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
Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime att...Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime attribute and a key.However,determining whether an attribute is a prime attribute is a nondeterministic polynomial-time complete(NP-complete)problem,making it intractable to determine if a relation scheme is in a specific normal form.While the prime attribute problem is generally NP-complete,there are cases where identifying prime attributes is not challenging.In a relation scheme R(U,F),we partition U into four distinct subsets based on where attributes in U appear in F:U_(1)(attributes only appearing on the left-hand side of FDs),U_(2)(attributes only appearing on the right-hand side of FDs),U_(3)(attributes appearing on both sides of FDs),and U_(4)(attributes not present in F).Next,we demonstrate the necessary and sufficient conditions for a key to be the unique key of a relation scheme.Subsequently,we illustrate the features of prime attributes in U_(3) and generalize the features of common prime attributes.The findings lay the groundwork for distinguishing between complex and simple cases in prime attribute identification,thereby deepening the understanding of this problem.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
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
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
To investigate the distribution and velocity attributes of gas hydrates in the northern continental slope of South China Sea, Guangzhou Marine Geological Survey conducted four-component （4C） ocean-bottom seismometer...To investigate the distribution and velocity attributes of gas hydrates in the northern continental slope of South China Sea, Guangzhou Marine Geological Survey conducted four-component （4C） ocean-bottom seismometer （OBS） surveys. A case study is presented to show the results of acquiring and processing OBS data for detecting gas hydrates. Key processing steps such as repositioning, reorientation, PZ summation, and mirror imaging are discussed. Repositioning and reorientation find the correct location and direction of nodes. PZ summation matches P- and Z-components and sums them to separate upgoing and downgoing waves. Upgoing waves are used in conventional imaging, whereas downgoing waves are used in mirror imaging. Mirror imaging uses the energy of the receiver ghost reflection to improve the illumination of shallow structures, where gas hydrates and the associated bottom-simulating reflections （BSRs） are located. We developed a new method of velocity analysis using mirror imaging. The proposed method is based on velocity scanning and iterative prestack time migration. The final imaging results are promising. When combined with the derived velocity field, we can characterize the BSR and shallow structures; hence, we conclude that using 4C OBS can reveal the distribution and velocity attributes of gas hydrates.展开更多
The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness predi...The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain.Using seismic forward modeling of a typical thin channel sand body,a new seismic attribute-the ratio of peak frequency to amplitude was constructed.Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies,while the amplitude attribute is sensitive to the strata lithology.The ratio of the two attributes can highlight the boundaries of the channel sand body.Moreover,the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness.Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well.The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries.Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.展开更多
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.
文摘Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime attribute and a key.However,determining whether an attribute is a prime attribute is a nondeterministic polynomial-time complete(NP-complete)problem,making it intractable to determine if a relation scheme is in a specific normal form.While the prime attribute problem is generally NP-complete,there are cases where identifying prime attributes is not challenging.In a relation scheme R(U,F),we partition U into four distinct subsets based on where attributes in U appear in F:U_(1)(attributes only appearing on the left-hand side of FDs),U_(2)(attributes only appearing on the right-hand side of FDs),U_(3)(attributes appearing on both sides of FDs),and U_(4)(attributes not present in F).Next,we demonstrate the necessary and sufficient conditions for a key to be the unique key of a relation scheme.Subsequently,we illustrate the features of prime attributes in U_(3) and generalize the features of common prime attributes.The findings lay the groundwork for distinguishing between complex and simple cases in prime attribute identification,thereby deepening the understanding of this problem.
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.
基金supported by the National Hi-tech Research and Development Program of China(863 Program)(Grant No.2013AA092501)the China Geological Survey Projects(Grant Nos.GZH201100303 and GZH201100305)
文摘To investigate the distribution and velocity attributes of gas hydrates in the northern continental slope of South China Sea, Guangzhou Marine Geological Survey conducted four-component （4C） ocean-bottom seismometer （OBS） surveys. A case study is presented to show the results of acquiring and processing OBS data for detecting gas hydrates. Key processing steps such as repositioning, reorientation, PZ summation, and mirror imaging are discussed. Repositioning and reorientation find the correct location and direction of nodes. PZ summation matches P- and Z-components and sums them to separate upgoing and downgoing waves. Upgoing waves are used in conventional imaging, whereas downgoing waves are used in mirror imaging. Mirror imaging uses the energy of the receiver ghost reflection to improve the illumination of shallow structures, where gas hydrates and the associated bottom-simulating reflections （BSRs） are located. We developed a new method of velocity analysis using mirror imaging. The proposed method is based on velocity scanning and iterative prestack time migration. The final imaging results are promising. When combined with the derived velocity field, we can characterize the BSR and shallow structures; hence, we conclude that using 4C OBS can reveal the distribution and velocity attributes of gas hydrates.
基金supported by National Key Science and Technology Special Projects (Grant No.2008ZX05000-004)CNPC Key S and T Special Projects (Grant No.2008E-0610-10)
文摘The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration.We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain.Using seismic forward modeling of a typical thin channel sand body,a new seismic attribute-the ratio of peak frequency to amplitude was constructed.Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies,while the amplitude attribute is sensitive to the strata lithology.The ratio of the two attributes can highlight the boundaries of the channel sand body.Moreover,the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness.Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well.The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries.Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.
