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
Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management pl...Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.展开更多
Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distribu...Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.展开更多
Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosph...Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosphere microbiome and functioning as influenced by these cropping methods is essential for sustaining rice productivity.The objective of this study was to assess the impact of three different rice cropping methods(wetland rice,SRI,and aerobic rice)on the biochemical properties and bacterial communities within the rice rhizosphere across three key rice growth stages:tillering,flowering,and maturity.Soil organic carbon(SOC),microbial biomass carbon(MBC),dehydrogenase activity,substrate-induced respiration(SIR),and metabolic quotient(MQ)were assessed along with high-throughput 16S rRNA sequencing of rice rhizosphere soils.The rice rhizosphere soil registered the highest SOC,MBC,and dehydrogenase activity in SRI followed by wetland rice and then aerobic rice.Cropping method had a minimal impact on SIR and MQ.Along with cropping method,growth stage also significantly altered these biological attributes of rice rhizosphere.The trends of the highest SOC content and dehydrogenase activity at the flowering stage and the highest MBC content and SIR at the tillering stage of rice were observed in all three rice cropping methods.The analysis of bacterial communities,based on 16S rRNA gene sequencing,revealed that both cropping method and growth stage significantly impacted the composition of rhizosphere microbiomes.However,the influence of cropping method was less pronounced compared to growth stage.Cropping method caused notable shifts in the abundances of Proteobacteria,Bacteroidetes,and Chloroflexi,while growth stage affected the abundances of Proteobacteria,Actinobacteria,Cyanobacteria,Firmicutes,Chloroflexi,and Bacteroidetes.Based on these results,the SRI method led to higher diversification to the rhizosphere bacteriobiota,as well as greater incorporation of carbon into the soil and increased dehydrogenase activity compared to wetland rice and aerobic rice.This study deepens our understanding of how different cropping methods influence plant-microbe interaction and the implications for overall rice productivity and soil health.展开更多
The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogene...The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.展开更多
Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in t...Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.展开更多
Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biom...Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.展开更多
Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation o...Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation of specific decision-making problems and choose the best method to solve them.Multi-valued neutrosophic set,as an extension of neutrosophic set,can more effectively and accurately describe incomplete,uncertain or inconsistent information.TODIM and TOPSIS methods are two commonly used multi-attribute decision-making methods,each of which has its advantages and disadvantages.This paper proposes a new method based on TODIM and TOPSIS to solve multi-attribute decision-making problems under multi-valued neutrosophic environment.After introducing the related theory of multi-valued neutrosophic set and the traditional TODIM and TOPSIS methods,the new method based on a combination of TODIM and TOPSIS methods is described.And then,two illustrative examples proved the feasibility and validity of the proposed method.Finally,the result has been compared with some existing methods under the same examples and the proposed method’s superiority has been proved.This paper studies this kind of decision-making problem from algorithm idea,algorithm steps and decision-making influencing factors.展开更多
Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures...Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures (Shortened or Overlapped) on tree growth, yield components, fruit quality, and leaf mineral nutrients in an “Aztec Fuji” apple (Malus domestica Bork.) high-density orchard was studied over five years. Tilted trees with shortened arm configuration (TilShArm) always had significantly larger trunk cross-sectional area (TCSA) than Upright trees with an Overlapped arm configuration (UpOverArm) every year from 2012 to 2016. Trees with a TilShArm system had more cumulative fruit per tree than those with an Upright orientation. Trees with a tilted canopy (TilShArm and TilOverArm) tended to have higher yield per tree and yield per hectare than those with an upright system. Trees with a TilShArm system were more precocious and had more yield per tree than those with an upright canopy orientation in 2012. When values were polled over five years, trees with an upright canopy-shortened arm system (UpShArm) treatment had a lower biennial bearing index (BBI) than those with an upright canopy-overlapped system (UpOverArm). Trees receiving an arm shortening (UpShArm or TilShArm) configuration often had larger fruits than those with overlapped arms (UpOverArm and TilOverArm). Fruit from trees receiving an UpOverArm had higher fruit firmness than those from trees with other canopy-branch arrangements at harvest due to their smaller size. Fruit from trees with a TilShArm and TilOverArm had significantly higher water core and bitter pit but lower sunburn than trees with an upright canopy (UpShArm and UpOverArm). Leaves from trees with an UpOverArm canopy-branch configuration had the lowest leaf Ca but the highest leaf K and Fe concentrations among all treatments.展开更多
Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and m...Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the efficient development and utilization of deep CBM.展开更多
Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evo...The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.展开更多
This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in lin...This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in line with the general understanding of the impact different geometries have on RCS but show that geometries can also influence the variance of measured RCS, and typical attributes that reduce RCS increase the variance of the measured RCS. Notably, an increased angle between the front face of a plate and the direction of the radar signal decreased RCS but increased the variance of the RCS measured.展开更多
A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and con...A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.展开更多
The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess fo...The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.展开更多
As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,in...As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.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
Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rul...Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.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
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
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.
