Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi...Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.展开更多
The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,inclu...The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.展开更多
The investigation of the tendency of climate change and its effects on ecology, economy and sociology is essential for long term policy making. The long-term measurement of the physical and chemical properties of the ...The investigation of the tendency of climate change and its effects on ecology, economy and sociology is essential for long term policy making. The long-term measurement of the physical and chemical properties of the atmosphere with state-of-the-arts instruments provides high-quality data for these studies. The evaluated data are stored in really special file structures and formats that cannot be inserted in one common database. Moreover, the observed data usually available in ASCII format and the users sometimes need to convert them in other format. The file conversion is usually time consuming procedure and can contribute to the uncertainties. MeteoRead is a client database software that imports the observed atmospheric data e.g. wind direction, wind speed, aerosol particle concentration etc. and makes them available in different file formats, which are most commonly used in climate research. This Java<sup>TM</sup> based program applies the Structured Query Language (SQL) functions such as table creation on a database server, data or figures insertion into the table and data selection via Graphical User Interface. The selected data can be stored in NetCDF, HDF5, DataBase or TXT file formats and the figures can be available in PNG, JPG, JPNG, PDF or GIF file formats. The program was tested on Linux and Windows platforms with different Java<sup>TM</sup> Development Kit. The MeteoRead is planned to be developed to visualizing the annual, seasonal, monthly, daily or hourly average value of the selected data and to use the functionality of the SQL database to calculate various mathematical and statistical correlations.展开更多
1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zh...1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.展开更多
This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In ...This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.展开更多
Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient metho...Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.展开更多
Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime,...Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.展开更多
An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid in...An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid integration technology. Multimode output waveguides in the silica AWG with 2% refractive index difference are used to obtain fiat-top spectra. The output waveguide facet is polished to 45° bevel to change the light propagation direction into the mesa-type PIN PD, which simplifies the packaging process. The experimentM results show that the single channel I dB bandwidth of AWG ranges from 2.12nm to 3.06nm, the ROSA responsivity ranges from 0.097 A/W to 0.158A/W, and the 3dB bandwidth is up to 11 GHz. It is promising to be applied in the eight-lane WDM transmission system in data center interconnection.展开更多
The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO...The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.展开更多
Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offer...Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.展开更多
Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading...Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.展开更多
The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thr...The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.展开更多
Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, kn...Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of data.The paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.展开更多
As the data managed by applications has evolved over the years with the arrival of Web 2.0,a large number of new databases have been developed to manage various types of data.While the traditional relational databases...As the data managed by applications has evolved over the years with the arrival of Web 2.0,a large number of new databases have been developed to manage various types of data.While the traditional relational databases continue to exist,NoSQL databases which are document oriented or key-value stores or columnar continue to evolve and are embraced very rapidly across the industry.It is not just the type of data handled by these databases that is different but also the query language they use is also different.This paper talks about a uniform query framework that can be used for traditional relational databases and NoSQL databases.This query framework can also perform joins,aggregates,filter on data from various data sources in a single query.展开更多
In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantag...In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.展开更多
In studies of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the sur...In studies of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the survey. Infections are often clustered within geographical areas such as enumerator areas (EAs) and thus inducing unobserved frailty. In this paper we consider an approach for estimating parameters when infection time is unknown and assumed correlated within an EA where dependency is modeled as frailties assuming a normal distribution for frailties and a Weibull distribution for baseline hazards. The data was from a household based population survey that used a multi-stage stratified sample design to randomly select 23,275 interviewed individuals from 10,584 households of whom 15,851 interviewed individuals were further tested for HIV (crude prevalence = 9.1%). A further test conducted among those that tested HIV positive found 181 (12.5%) recently infected. Results show high degree of heterogeneity in HIV distribution between EAs translating to a modest correlation of 0.198. Intervention strategies should target geographical areas that contribute disproportionately to the epidemic of HIV. Further research needs to identify such hot spot areas and understand what factors make these areas prone to HIV.展开更多
Painting is done according to the artist’s style.The most representative of the style is the texture and shape of the brush stroke.Computer simulations allow the artist’s painting to be produced by taking this strok...Painting is done according to the artist’s style.The most representative of the style is the texture and shape of the brush stroke.Computer simulations allow the artist’s painting to be produced by taking this stroke and pasting it onto the image.This is called stroke-based rendering.The quality of the result depends on the number or quality of this stroke,since the stroke is taken to create the image.It is not easy to render using a large amount of information,as there is a limit to having a stroke scanned.In this work,we intend to produce rendering results using mass data that produces large amounts of strokes by expanding existing strokes through warping.Through this,we have produced results that have higher quality than conventional studies.Finally,we also compare the correlation between the amount of data and the results.展开更多
Human genetic variants have long been known to play an important role in both Mendelian disorders and common diseases. Notably, pathogenic variants are not limited to single-nucleotide variants. It has become apparent...Human genetic variants have long been known to play an important role in both Mendelian disorders and common diseases. Notably, pathogenic variants are not limited to single-nucleotide variants. It has become apparent that human diseases can also be caused by copy number variations （CNVs）, especially patient- specific novel CNVs （lafrate et al., 2004; Sebat et al., 2004; Redon et al., 2006; LuDski, 2007; Zhan~ et al.. 2009： Wu et al.. 2015）.展开更多
Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （...Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （Huarte, 2015）. Due to recent advances in next-generation sequencing technologies, tens of thousands of lncRNAs have been identified and annotated, a number of them have been proven to be functional in diverse biological processes through various mechanisms.展开更多
基金funded by University of Transport and Communications(UTC)under grant number T2025-CN-004.
