108 results about "Composite index" patented technology

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

The invention belongs to the field of pollution prevention and control data processing, and particularly relates to a laboratory instrument data automatic acquisition and quality tracing system and a laboratory instrument data automatic acquisition and quality tracing method. A distributed search traceability mapping space is constructed through the storage mapping module to realize space-time-semantic composite indexing and classification detection of data, and a causal reasoning model of the quality judgment module is used for performing anomaly judgment; a reverse Hash traceability mechanism is innovatively designed, an index-traceability node graph and a segmented Hash heterogeneous node anchoring strategy are combined, Hash head, body and tail fragments are stored in three types of nodes of acquisition, storage and detection, and tamper-proof traceability of abnormal data and dynamic optimization of acquisition parameters are realized through cross-node aggregation verification; according to the invention, the problems of low data credibility and poor abnormal traceability efficiency in ecological environment monitoring are solved, and full-chain closed-loop management of data acquisition-analysis-traceability-acquisition configuration adjustment is supported.

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention discloses a dam safety monitoring data effective information extraction method and system based on adaptive time sequence decomposition. The method comprises the following steps: firstly, obtaining a dam single-measuring-point monitoring time sequence; adopting a multi-population Jaya algorithm to adaptively optimize control parameters of variational mode decomposition (VMD); performing non-recursive decomposition on a data sequence by using the optimized VMD, and screening high-frequency and medium-and-low-frequency sub-modals through a correlation coefficient-energy entropy composite index; superposing the high-frequency sub-modes to form a high-frequency sequence, constructing a composite threshold in combination with kernel density estimation and a box graph method, and eliminating local singular values in the high-frequency sequence to generate a residual sequence; and finally, superposing and reconstructing the low and medium frequency sub-modes and the residual high-frequency sequence to realize effective information extraction of the monitoring data. According to the method, the feature extraction precision of dam monitoring data is remarkably improved through parameter adaptive optimization and composite criteria.

The invention, which relates to the technical field of power grid stability testing, discloses a stability boundary evaluation method for a network-following type and network-constructing type hybrid wind power plant, comprising the following steps: S1, collecting system data of the network-following type and network-constructing type hybrid wind power plant in real time, and calculating a composite index by using the collected data; s2, setting a critical threshold value, performing preliminary evaluation on the overall stability of the system by using the critical threshold value and the composite index, and if the preliminary evaluation result is stable, entering the step S3; s3, outputting fast, medium and slow scale index characteristics in real time by using a multi-time scale monitoring module, and calculating multi-scale indexes by using the fast, medium and slow scale index characteristics and a dynamic weight distribution mechanism; and S4, performing secondary judgment on the stability of the system by using a multi-scale index. According to the method, a collaborative decision-making mechanism based on a composite stability index and multi-time scale monitoring is adopted, namely, a double-layer criterion system is adopted, the dimension limitation of a single criterion is broken through, and the identification precision of the instability critical point of the hybrid wind power plant is remarkably improved.

The invention belongs to the technical field of data processing, and discloses a book personalized recommendation method based on big data analysis. The recommendation method comprises the following specific steps: S1: data integration and cleaning; S2: data storage and index optimization; S3: personalized recommendation algorithm design; S4: keyword interaction mechanism; according to the invention, through deep cooperation of data storage optimization and recommendation algorithm design, double breakthrough of performance and precision is realized; a B + tree joint index of MySQL enables a collaborative filtering algorithm to obtain a user borrowing sequence at a millisecond level and support time decay weighted calculation, and a MongoDB composite index compresses response time of community theme extension recommendation, so that recommendation generation time consumption is reduced through cooperation of the B + tree joint index and the MongoDB composite index, and a concurrence scene of thousands of people can be easily coped with.

Systems and methods of determining composite indices such as, e.g., health, safety, and performance indices, of an enclosure from weighted values of contributing metrics including sensor data and / or controlling one or more building systems based on the composite indices.