基金funded by NARI Group’s Independent Project of China(Grant No.524609230125)the Foundation of NARI-TECH Nanjing Control System Ltd.of China(Grant No.0914202403120020).
文摘Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.
基金supported by the Spanish Ministry of Science and Innovation project GREEN-RISK(Evaluation of past changes in ecosystem services and biodiversity in forests and restoration priorities under global change impacts-PID2020-119933RB-C21)A.C.received a pre-doctoral fellowship funded by the Spanish Ministry of Science and Innovation(PRE2021-099642).
文摘Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.
基金support from the Indian Council of Agricultural Research through the All India Network Project(AINP)on Soil Biodiversity and Biofertilizers to conduct this study is acknowledged。
文摘Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosphere microbiome and functioning as influenced by these cropping methods is essential for sustaining rice productivity.The objective of this study was to assess the impact of three different rice cropping methods(wetland rice,SRI,and aerobic rice)on the biochemical properties and bacterial communities within the rice rhizosphere across three key rice growth stages:tillering,flowering,and maturity.Soil organic carbon(SOC),microbial biomass carbon(MBC),dehydrogenase activity,substrate-induced respiration(SIR),and metabolic quotient(MQ)were assessed along with high-throughput 16S rRNA sequencing of rice rhizosphere soils.The rice rhizosphere soil registered the highest SOC,MBC,and dehydrogenase activity in SRI followed by wetland rice and then aerobic rice.Cropping method had a minimal impact on SIR and MQ.Along with cropping method,growth stage also significantly altered these biological attributes of rice rhizosphere.The trends of the highest SOC content and dehydrogenase activity at the flowering stage and the highest MBC content and SIR at the tillering stage of rice were observed in all three rice cropping methods.The analysis of bacterial communities,based on 16S rRNA gene sequencing,revealed that both cropping method and growth stage significantly impacted the composition of rhizosphere microbiomes.However,the influence of cropping method was less pronounced compared to growth stage.Cropping method caused notable shifts in the abundances of Proteobacteria,Bacteroidetes,and Chloroflexi,while growth stage affected the abundances of Proteobacteria,Actinobacteria,Cyanobacteria,Firmicutes,Chloroflexi,and Bacteroidetes.Based on these results,the SRI method led to higher diversification to the rhizosphere bacteriobiota,as well as greater incorporation of carbon into the soil and increased dehydrogenase activity compared to wetland rice and aerobic rice.This study deepens our understanding of how different cropping methods influence plant-microbe interaction and the implications for overall rice productivity and soil health.
文摘The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.
文摘Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.
基金supported by the Guangxi Key R&D Program (project No. AB16380254)a research project of Guangxi Forestry Department (Guilinkezi [2015] No.5)supported a grant for Bagui Senior Fellow (C33600992001)。
文摘Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.
基金This research was funded by the Humanities and Social Sciences Foundation of Ministry of Education of the Peoples Republic of China(17YJA630115)The recipient of the founding is DX.
文摘Due to the complexity of decision-making problems and the subjectivity of decision-makers in practical application,it is necessary to adopt different forms of information expression according to the actual situation of specific decision-making problems and choose the best method to solve them.Multi-valued neutrosophic set,as an extension of neutrosophic set,can more effectively and accurately describe incomplete,uncertain or inconsistent information.TODIM and TOPSIS methods are two commonly used multi-attribute decision-making methods,each of which has its advantages and disadvantages.This paper proposes a new method based on TODIM and TOPSIS to solve multi-attribute decision-making problems under multi-valued neutrosophic environment.After introducing the related theory of multi-valued neutrosophic set and the traditional TODIM and TOPSIS methods,the new method based on a combination of TODIM and TOPSIS methods is described.And then,two illustrative examples proved the feasibility and validity of the proposed method.Finally,the result has been compared with some existing methods under the same examples and the proposed method’s superiority has been proved.This paper studies this kind of decision-making problem from algorithm idea,algorithm steps and decision-making influencing factors.
