereby,the continuous escalation in AMR necessitates urgent advancements in novel antibacterial strategies.The MraY enzyme,which plays a pivotal role in synthesizing bacterial cell wall-composing polysaccharides,holds significant potential as a target for antibacterial agents[2].However,its conformational dynamics have presented substantial challenges in developing MraY-targeting inhibitors.展开更多
Bemisia tabaci is a polyphagous herbivore that feeds on a wide range of horticultural and ornamental crops cultivated under diverse ecological zones. In Sierra Leone, B. tabaci is found to infest a wide range of veget...Bemisia tabaci is a polyphagous herbivore that feeds on a wide range of horticultural and ornamental crops cultivated under diverse ecological zones. In Sierra Leone, B. tabaci is found to infest a wide range of vegetable crops by directly feeding on phloem sap thereby inducing physiological disorders, and also serve as a vector to gemini viruses. Invariably the destructive feeding of B. tabaci affects the productivity and aesthetic values of vegetables and other horticultural crops and hence is considered a serious economic pest. A bioassay experiment was carried out by rearing B. tabaci populations on four vegetable crops under controlled laboratory conditions to determine its life table and demographic parameters. Results showed that the intrinsic rate of growth which measures the population size and growth pattern was highest for populations reared on tomato crops with the following values: rm 0.145 female female−1 day−1, the gross reproduction rate (Ro), and finite growth rate λ were highest for population reared on tomato, correspondingly the development period from egg-adult emergence was shortest with a value of 26 d. Conversely, the computed demographical parameters rm, λ and Ro for the population reared on sweet pepper were 0.106 female female−1 day−1 respectively, with a corresponding development period egg-adult emergence as 36d. The computed biological parameters for okra and garden egg varied with intermediary values between tomato and pepper host materials. The survivorship rates were quite significant for the smaller instars (Instars 1-III) with over 80% surviving to pre-pupa and pupa stage for the populations reared for all the test materials. High mortality was noticed for the pre-pupa and pupa stages as their survival rates were significantly low compared to the high survival rates of the smaller instars. Less than 50% of pupae failed to emerge to adults except for populations reared on tomato test materials where 52% emerged to adults. The study indicated tomato as the most suitable host among the four vegetable crops. Although life table and demographic parameters are invaluable information for forecasting pest populations and help in designing pest management efforts, further investigations such as the economic threshold and economic injury levels of B. tabaci population are requisite decision tools for sound pest management decisions of B. tabaci on these vegetable crops. The information obtained from this investigation would be quite relevant for extension service and pest management practitioners where mixed vegetable farming is a common practice.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.展开更多
文摘Translation is the use of one language to another language;this is expressed by the contents of the re-express.For the English-Chinese translation,the attributive clause translation is the most important.However,the English attributive clause is divided into restrictive and non-restrictive attributive clauses.In the translation process,it should be based on its structure and meaning.To take appropriate translation,get rid of restrictions on sentence structure so that the translation is equal to the original as much as possible.In this paper,the translation skills and samples have been used to further express the attributive clause to the readers.
文摘The attributive clause plays a significant role in E-C translation.Due to the stylistic difference in Chinese and English(Chinese is paratactic while English is hypotactic),complex English attributive clauses cannot be translated literally as awkwardness may occur.One possible approach to coping with the problem is the adoption of‘Division’in translating long English attributive clauses.This essay provides three steps in translating complex English attributive clauses into Chinese(clarifying meaning units,restructuring and polishing)and the accompanied techniques such as amplification and omission to make the translation more readable and natural to Chinese readers.
文摘Attributive clauses in EST often consist of complex structures and deliver a multiplicity of meanings. It will be one of challenges facing the translators to transfer the attributive clauses in EST faithfully, completely and smoothly into Chinese so that the understanding and response of the target language readers to the information produced in the translation are almost the same as that of the original language readers to the information contained in the original attributive clauses in EST, thus achieving functional equivalence of the translation. Under the direction of Nida's functional equivalence theory, this paper is dedicated to discussing the translation techniques of EST attributive clauses, and hope it benefits the translation of EST in the future.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,and 41975037)the National Key Research and Development Programof China(No.2022YFC3700303).
文摘Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.
基金supported by the National Natural Science Foundation of China(Nos.11171322,11426236)the Fundamental Research Funds for the Central Universities(WK0010000051).