文摘Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography.
基金supported by Hainan Provincial Natural Science Foundation of China Nos.622RC617,624RC485Open Foundation of State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2023-1-07).
文摘The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government departments.However,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade processes.In addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain technology.This scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among participants.In order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public parameters.Integration with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different organizations.The scheme ensures the auditability of data access control information and the visualization of on-chain data sharing.The MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward security.Theoretical analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.
文摘The investigation of the tendency of climate change and its effects on ecology, economy and sociology is essential for long term policy making. The long-term measurement of the physical and chemical properties of the atmosphere with state-of-the-arts instruments provides high-quality data for these studies. The evaluated data are stored in really special file structures and formats that cannot be inserted in one common database. Moreover, the observed data usually available in ASCII format and the users sometimes need to convert them in other format. The file conversion is usually time consuming procedure and can contribute to the uncertainties. MeteoRead is a client database software that imports the observed atmospheric data e.g. wind direction, wind speed, aerosol particle concentration etc. and makes them available in different file formats, which are most commonly used in climate research. This Java<sup>TM</sup> based program applies the Structured Query Language (SQL) functions such as table creation on a database server, data or figures insertion into the table and data selection via Graphical User Interface. The selected data can be stored in NetCDF, HDF5, DataBase or TXT file formats and the figures can be available in PNG, JPG, JPNG, PDF or GIF file formats. The program was tested on Linux and Windows platforms with different Java<sup>TM</sup> Development Kit. The MeteoRead is planned to be developed to visualizing the annual, seasonal, monthly, daily or hourly average value of the selected data and to use the functionality of the SQL database to calculate various mathematical and statistical correlations.
基金granted by the National Science&Technology Major Projects of China(Grant No.2016ZX05033).
文摘1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.
基金Supported by the National Science of China(6 0 0 75 0 15 ) and Key Project of Scientific and Technological Departmentin Anhui
文摘This paper first puts forward a case based system framework based on data mining techniques. Then the paper examines the possibility of using neural networks as a method of retrieval in such a case based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.
文摘Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.
基金supported by the NSC under Grant No.NSC-101-2221-E-239-032 and NSC-102-2221-E-239-020
文摘Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.
基金Supported by the National High Technology Research and Development Program of China under Grant No 2015AA016902the National Natural Science Foundation of China under Grant Nos 61435013 and 61405188the K.C.Wong Education Foundation
文摘An 8×10 GHz receiver optical sub-assembly （ROSA） consisting of an 8-channel arrayed waveguide grating （AWG） and an 8-channel PIN photodetector （PD） array is designed and fabricated based on silica hybrid integration technology. Multimode output waveguides in the silica AWG with 2% refractive index difference are used to obtain fiat-top spectra. The output waveguide facet is polished to 45° bevel to change the light propagation direction into the mesa-type PIN PD, which simplifies the packaging process. The experimentM results show that the single channel I dB bandwidth of AWG ranges from 2.12nm to 3.06nm, the ROSA responsivity ranges from 0.097 A/W to 0.158A/W, and the 3dB bandwidth is up to 11 GHz. It is promising to be applied in the eight-lane WDM transmission system in data center interconnection.
基金financial support extended for this academic work by the Beijing Natural Science Foundation(Grant 2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212).
文摘The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.
文摘Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.
文摘Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.
