Semantic-based query processing for relational data integration

To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Storage optimization for query processing over data streams

A defining characteristic of continuous queries over on-line data streams,possibly bounded by sliding windows,is the potentially infinite and time-evolving nature of their inputs and outputs.For different update patterns of continuous queries,suitable data structures bring great query processing efficiency.In this paper,we proposed a data structure suitable for weak nonmonotonic update pattern in which the lifetime of each tuple is known at generation time,but the length of lifetime is not necessarily the same.The new data structure combined the ladder queue with the feature of weak non-monotonic update pattern.The experiment results show that the new data structure performs much better than the traditional calendar queue in many cases.

Efficient Pr-Skyline Query Processing and Optimization in Wireless Sensor Networks

As one of the commonly used queries in modern databases, skyline query has received extensive attention from database research community. The uncertainty of the data in wireless sensor networks makes the corresponding skyline uncertain and not unique. This paper investigates the Pr-Skyline problem, i.e., how to compute the skyline with the highest existence probability in a computational and energy-efficient way. We formulate the problem and prove that it is NP-Complete and cannot be approximated in a given expression. However, the proposed algorithm SKY-SEARCH with pruning techniques can guarantee the computational efficiency given relatively large input size, while the filter-based distributed optimization strategy significantly reduces the transmission cost and the required storage space of the sensor nodes. Extensive experiments verify the efficiency and scalability of SKY-SEARCH and the distributed optimizing strategy.

Constraints on Characteristics and Distribution of Gas Hydrate and Free Gas Using Broad-Band Processing of Three-Dimensional Seismic Data

Gas hydrate drilling expeditions in the Pearl River Mouth Basin,South China Sea,have identified concentrated gas hydrates with variable thickness.Moreover,free gas and the coexistence of gas hydrate and free gas have been confirmed by logging,coring,and production tests in the foraminifera-rich silty sediments with complex bottom-simulating reflectors(BSRs).The broad-band processing is conducted on conventional three-dimensional(3D)seismic data to improve the image and detection accuracy of gas hydratebearing layers and delineate the saturation and thickness of gas hydrate-and free gas-bearing sediments.Several geophysical attributes extracted along the base of the gas hydrate stability zone are used to demonstrate the variable distribution and the controlling factors for the differential enrichment of gas hydrate.The inverted gas hydrate saturation at the production zone is over 40% with a thickness of 90 m,showing the interbedded distribution with different boundaries between gas hydrate-and free gas-bearing layers.However,the gas hydrate saturation value at the adjacent canyon is 70%,with 30-m-thick patches and linear features.The lithological and fault controls on gas hydrate and free gas distributions are demonstrated by tracing each gas hydrate-bearing layer.Moreover,the BSR depths based on broad-band reprocessed 3D seismic data not only exhibit variations due to small-scale topographic changes caused by seafloor sedimentation and erosion but also show the upward shift of BSR and the blocky distribution of the coexistence of gas hydrate and free gas in the Pearl River Mouth Basin.

A Parallel Processing Method for Moving Top-K Spatial Keyword Query

We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.

Investigation on taste characteristics and sensory perception of soft-boiled chicken during oral processing based on electronic tongue and electronic nose

The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual oral environment.To explore the oral processing characteristics of soft-boiled chicken,the sensory properties,texture,particle size,viscosity,characteristic values of electronic nose and tongue of different chicken samples were investigated.The correlation analysis showed that the physical characteristics especially the cohesiveness,springiness,resilience of the sample determined oral processing behavior.The addition of chicken skin played a role in lubrication during oral processing.The particle size of the bolus was heightened at the early stage,and the fluidity was enhanced in the end,which reduced the chewing time to the swallowing point and raised the aromatic compounds signal of electronic nose.But the effect of chicken skin on chicken thigh with relatively high fat content,was opposite in electronic nose,which had a certain masking effect on the perception of umami and sweet taste.In conclusion,fat played a critical role in chicken oral processing and chicken thigh had obvious advantages in comprehensive evaluation of soft-boiled chicken,which was more popular among people.