The invention relates to the technical field of medical data mining, and particularly discloses an elderly sepsis index model, a prediction model construction method and a prediction system.The method comprises the steps that a development queue data set and an independent verification queue data set of elderly sepsis patients at the age larger than or equal to 60 years old are determined according to an intensive care database; performing sepsis death and prognosis importance sorting on candidate inflammation-immune composite indexes of the development queue data set according to a total correlation feature selection algorithm; superposing and combining preset basic indexes, each index in the importance inflammation marker indexes and a preset importance scoring standard; comparing the scoring results of the multiple groups of joint optimization index models, and evaluating the scoring results; and determining the joint optimization index model with the optimal evaluation result as the senile sepsis index model. According to the construction method of the senile sepsis index model provided by the invention, the index model suitable for senile sepsis can be obtained based on the existing index database.

The embodiment of the invention provides a big data index management method and system based on a weak model, and the method comprises the steps: selecting a data source based on a business scene, writing an SQL statement, recognizing dimension information and index information based on the SQL statement, and writing the dimension information and the index information into an index table, correspondingly writing the SQL statement and the index table into a distributed column database; receiving a query request of the user-defined index, querying SQL statements based on parameter key information of the query request in combination with basic information of the user-defined index, assembling the SQL statements, generating user-defined SQL statements, and performing statement optimization on the user-defined SQL statements; index codes are created according to the dimension information and the index information, detail indexes are generated based on the index codes, synchronization parameters of the detail indexes are set, and the detail indexes comprise basic indexes, derivative indexes and composite indexes.

The invention discloses a cracking zone tunnel blasting construction safety risk grading evaluation method and system, and belongs to the technical field of tunnel engineering safety monitoring. According to the method, dimensionality reduction and conflict resolution are performed on the original indexes by adopting the PCA-EWM method, and the mutually independent composite indexes among the indexes are obtained, so that the independence and effectiveness of the indexes are improved, and the problem that the final evaluation result is inaccurate due to the fact that the information among the original indexes is overlapped and the weight distribution is inevitably caused by repeated distribution is avoided; the weight is dynamically adjusted through the offset of the criterion layer so as to adapt to the complex engineering environment, and it is ensured that the evaluation result can reflect the current risk state; due to the fact that the composite index of tunnel blasting and the construction risk degree are in a nonlinear relation, a normal cloud model is adopted to reconstruct a membership function and process nonlinear risk evolution of the index so as to better adapt to nonlinear characteristics of the tunnel blasting construction data of the cracked zone, the accuracy of risk assessment is improved, and the safety of construction is guaranteed.

The invention provides a reservoir sediment modeling method based on multi-time-sequence multi-beam sounding point cloud data, and relates to the field of reservoir sediment monitoring and modeling, and the method comprises the steps: constructing a reservoir area space grid skeleton model, and collecting multi-time-sequence multi-beam point cloud data and corresponding hydrological auxiliary data; performing space division by adopting an adaptive tile subdivision method, generating multi-level point cloud tiles by adopting a feature point downsampling method, and establishing a three-dimensional composite index; the indexed multi-level point cloud tile set is processed through an anisotropic ICP registration method, and a point cloud tile set of multi-time-sequence registration is obtained; establishing an initial triangulated irregular network sediment model, carrying out differentiated partitioning by utilizing historical hydrological auxiliary data, and carrying out dynamic updating based on a partitioning state to obtain a dynamic reservoir sediment model; and S5, calculating to obtain deposition parameters, and mapping the deposition by adopting a three-dimensional engine to obtain an interactive three-dimensional dynamic deposition situation map. According to the invention, high-precision dynamic visualization of the reservoir sediment deposition process is realized.

The invention discloses a dynamic SQL (Structured Query Language) optimization method based on consanguinity dependency, and relates to the technical field of database query optimization. The method comprises the following steps: analyzing all indexes in a database, and dividing the indexes into atomic indexes and composite indexes; a blood relationship dependency tree is constructed according to the mathematical relationship between the indexes, each node represents one index, and each directed edge represents the dependency relationship; receiving a to-be-queried index set, and recursively searching all dependent target atomic indexes based on the graph structure; grouping the target atomic indexes according to data sources, and generating corresponding SQL query statements; and executing the SQL statement, and calculating a final query index result after obtaining an atomic index value. According to the invention, through unified management of the index dependence structure, repeated calculation across indexes is eliminated, the query performance is improved, the resource consumption is reduced, and the index development and maintenance efficiency is improved.