文摘As the applications for modeling of big data and analysis advance in scope,computational efficiency faces greater challenges in terms of storage and speed.In many practical problems,a great amount of historical data is sequentially collected and used for online statistical modeling.For modeling sequential data,we propose a sequential linear regression method that extracts essential information from historical data.This carefully selected information is then utilized to update a model according to a sequential estimation scheme.With this technique,the earlier data no longer needs to be stored,and the sequential updating is computationally efficient in speed and storage.A weighted strategy is introduced on the current model to determine the impact of data from different periods.When compared with estimation methods that use historical data,our numerical experiments demonstrate that our solution increases the speed while decreasing the storage load.
基金Supported by STI2030-Major Projects,No.2021ZD0202001National Natural Science Foundation of China,No.T2341003Capital Funds for Health Improvement and Research,No.CFH 2022-2-4012.
文摘BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.
基金Supported by Taishan Scholar Special Funding,No.tsqnz20240858Medical and Health Technology Project of Shandong Province,No.202402050122+4 种基金Science and Technology Development Plan of Jinan Municipal Health Commission,No.2024301008Clinical Medical Science and Technology Innovation Program of Jinan Science and Technology Bureau,No.202430055Natural Science Foundation of Jiangxi Province,No.20224BAB206077Gansu Provincial Hospital Intra-Hospital Research Fund Project,No.22GSSYB-6and the 2022 Master/Doctor/Postdoctoral Program of National Health Commission Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor,No.NHCDP2022004 and No.NHCDP2022008.
文摘Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the multifactorial nature and the deficiency of available disease-modifying treatments.The application of mesenchymal stem/stromal cells(MSCs)as therapeutic drugs has provided novel treatment options for diverse degenerative and chronic diseases including KOA.However,the complexity and specificity of the“live”cells have posed challenges for MSC-based drug development and the concomitant scale-up preparation from laboratory to industrialization.For instance,despite the considerable progress in ex vivo cell culture technology for fulfilling the robust development of drug conversion and clinical trials,yet significant challenges remain in obtaining regulatory approvals.Thus,there’s an urgent need for the research and development of MSC drugs for KOA.In this review,we provide alternative solution strategies for the preparation of MSC drugs on the basis of the principle of quality by design,including designing the cell production processes,quality control,and clinical applications.In detail,we mainly focus on the quality by design method for MSC manufacturing in standard cell-culturing factories for the treatment of KOA by using the Quality Target Product Profile as a starting point to determine potential critical quality attributes and to establish relationships between critical material attributes and critical process parameters.Collectively,this review aims to meet product performance and robust process design,and should help to reduce the gap between compliant products and the production of compliant good manufacturing practice.
基金supported by the Capital’s Funds for Health Improvement and Research (No. 2024-1G-4023)。
文摘Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level simulation model based on comparative risk assessment to estimate population attributable fractions and avoidable liver cancer deaths. Risk factor prevalence data were obtained from national surveys and epidemiological estimates. Three intervention scenarios for each risk factor were projected:elimination(Scenario 1), ambitious reduction(Scenario 2), and manageable targets aligned with national/global goals(Scenario 3). The impact of secondary prevention through liver cancer screening at different coverage was evaluated.Results: Between 2021 and 2050, liver cancer deaths in China are projected to reach 9.44 million in males and4.29 million in females. Eliminating hepatitis B virus and hepatitis C virus could prevent 65.62%(57.47%-73.77%)and 28.47%(24.93%-32.00%) of liver cancer deaths, respectively. Achieving manageable targets in reducing the prevalence of smoking and alcohol drinking could prevent 6.57%(5.75%-7.38%) and 0.85%(0.75%-0.96%) of liver cancer deaths, with a more pronounced effect observed in males. Eliminating high body mass index(BMI)could avert 45,000 male and 25,000 female deaths annually by 2050, while diabetes elimination could prevent60,000 male and 21,000 female deaths. Secondary prevention through liver cancer screening with 80% coverage could reduce liver cancer deaths by 3.59%(3.14%-4.04%) for the total population. Combining all interventions under Scenario 1 could prevent up to 88.39%(76.65%-99.81%) of male and 77.80%(67.42%-87.88%) of female liver cancer deaths by 2050.Conclusions: Comprehensive risk factor control could prevent over 80% of liver cancer deaths in China by2050. Secondary prevention through screening may offer modest additional benefits. These findings provide strong quantitative support for targeted, evidence-based interventions and underscore the need for policy action to address key risk factors.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00238866)Korea government(MOE)(2024 government collaboration type training project[Information security field],No.2024 personal information protection-002).
文摘This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.
文摘Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.
文摘Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.
文摘Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.
