Keyword Search in Large-Scale Databases with Topic Cluster Units

To solve the inefficiency of the existing keyword search methods in large databases, this paper proposes TCU-based query, an offline query method based on topic cluster units. First, topic cluster units (TCUs) are constructed through vertical grouping and horizontal grouping on tables and tuples. In contrast to traditional keyword query methods, this offline method cannot only reduce the query response time, but also return results comprising richer and more complete semantic information. In order to further improve the efficiency of data preprocessing, an optimized solution for table join ordering based on the genetic algorithm is presented. Second, we select index terms using the association rule, and then we build an index on every topic cluster; by doing so we can improve the query speed significantly. Finally, we conduct extensive experiments to demonstrate that our approach greatly improves the performance of keyword search

Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational Databases

Due to the ambiguity and impreciseness of keyword query in relational databases, the research on keyword query expansion has attracted wide attention. Existing query expansion methods expose users’ query intention to a certain extent, but most of them cannot balance the precision and recall. To address this problem, a novel two-step query expansion approach is proposed based on query recommendation and query interpretation. First, a probabilistic recommendation algorithm is put forward by constructing a term similarity matrix and Viterbi model. Second, by using the translation algorithm of triples and construction algorithm of query subgraphs, query keywords are translated to query subgraphs with structural and semantic information. Finally, experimental results on a real-world dataset demonstrate the effectiveness and rationality of the proposed method

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate

Analysis of High Temperature Motors With Micro-Arc Oxidation Ceramic Insulated Wire

The use of organic insulation in the motor has insulation temperature limitations, which limit its usage and further increase its power density. In response to this issue, this paper proposes a high-temperature resistant motor with the micro-arc oxidation ceramic insulated wire, of which the long-term service temperature is able to exceed 350 °C. Firstly, the generation conditions, formation principles, structural characteristics and electrical characteristics of micro-arc oxidation ceramic insulated wires were introduced. Secondly, the high temperature motor base on micro-arc oxidation ceramic insulated wires is proposed, and the temperature analysis is presented, the influence of motor load on temperature rise was studied. Then, the manufacturing process and insulation method of the motor were introduced. Finally, the high-temperature motor prototype was made, and the high-temperature resistance performance of the high-temperature motor prototype was tested, and the temperature distribution of the motor under different load conditions was verified

Symmetric learning data augmentation model for underwater target noise data expansion

An important issue for deep learning models is the acquisition of training of data. Without abundant data from a real production environment for training, deep learning models would not be as widely used as they are today. However, the cost of obtaining abundant real-world environment is high, especially for underwater environments. It is more straightforward to simulate data that is closed to that from real environment. In this paper, a simple and easy symmetric learning data augmentation model (SLDAM) is proposed for underwater target radiate-noise data expansion and generation. The SLDAM, taking the optimal classifier of an initial dataset as the discriminator, makes use of the structure of the classifier to construct a symmetric generator based on antagonistic generation. It generates data similar to the initial dataset that can be used to supplement training data sets. This model has taken into consideration feature loss and sample loss function in model training, and is able to reduce the dependence of the generation and expansion on the feature set. We verified that the SLDAM is able to data expansion with low calculation complexity. Our results showed that the SLDAM is able to generate new data without compromising data recognition accuracy, for practical application in a production environmen

Systematic Study and Imaging Application of Aggregation-Induced Emission of Ester-Isophorone Derivatives

The dicyanoisophorone derivatives show obvious AIE behaviors in our previous work. To study the bioimaging application of these chromophores with AIE/AIEE properties, the ester groups substituted for one cyan to form a new family based on isophorone (<b>2a</b>–<b>2e</b>). <b>2a</b>–<b>2d</b> exhibit obvious AIE/AIEE phenomena, while <b>2e</b> shows fluorescence quenching in the aggregate state. The morphology and size of aggregates with different water contents were investigated using SEM and DLS, indicating that a large number of smaller globular or quadrate nanoparticles with average diameters in the range 78.79–392.7 nm in mixed solutions are related to these AIE/AIEE or ACQ behaviors. We also made comparative analyses of their optical properties in different states. The crystal data of <b>2a</b>–<b>2d</b> reveal that the multiple intra- and intermolecular interactions leads to the molecular conformation being more stable, increases the planarity of compounds, restricts the intramolecular motions, and promotes the formation of <i>J</i>-type aggregate, enabling chromophores <b>2a</b>–<b>2d</b> to emit intensely in the solid state. In addition, the frontier molecular orbital energy and band gap calculated by density functional theory are quite consistent with the experimental results. Finally, these AIE/AIEE-active compounds could be used in bioimaging applications, which immensely provide a new strategy to the application of some AIE/AIEE systems

Att göra barns röster hörda - barns uppfattningar av visuellt berättande och högläsning ur barns perspektiv

Abstract Syftet med denna studie var att ge röst åt de medverkande barnens tankar, genom att synliggöra deras perspektiv på och uppfattningar av fenomenet visuellt berättande och högläsning i förskolan. Studien är gjord ur barns perspektiv och inspirerad av en fenomenografisk ansats, vilket innebar att det var centralt för oss att söka efter barnens uppfattningar. Vi anser att det finns en kunskapslucka då tidigare forskning kring visuellt berättande och högläsning ur barns perspektiv är bristfällig, eftersom den forskningen mestadels är utförd ur vuxenperspektiv. Våra frågeställningar var: Vilka uppfattningar har barnen av stunden och av karaktärerna i berättelserna? Vad uppfattar barnen att de kan lära sig av visuellt berättande och högläsning? Hur uppfattar barnen sitt inflytande över det som berättas och läses för dem? För att uppnå syftet och besvara våra frågeställningar intervjuade vi barnen och använde även metoden fotoelicitering, vilken innebar att barnens berättande lockades fram med hjälp av deras egentagna fotografier. Därefter har en fenomenografiskt inspirerad analys genomförts, i vilken barnens utsagor sammanställdes i beskrivningskategorier. Dessa analyserades sedan med hjälp av våra teoretiska utgångspunkter barns perspektiv och begreppet uppfattning. Vi kom då fram till följande tre beskrivningskategorier: Barnens uppfattningar av stunden och karaktärerna, barnens uppfattningar av att lära sig och barnens uppfattningar av inflytande, vilka utgör studiens resultat. Vår slutsats utifrån syftet, är att vi gett röst åt de medverkande barnens tankar genom att vi synliggjort deras uppfattningar av fenomenet visuellt berättande och högläsning

Solvatochromic Two-Photon Fluorescent Probe Enables <i>In Situ</i> Lipid Droplet Multidynamics Tracking for Nonalcoholic Fatty Liver and Inflammation Diagnoses

Intracellular lipid storage and regulation occur in lipid droplets, which are of great significance to the physiological activities of cells. Herein, a lipid droplet-specific fluorescence probe (lip-YB) with a high quantum yield (QYlip‑YB = 73.28%), excellent photostability, and quickly polarity sensitivity was constructed successfully. Interestingly, lip-YB exhibited remarkable two-photon (TP) characteristics, which first realized real-time monitoring of the lipid droplet multidynamics process, diagnosing nonalcoholic fatty liver disease (NAFLD) and inflammation in living mice via TP fluorescence imaging. It is found that the as-prepared lip-YB provides a new avenue to design lipid droplet-specific imaging probes, clarifies its roles and mechanisms in cell metabolism, and can timely intervene in lipid droplet-related diseases during various physiological and pathological processes