The embodiment of the invention provides a rail transit 3D data defect detection method and system based on spatial indexing, and the method comprises the steps: collecting rail 3D original data, carrying out the preprocessing, and unifying the data into a unified rail coordinate system; dividing the preprocessed track 3D data into a plurality of regular grid units as space units according to spatial positions, extracting multi-dimensional features of data in each space unit, establishing a composite index for each space unit, and storing the composite index in a space-time database; after the acquired 3D data of the current track are subjected to identical processing, spatial position areas corresponding to the 3D data of the current track and historical data are quickly matched through a composite index, and a difference value of multi-dimensional features is calculated; dynamically adjusting the defect judgment threshold of each dimension according to the statistical distribution of the difference values, judging the defect type and severity of the region meeting the threshold, and generating a detection report. The problems that traditional 3D data processing is low in efficiency and long in comparison time consumption are solved.

The invention relates to the technical field of industrial data management, in particular to a supply chain data real-time integration processing method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining an external compliance data document, calling a document layout analysis model based on a multi-modal converter, extracting a text semantic vector, fusing the text semantic vector with a two-dimensional space coordinate feature to generate a composite index key, and carrying out the real-time integration processing of the supply chain data. Executing clustering analysis to recognize the text blocks and generating structured metadata; carrying out discretization processing on the business transaction data flow, and constructing an associated database with production environment and logistics data as attributes; executing cross-modal entity alignment by using the structured metadata, and establishing a bidirectional pointer link between the unstructured document index and the associated database; receiving a query vector, executing a recursive traversal algorithm based on a bidirectional pointer link, calculating a cascade influence probability as a correlation sorting score, and outputting a retrieval result; through multi-modal feature fusion and cross-modal entity alignment, deep integration and risk linkage retrieval of supply chain heterogeneous data are realized.

The invention discloses a foreign language teaching effect evaluation method and system based on artificial intelligence. The method comprises the steps of data acquisition, voice feature extraction, multi-modal fusion evaluation modeling and teaching effect evaluation. The invention relates to the technical field of teaching effect intelligent evaluation, in particular to a foreign language teaching effect evaluation method and system based on artificial intelligence. According to the scheme, a pronunciation definition composite index is constructed through formant difference analysis, the acoustic semantic coupling degree is calculated, and the phoneme transfer stability is evaluated; the definition, the accuracy and the continuity of the voice can be accurately quantified in a multi-noise environment; according to the scheme, modal attention is introduced, full-interconnection cross-modal cross attention is utilized to realize multi-modal information bidirectional interaction, a complementary gating mechanism is innovatively introduced to adaptively adjust a modal fusion proportion, a light quantum network is combined to generate a local score, a meta-decision network dynamically distributes weight, and finally end-to-end optimization is carried out through a differential weighting strategy. And teaching effect evaluation of fineness and accuracy is realized.

The invention discloses an efficiency monitoring system for a large model training cluster. The efficiency monitoring system comprises a training data acquisition module, a back-end processing module and a client, the training data acquisition module comprises a monitoring hook submodule and a database; the monitoring hook submodule intercepts an import request of a core module of a training task by rewriting a Python module finder, and installs a monitoring hook in a target training process; the monitoring hook collects corresponding data in real time and stores the data into a database; the back-end processing module acquires various monitoring data acquired by the data acquisition module through the data acquisition module interface access layer, and calculates various composite indexes related to training efficiency based on the preprocessed monitoring data through a high-order index calculation engine. According to the invention, real-time monitoring and non-intrusive deployment can be realized, and data integrity can be ensured.