文摘Canopy and branch architectures in high-density orchards can be crucial in production and fruit quality. The influence of two canopy orientations (Upright and Tilted) in combination with two arm (branch) architectures (Shortened or Overlapped) on tree growth, yield components, fruit quality, and leaf mineral nutrients in an “Aztec Fuji” apple (Malus domestica Bork.) high-density orchard was studied over five years. Tilted trees with shortened arm configuration (TilShArm) always had significantly larger trunk cross-sectional area (TCSA) than Upright trees with an Overlapped arm configuration (UpOverArm) every year from 2012 to 2016. Trees with a TilShArm system had more cumulative fruit per tree than those with an Upright orientation. Trees with a tilted canopy (TilShArm and TilOverArm) tended to have higher yield per tree and yield per hectare than those with an upright system. Trees with a TilShArm system were more precocious and had more yield per tree than those with an upright canopy orientation in 2012. When values were polled over five years, trees with an upright canopy-shortened arm system (UpShArm) treatment had a lower biennial bearing index (BBI) than those with an upright canopy-overlapped system (UpOverArm). Trees receiving an arm shortening (UpShArm or TilShArm) configuration often had larger fruits than those with overlapped arms (UpOverArm and TilOverArm). Fruit from trees receiving an UpOverArm had higher fruit firmness than those from trees with other canopy-branch arrangements at harvest due to their smaller size. Fruit from trees with a TilShArm and TilOverArm had significantly higher water core and bitter pit but lower sunburn than trees with an upright canopy (UpShArm and UpOverArm). Leaves from trees with an UpOverArm canopy-branch configuration had the lowest leaf Ca but the highest leaf K and Fe concentrations among all treatments.
文摘Deep coalbed methane(CBM)resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas.The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM.Therefore,fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM.First,the basic principles of calculating texture attributes by gray-level cooccurrence matrix(GLCM)and gray-level run-length matrix(GLRLM)were introduced.A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation.The seismic texture attributes were extracted using the GLCM and GLRLM.The research results indicate that the texture attributes calculated by both methods are responsive to fractures,with the 45°and 135°gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures.Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes,providing an important basis for the efficient development and utilization of deep CBM.
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
基金key technology project for the prevention and control of major workplace safety accidents in 2017 from the State Administration of Work Safety of China-the research on the identification and assessment technology and control system of major risks of enterprises for the prevention and control of severe accidents(Hubei-0002-2017AQ)supported by the Department of Emergency Management of Hubei Province,Wuhan 430064,China.
文摘The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.
文摘This resolution 5 (25−1 factorial) study aimed to ascertain an understanding of the interactions between different geometries on the resulting Radar Cross Section (RCS) of a target. The results of the study are in line with the general understanding of the impact different geometries have on RCS but show that geometries can also influence the variance of measured RCS, and typical attributes that reduce RCS increase the variance of the measured RCS. Notably, an increased angle between the front face of a plate and the direction of the radar signal decreased RCS but increased the variance of the RCS measured.
文摘A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.
文摘The domestic space can be defined as the sphere that articulates the needs for subjective containment and contextual stimuli.In this sense,questions arise about the indispensable attributes that spaces must possess for this articulation to take place adequately.Architecture,as the discipline in charge of satisfying the specific spatial needs of those who inhabit these spaces and,in a broader sense,as a concrete contribution to society,must address this relationship in all its complexity and generate concrete responses that incorporate the appropriate spatial attributes during the design processes.The design processes that shape living spaces confront this dialectic,and the manner in which they do so brings identity and character to them.It is believed that the higher the level of variables that are contemplated and weighted,the greater the adequacy of spaces to the changing dynamics of the people who inhabit them.This article focuses on a thorough analysis of these spatial attributes,in parallel to the definition of each one as a particular condition for design,based on their conceptualization,breakdown,and articulation.Conceptually,the following attributes are addressed:flexibility,adaptability,variability,versatility,multiplicity,plurality,integrality,gradualness,incrementality,progressiveness,independence,connectivity,intimacy,and privacy.Each of these attributes is valued as a contribution to creating adequate habitability in contextual terms,with consideration to possible integrations and combinations.
文摘As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.
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 the National Natural Science Foundation of China under Grant 61972057Hunan Provincial Natural Science Foundation of China under Grant 2022JJ30623.
文摘Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.
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.
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.