基金Supported by National Natural Science Foundation of China(61304079,61125306,61034002)the Open Research Project from SKLMCCS(20120106)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-13-018A)the China Postdoctoral Science.Foundation(201_3M_5305_27)
基金primarily supported by the National 973 Fundamental Research Program of China(Grant No.2013CB430103)the Department of Transportation Federal Aviation Administration(Grant No.NA17RJ1227)through the National Oceanic and Atmospheric Administration+1 种基金supported by the National Science Foundation of China(Grant No.41405100)the Fundamental Research Funds for the Central Universities(Grant No.20620140343)
文摘The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.
基金This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:HI21C1831)and the Soonchunhyang University Research Fund.
文摘Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of data.The paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.
文摘As the data managed by applications has evolved over the years with the arrival of Web 2.0,a large number of new databases have been developed to manage various types of data.While the traditional relational databases continue to exist,NoSQL databases which are document oriented or key-value stores or columnar continue to evolve and are embraced very rapidly across the industry.It is not just the type of data handled by these databases that is different but also the query language they use is also different.This paper talks about a uniform query framework that can be used for traditional relational databases and NoSQL databases.This query framework can also perform joins,aggregates,filter on data from various data sources in a single query.
文摘In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.
文摘In studies of HIV, interval-censored data occur naturally. HIV infection time is not usually known exactly, only that it occurred before the survey, within some time interval or has not occurred at the time of the survey. Infections are often clustered within geographical areas such as enumerator areas (EAs) and thus inducing unobserved frailty. In this paper we consider an approach for estimating parameters when infection time is unknown and assumed correlated within an EA where dependency is modeled as frailties assuming a normal distribution for frailties and a Weibull distribution for baseline hazards. The data was from a household based population survey that used a multi-stage stratified sample design to randomly select 23,275 interviewed individuals from 10,584 households of whom 15,851 interviewed individuals were further tested for HIV (crude prevalence = 9.1%). A further test conducted among those that tested HIV positive found 181 (12.5%) recently infected. Results show high degree of heterogeneity in HIV distribution between EAs translating to a modest correlation of 0.198. Intervention strategies should target geographical areas that contribute disproportionately to the epidemic of HIV. Further research needs to identify such hot spot areas and understand what factors make these areas prone to HIV.
基金This research was supported by the Chung-Ang University Research Scholarship Grants in 2017.
文摘Painting is done according to the artist’s style.The most representative of the style is the texture and shape of the brush stroke.Computer simulations allow the artist’s painting to be produced by taking this stroke and pasting it onto the image.This is called stroke-based rendering.The quality of the result depends on the number or quality of this stroke,since the stroke is taken to create the image.It is not easy to render using a large amount of information,as there is a limit to having a stroke scanned.In this work,we intend to produce rendering results using mass data that produces large amounts of strokes by expanding existing strokes through warping.Through this,we have produced results that have higher quality than conventional studies.Finally,we also compare the correlation between the amount of data and the results.
基金supported by the National Basic Research Program of China(No.2012CB944600)the National Key Research and Development Program of China(No.2016YFC0905100)+1 种基金the National Natural Science Foundation of China(Nos.31521003,31625015,31571297,31601046,31525014 and 91331204)the Science and Technology Commission of Shanghai Municipality(No.16YF1413900)
文摘Human genetic variants have long been known to play an important role in both Mendelian disorders and common diseases. Notably, pathogenic variants are not limited to single-nucleotide variants. It has become apparent that human diseases can also be caused by copy number variations （CNVs）, especially patient- specific novel CNVs （lafrate et al., 2004; Sebat et al., 2004; Redon et al., 2006; LuDski, 2007; Zhan~ et al.. 2009： Wu et al.. 2015）.
基金supported by grants from the National Key R&D Program of China (2017YFA0106700)National Natural Science Foundation of China (81772614, U1611261, 81772586 and 81602461)+3 种基金China Postdoctoral Science Foundation (2017M610573)Young Elite Scientists Sponsorship Program by CAST (2017QNRC001)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017, to J. Zheng)Fundamental Research Funds for the Central Universities (SYSU:17ykzd32)
文摘Long noncoding RNAs （IncRNAs） have been increasingly implicated in a variety of human diseases, including autoimmune disease （Wu et al., 2015）, neurodegenerative diseases （Wapinski and Chang, 2011） and cancer （Huarte, 2015）. Due to recent advances in next-generation sequencing technologies, tens of thousands of lncRNAs have been identified and annotated, a number of them have been proven to be functional in diverse biological processes through various mechanisms.