The invention discloses an urban credit monitoring analysis method and device and a medium, and the method comprises the steps: carrying out the analysis of collected standard urban credit monitoring data according to a preset assessment index specification, and determining a basic index of urban credit monitoring; according to the basic indexes, constructing a monitoring index library of urban credit, and determining to-be-optimized indexes in the monitoring index library through data comparison; combining the indexes to be optimized through a combination number combination algorithm to generate a composite index; and analyzing the composite index through a pre-trained neural network model, generating urban credit monitoring knowledge, and accessing the urban credit monitoring knowledge to a knowledge base through a knowledge base API to construct an urban credit monitoring knowledge base. According to the method, the urban credit monitoring knowledge base is constructed, so that the difficulty of acquiring the urban credit monitoring data by the user is greatly simplified, the accuracy and efficiency of urban credit monitoring analysis are effectively improved, and the actual demand and benefit of the customer are met to the maximum extent.

The application provides a deep rock mass multi-field coupling test data-driven intelligent grading method, relates to the technical field of deep rock mass grading, and constructs a multi-field coupling mechanism database through deep rock mass multi-field coupling test data, refines intelligent characteristic indexes representing multi-field coupling effects from the database, the intelligent characteristic indexes include traditional indexes and composite indexes, the composite indexes are determined based on geological environment parameters of the deep rock mass, the intelligent characteristic indexes are taken as inputs, corresponding deep rock mass grades are taken as outputs, a machine learning model is trained, a deep rock mass intelligent grading model under different geological environment parameters is obtained, intelligent characteristic indexes of deep rock mass to be graded in an engineering site and geological environment parameters thereof are acquired, grading is performed through the deep rock mass intelligent grading model matched with the geological environment parameters, and the grade of the deep rock mass is obtained, so that the problem of low accuracy of existing deep rock mass grading is solved, and the application is suitable for deep rock mass grading.

The invention discloses a virtual measurement method and device, equipment, a storage medium and a product, and relates to the technical field of computers, and the virtual measurement method comprises the steps that multi-time-scale abstract processing is conducted on process parameters and production data sequences obtained online, and a multi-scale time sequence group is obtained; based on a preset virtual measurement model, virtual measurement is carried out on the multi-scale time sequence group, a key parameter measurement result is obtained, and the virtual measurement model is obtained by adopting a reinforcement learning mode to carry out virtual measurement training on a preset time sequence data sample. According to the method, the multi-scale time sequence group of the online time sequence characteristics is extracted, virtual measurement is performed on the online time sequence characteristics by using the trained virtual measurement model, the key parameter measurement result of the composite index is obtained, accurate prediction of the composite index is realized, and the accuracy of the prediction result is improved. Therefore, the problem that comprehensive monitoring of the composite indexes is difficult to achieve in precise laser cutting through a traditional method is solved, and the prediction accuracy is improved.

The invention discloses a time series data analysis method and device, a storage medium and electronic equipment, and relates to the technical field of the Internet of Things. By establishing a composite index structure combining a time index and an equipment index, under the composite index structure, corresponding index entries are dynamically created for a target data flow, the framework of the composite index structure is not changed, and the time series data analysis efficiency is improved. The content is dynamically filled along with the data, and finally a complete composite index result capable of being efficiently retrieved is formed. And the time index adopts a B-Tree structure, so that data in a specific time range can be quickly positioned. Equipment indexing is realized based on a hash table, data generated by specific equipment can be efficiently retrieved, the throughput bottleneck of mass data storage is broken through, and second-level writing and aggregation query of ten millions of data are realized. And in combination with artificial intelligence AI, distributed time series data management and an Internet of Things multi-source data fusion technology, in the face of massive time series data, the analysis processing capability of the time series data is improved to meet the demand of time series data analysis.