基金supported by the National Key R&D Program of China(Grant Nos.2024YFE0109800 and 2024YFE0109802)the National Natural Science Foundation of China(Grant No.12305127)the International Partnership Program of the Chinese Academy of Sciences(Grant No.016GJHZ2022054FN)。
文摘Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic variables.In this study,we analyze the proton structure function F_(2)employing the analytical solution of the Balitsky–Kovchegov equation,with a focus on the high Q^(2)regime and small x domains.Our results indicate that as Q^(2)increases,the slope parameterλ,which characterizes the growth rate of F_(2),exhibits a gradual decrease,approaching a limiting value ofλ≈0.41±0.01 for large Q^(2).We suggest that this behavior ofλmay be attributed to mechanisms such as gluon overlap and the suppression of phase space growth.To substantiate these conclusions,further high-precision electron–ion collision experiments are required,encompassing a broad range of Q^(2)and x.
基金Department of Water Resources of Shaanxi Province of China,No.2023slkj-8National Natural Science Foundation of China,No.51779209。
文摘Studying runoff characteristics and quantifying human activities’impact on northern Shaanxi,a crucial mineral resource area in China,is crucial to alleviate water resource contradictions.In this study,hydrological element trends were analyzed using theβ-z-h three-parameter indication method.The Mann-Kendall,Pettitt,moving T,and Yamamoto methods were used to test the mutation point of hydrological elements.The Budyko framework was used to quantitatively assess the impacts of climate change and multiple human activities on runoff reduction.The results showed that(1):Precipitation(PRE),potential evapotranspiration(E0),and temperature(TEM)showed increasing trends;runoff in the Huangfuchuan,Gushanchuan,Kuye River,Tuwei River,Wuding River,Qingjian River,and Yanhe River catchments showed decreasing trends(HFC,GSC,KYR,TWR,WDR,QJR,YR);whereas runoff in the Jialu River(JLR)catchment showed a“V-shaped”trend from 1980 to2020.(2)Runoff was positively correlated with PRE and negatively correlated with E0and the subsurface index(n),with the elasticity coefficients of PRE,E0,and n showing an increasing trend in the change period.(3)Human activities were a key factor in runoff reduction,although the impact of different human activities showed spatial variations.This study provides a scientific foundation for achieving the sustainable development of water resources in mining areas.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
文摘Despite the availability of effective antibacterial drugs,the increasing prevalence of antibiotic-resistant bacteria is primarily attributed to their excessive and inappropriate utilization.Antimicrobial resistance(AMR)represents one of the most concerning global health and development threats[1].Thereby,the continuous escalation in AMR necessitates urgent advancements in novel antibacterial strategies.The MraY enzyme,which plays a pivotal role in synthesizing bacterial cell wall-composing polysaccharides,holds significant potential as a target for antibacterial agents[2].However,its conformational dynamics have presented substantial challenges in developing MraY-targeting inhibitors.
文摘Bemisia tabaci is a polyphagous herbivore that feeds on a wide range of horticultural and ornamental crops cultivated under diverse ecological zones. In Sierra Leone, B. tabaci is found to infest a wide range of vegetable crops by directly feeding on phloem sap thereby inducing physiological disorders, and also serve as a vector to gemini viruses. Invariably the destructive feeding of B. tabaci affects the productivity and aesthetic values of vegetables and other horticultural crops and hence is considered a serious economic pest. A bioassay experiment was carried out by rearing B. tabaci populations on four vegetable crops under controlled laboratory conditions to determine its life table and demographic parameters. Results showed that the intrinsic rate of growth which measures the population size and growth pattern was highest for populations reared on tomato crops with the following values: rm 0.145 female female−1 day−1, the gross reproduction rate (Ro), and finite growth rate λ were highest for population reared on tomato, correspondingly the development period from egg-adult emergence was shortest with a value of 26 d. Conversely, the computed demographical parameters rm, λ and Ro for the population reared on sweet pepper were 0.106 female female−1 day−1 respectively, with a corresponding development period egg-adult emergence as 36d. The computed biological parameters for okra and garden egg varied with intermediary values between tomato and pepper host materials. The survivorship rates were quite significant for the smaller instars (Instars 1-III) with over 80% surviving to pre-pupa and pupa stage for the populations reared for all the test materials. High mortality was noticed for the pre-pupa and pupa stages as their survival rates were significantly low compared to the high survival rates of the smaller instars. Less than 50% of pupae failed to emerge to adults except for populations reared on tomato test materials where 52% emerged to adults. The study indicated tomato as the most suitable host among the four vegetable crops. Although life table and demographic parameters are invaluable information for forecasting pest populations and help in designing pest management efforts, further investigations such as the economic threshold and economic injury levels of B. tabaci population are requisite decision tools for sound pest management decisions of B. tabaci on these vegetable crops. The information obtained from this investigation would be quite relevant for extension service and pest management practitioners where mixed vegetable farming is a common practice.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