Toluene Processed All-Polymer Solar Cells with 18%Efficiency and Enhanced Stability Enabled by Solid Additive:Comparison Between Sequential-Processing and Blend-Casting

The emergence of polymerized small molecule acceptors(PSMAs)has significantly improved the performance of all-polymer solar cells(all-PSCs).However,the pace of device engineering lacks behind that of materials development,so that a majority of the PSMAs have not fulfilled their potentials.Furthermore,most high-performance all-PSCs rely on the use of chloroform as the processing solvent.For instance,the recent highperformance PSMA,named PJ1-γ,with high LUMO,and HOMO levels,could only achieve a PCE of 16.1%with a high-energy-level donor(JD40)using chloroform.Herein,we present a methodology combining sequential processing(SqP)with the addition of 0.5%wt PC_(71)BM as a solid additive(SA)to achieve an impressive efficiency of 18.0%for all-PSCs processed from toluene,an aromatic hydrocarbon solvent.Compared to the conventional blend-casting(BC)method whose best efficiency(16.7%)could only be achieved using chloroform,the SqP method significantly boosted the device efficiency using toluene as the processing solvent.In addition,the donor we employ is the classic PM6 that has deeper energy levels than JD40,which provides low energy loss for the device.We compare the results with another PSMA(PYF-T-o)with the same method.Finally,an improved photostability of the SqP devices with the incorporation of SA is demonstrated.

Effects of friction stir processing and nano-hydroxyapatite on the microstructure,hardness,degradation rate and in-vitro bioactivity of WE43 alloy for biomedical applications

Nowadays,magnesium alloys are emerging in biomedical implants for their similar properties to natural bones.However,the rapid degradation of magnesium alloys in biological media hinders successful implantation.Refinement of microstructure,as well as reinforcement particles can significantly improve the degradation rate.In this work,multi-pass friction stir processing(FSP)was proposed to synthesize WE43/nano-hydroxyapatite(n HA)surface composite,the microstructure,reinforced particle distribution,micro-hardness,corrosion behavior and in-vitro bioactivity were studied.The subsequent FSP passes of WE43 alloy and WE43/n HA composite refined the grain size which was reduced by 94.29%and 95.92%(2.63 and 1.88μm,respectively)compared to base metal after three passes.This resulted in increasing the microhardness by 120%(90.86 HV0.1)and 135%(105.59 HV0.1)for the WE43 and WE43-n HA,respectively.It is found that increasing FSP passes improved the uniform distribution of n HA particles within the composite matrix which led to improved corrosion resistance and less degradation rate.The corrosion rate of the FSPed WE43/n HA composite after three passes was reduced by 38.2%(4.13 mm/year)and the degradation rate was reduced by 69.7%(2.87 mm/y).This is attributed to secondary phase(Mg24Y5and Mg41Nd5)particle fragmentation and redistribution,as well as a homogeneous distribution of n HA.Additionally,the growing Ca-P and Mg(OH)2layer formed on the surface represented a protective layer that reduced the degradation rate.The wettability test revealed a relatively hydrophilic surface with water contact angle of 49.1±2.2°compared to 71.2±2.1°for base metal.Also,biomineralization test showed that apatite layer grew after immersion 7d in simulated body fluid with atomic ratio of Ca/P 1.60 approaching the stoichiometric ratio(1.67)indicating superior bioactivity of FSPed WE43/n HA composite after three passes.These results raise that the grain refinement by FSP and introduction of n HA particles significantly improved the degradation rate and in-vitro bioactivity of WE43 alloy for biomedical applications.

A new insight into LPSO phase transformation and mechanical properties uniformity of large-scale Mg-Gd-Y-Zn-Zr alloy prepared by multi-pass friction stir processing

A large-scale fine-grained Mg-Gd-Y-Zn-Zr alloy plate with high strength and ductility was successfully prepared by multi-pass friction stir processing(MFSP)technology in this work.The structure of grains and long period stacking ordered(LPSO)phase were characterized,and the mechanical properties uniformity was investigated.Moreover,a quantitative relationship between the microstructure and tensile yield strength was established.The results showed that the grains in the processed zone(PZ)and interfacial zone(IZ)were refined from 50μm to 3μm and 4μm,respectively,and numerous original LPSO phases were broken.In IZ,some block-shaped 18R LPSO phases were transformed into needle-like 14H LPSO phases due to stacking faults and the short-range diffusion of solute atoms.The severe shear deformation in the form of kinetic energy caused profuse stacking fault to be generated and move rapidly,greatly increasing the transformation rate of LPSO phase.After MFSP,the ultimate tensile strength,yield strength and elongation to failure of the large-scale plate were 367 MPa,305 MPa and 18.0% respectively.Grain refinement and LPSO phase strengthening were the major strengthening mechanisms for the MFSP sample.In particularly,the strength of IZ was comparable to that of PZ because the strength contribution of the 14H LPSO phase offsets the lack of grain refinement strengthening in IZ.This result opposes the widely accepted notion that IZ is a weak region in MFSP-prepared large-scale fine-grained plate.

Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

With its untameable and traceable properties,blockchain technology has been widely used in the field of data sharing.How to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data sharing.In this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in blockchain.Both the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy protection.However,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF queries.We propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF queries.The framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy protection.The data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy budget.We propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query time.The VB-cm tree uses the vector commitment to verify the query results.The fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing operations.We conduct an extensive evaluation of the proposed framework.The experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

Large Language Model Based Semantic Parsing for Intelligent Database Query Engine

With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.

Distributed location-based query processing on large volumes of moving items

Recently, new techniques to efficiently manage current and past location information of moving objects have received significant interests in the area of moving object databases and location based service systems. In this paper, we exploit query processing schemes for location management systems, which consist of multiple data processing nodes to handle massive volume of moving objects such as cellular phone users. To show the usefulness of the proposed schemes, some experimental results showing performance factors regarding distributed query processing are explained. In our experiments, we use two kinds of data set: one is generated by the extended GSTD simulator and another is generated by the real time data generator which generates location sensing reports of various types of users having different movement patterns.

Development and Validation of Integrated Nutrient Management Practices of Industrial Processing Varieties: Asterix and Courage in Bangladesh

An experiment was meticulously conducted at the research field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh, during the 2011-2012 potato growing season to develop integrated crop management practices for the potato seed production of industrial processing varieties Asterix and Courage. Significantly, higher growth and yield parameters were found in the BADC-recommended practice. Later, another experiment was conducted to validate the BADC practice during the 2013-2014 potato growing season in two locations in Bangladesh. Results showed that the production of tuber per hill, tuber weight per hill as well as gross tuber yield per plot, higher proportion of storable seed tubers, and more quality seed potatoes (A-grade and B-grade) seed tubers were found significantly higher in the “BADC developed practice” compared to other treatments. Viral diseases (PLRV and PVY) prevalence was lower in “BADC developed practice”. Moreover, “BADC developed practice” contributed more economic yield by minimizing input cost compared to “Munshiganj advanced farmers’ practice”. Therefore, the “BADC developed practice” was found “superior” regarding yield, quality, and profitability in seed potato production of industrial varieties—Asterix and Courage in Bangladesh.

Improved Responses with Multitaper Spectral Analysis for Magnetotelluric Time Series Data Processing:Examples from Field Data

In order to attain good quality transfer function estimates from magnetotelluric field data(i.e.,smooth behavior and small uncertainties across all frequencies),we compare time series data processing with and without a multitaper approach for spectral estimation.There are several common ways to increase the reliability of the Fourier spectral estimation from experimental(noisy)data;for example to subdivide the experimental time series into segments,taper these segments(using single taper),perform the Fourier transform of the individual segments,and average the resulting spectra.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Modulation of starch digestibility using non-thermal processing techniques:A review

Obesity and type 2 diabetes are widespread throughout the world, especially in developed countries. Starch is an important part of human staple food, the modulating of starch digestibility is conducive to reducing postprandial blood glucose levels and alleviating the chronic disease caused by high caloric intake. The digestion properties of starch are correlated with its structural features, including crystallization, amylose/amylopectin ratio, non-starch components, etc. Among the modified methods applied to regulate starch digestibility, non-thermal processing techniques(NTPT) receive extensive attention due to the characteristics of safety, environmental friendliness and high efficiency. The influence and mechanism of NTPT on the digestion properties of starch are discussed in this review, including ultrasounds, high pressure, γ-irradiation, etc. NTPT induces the alternation of morphological and structural characteristics of native starch, changing their sensitivity to enzymes. The effects of NTPT on the digestibility of starch are highly related to the processing parameters and structure characteristics of native starch. The review shows that NTPT is an effective way to modulate the digestion properties of starch and prevent people from suffering from chronic diseases such as obesity and type 2 diabetes.