The invention discloses a method and equipment for processing meteorological grid point data based on a database, and the method comprises the steps: analyzing original meteorological grid point data, and extracting metadata information; the metadata information is grouped and coded according to at least two dimensions, and binary bit streams corresponding to coded values obtained after information of each dimension is coded serve as input data; starting from the most significant bit of the binary bit stream, sequentially extracting one bit from each dimension according to a preset sequence for interlacing, and generating a primary key corresponding to the original meteorological grid point data; the main keys are written into a database for storage, and the database sorts the main keys according to the sizes of the main keys. According to the method provided by the invention, through a multi-dimensional joint index construction method, efficient and flexible support for multi-dimensional range query of the type is realized, complex multi-dimensional range query is converted into efficient one-dimensional continuous interval scanning of a data storage engine, and the meteorological service data retrieval performance is improved.

The application provides a multi-source Parkinson's disease database integration method based on LEDD-UPDRS composite index alignment, and is applied to the technical field of data processing. In view of the heterogeneous problems of multi-source Parkinson's disease databases, the application integrates heterogeneous data of PPMI, PDBP and local queues, disassembles characteristics according to four modes of heredity and clinic, introduces a standardized term library and a BERT-BiLSTM-CRF model unified term adjusted in combination with medical corpus, generates a cross-library feature table; a storage architecture is constructed based on FlashROM mode to realize data storage and retrieval; a composite observation system is built with LEDD and UPDRS-III, consistency is verified by multiple methods, data is standardized, missing data is filled in combination with multiple models to generate an integrated data set; a double prediction model is built relying on the data set, and the advantages of the data are realized by a Cohen's d value verification method; finally, cross-library joint analysis is carried out, phenotype-genotype correlation modeling and causal inference are completed, and data support is provided for Parkinson's disease research and clinical prediction.

The application discloses a kind of efficiency monitoring systems of large model training cluster, including training data acquisition module, backend processing module and client;Training data acquisition module includes monitoring hook sub-module and database;Monitoring hook sub-module is realized by rewriting the module finder of Python, the import request of the core module of the training task is intercepted, and monitoring hook is installed into target training process;Monitoring hook real-time acquisition corresponding data and store in database;Backend processing module accesses the various monitoring data collected by data acquisition module through data acquisition module interface access layer, and calculates various training efficiency related composite indexes based on preprocessed monitoring data through high-order index calculation engine.The application can be real-time monitored, non-invasive deployment and can guarantee data integrity.

The application discloses a foreign language teaching effect evaluation method and system based on artificial intelligence, and the content comprises data collection, speech feature extraction, multi-modal fusion evaluation modeling and teaching effect evaluation. The application relates to the technical field of intelligent evaluation of teaching effect, and particularly discloses a foreign language teaching effect evaluation method and system based on artificial intelligence. The scheme constructs a pronunciation articulation composite index through formant difference analysis, calculates acoustic semantic coupling degree, and evaluates phoneme transfer stability, so that the articulation, accuracy and coherence of speech can be accurately quantified in a multi-noise environment. The scheme introduces modal attention, realizes bidirectional interaction of multi-modal information by using full-interconnection cross-modal cross-attention, and innovatively introduces a complementary gating mechanism to adaptively adjust the modal fusion ratio. Local scores are generated by combining a lightweight network, meta-decision network dynamically allocates weights, and finally, end-to-end optimization is realized by using a differentiable weighting strategy, so that the teaching effect evaluation of fineness and accuracy is realized.

The present application discloses a data processing method, device, medium, and product based on a vector set, relating to the technical field of data processing, including: receiving a query instruction, where the composite index data includes a composite shape graph, and the composite shape graph is constructed with vector indices in the union of the vector indices of the initial vectors in a group of initial vector sets as shape nodes, and the index indication data of each initial vector set is used to indicate, in the composite shape graph, the vector indices of the initial vectors in each initial vector set; determining the index indication data corresponding to the query instruction, performing vector index retrieval in the composite shape graph according to the query vector and the index indication data corresponding to the query instruction, and generating a query result based on a group of retrieved vector indices, solving the problem of large storage overhead in the related art and achieving the technical effect of improving the utilization rate of storage resources.

The invention relates to the field of data query, and discloses a space grid data query method based on AABB and KDTree composite index, comprising the following steps: step 1, acquiring simulation data of a part, establishing a space mapping relationship between the simulation data and a geometry of a die casting, and performing AABB space division on the geometry, the AABB space including primary AABB space division and secondary AABB space division; the primary AABB space division divides the geometry of the die casting into a plurality of topologically associated sub-regions; the sub-region is divided into a plurality of tetrahedron units by the secondary AABB space division; performing binary tree structure recombination on the tetrahedron units in the sub-regions by adopting KDTree; and 2, judging whether the target coordinate point is in the sub-region or not, traversing the tetrahedron unit of the sub-region, and remarkably reducing the calculation complexity and improving the spatial query efficiency of the large-scale grid model by introducing a spatial index structure and an algorithm optimization strategy.

The invention provides a medical visual report generation method and system based on caliber standardization, and the method comprises the steps: obtaining medical data atomic indexes, and carrying out the unification of index calibers based on entity matching and dimension matching, and obtaining standard atomic indexes; based on the standard atomic index, updating the composite index by utilizing entity matching and a preset conversion template to obtain a standard composite index; defining data structure mapping between the standard index data and the visual report, and generating a standard index data interface; and calling the standard index data and a corresponding index data interface to generate a standard visual report. According to the method, atomic indexes are unified through entity and dimension matching, the consistency problem of basic data is solved, then composite indexes are updated through entity matching and a preset conversion template, and accurate control from microcosmic to macroscopic is achieved; and in a visual report generation link, efficient transmission and flexible display of data are realized by establishing data structure mapping and configuring interface information.

The invention discloses an early warning data screening method based on rule analysis and dynamic triggering, and belongs to the technical field of data early warning. The method comprises the following steps: analyzing an early warning rule configuration table, generating workdays, dynamically calculating weekly / monthly valid dates in combination with a holiday table, recursively generating annual sequence screening on natural days, and customizing and directly extracting three frequency type dynamic screening logics of a preset date; a dynamic task scheduling mechanism is constructed based on Spring Boot, data rescreening and timed early warning state check of dynamic screening logic are triggered in real time during rule change through priority improvement and thread semaphore control, and in the dynamic updating process, B-Tree single-column index, composite index and function index are adopted to optimize query efficiency. The service coverage capability, the real-time response speed and the high concurrency performance of the early warning system are remarkably improved, and the method is particularly suitable for complex scenes needing high-frequency rule adjustment such as finance and government affairs.

The invention provides an index analysis method and device, equipment and a medium, and relates to the technical field of data processing, the index analysis method and device are applied to an SAP system, the SAP system is connected with an index configuration platform, the index configuration platform is connected with a visual platform, and the method comprises the steps that a configuration set generated by the index configuration platform in response to an index analysis request initiated by a user is acquired; wherein the configuration set comprises target indexes selected by the user and configuration information of the target indexes, and the target indexes are divided into basic indexes and composite indexes; determining an index calculation result of the basic index according to the configuration information of the basic index; determining the index calculation result of the composite index according to the configuration information of the composite index and the index calculation result of the basic index associated with the configuration information; and sending each index calculation result to a visual platform for display through the index configuration platform. The problem that timeliness and accuracy of index analysis are difficult to meet enterprise requirements in an existing large-scale data environment can be effectively solved.

The invention discloses a social media data fused urban inefficient land intelligent identification method, which comprises the following steps of: realizing multi-source data space fusion by acquiring a social media geographic marking text, an urban updating policy file, a remote sensing image and basic geographic data; a natural language processing and topic modeling method is used for extracting keywords from the city update text, a topic-feature word mapping table is constructed, a RoBERTa-l arge model and a semantic rule are combined to calculate a text emotion score, and a land use negative emotion map is generated; and constructing a multi-dimensional evaluation system including indexes such as plot ratio and road network density, calculating a composite index by adopting an AMP algorithm, dynamically allocating weights through an entropy weight method, and judging the low-efficiency land. And a white list area is introduced for verification and parameter iterative optimization, and finally, an identification result is visually displayed in WebG IS and AR platforms, so that the accuracy and decision support capability of urban low-efficiency land identification are improved.