286 results about "Technology analysis" patented technology

The invention discloses a construction site safety risk intelligent early warning system and method based on BIM and big data analysis, relates to the technical field of building engineering construction safety, and solves the problem that it is difficult to transmit construction site multi-source data which is collected and preprocessed in real time in real time and carry out space mapping with a BIM model. A rule engine is difficult to carry out initial early warning; a machine learning model is difficult to analyze time series data, predict collapse risks and identify dangerous behaviors; a risk prediction model is difficult to construct and is difficult to integrate into a BIM model; and pushing and closed-loop management are difficult to carry out on the risk early warning information. According to the method, the multi-source data is collected at the construction site, the digital twinborn scene is constructed by mapping the multi-source data to the BIM model by means of space-time alignment, the multi-source data is analyzed and processed by applying technologies such as a rule engine and a machine learning algorithm, and the result is integrated to the BIM model, so that visual risk monitoring and early warning are realized.

The invention discloses a forest defense method and system based on digital twin and intelligent sensors, and the method comprises the steps: collecting and monitoring environment data, and carrying out the integration processing through data fusion; a big data analysis technology is adopted to analyze a potential risk area where a fire occurs, a high-risk area is identified by evaluating a risk level, and a fire spreading trend is predicted; constructing a three-dimensional virtual model of the forest region by adopting a digital twinborn technology; the spreading conditions of the fire at different time and spatial positions are simulated; according to the predicted fire spreading trend, when a potential fire risk is detected, an early warning mechanism is started; automatic generation and screening of the emergency plans are realized, and the emergency plans are generated. The invention provides an emergency protection system integrating the functions of multi-source sensing data fusion, intelligent early warning analysis, dynamic emergency plan generation and the like. In addition, the problem that a traditional emergency plan is static and cannot be dynamically adjusted according to actual conditions is solved.

Disclosed are examples of a computer implemented screening system for large language models (LLMs). The disclosed examples are designed to address technical issues and other concerns associated with LLMs by conducting automatic screening on the input and output of these models. In some variations, the described technology is directed to a system for analyzing the accuracy of large language models (e.g., ChatGPT) in performing textual math word problems. Users input word math problems into the LLM, the output is then analyzed by the proposed technique and the user is given a score of how accurate the LLM response is.

The invention is suitable for the technical field of recruitment portraits, and provides a recruitment talent portrait generation method and system based on multi-dimensional information, and the method comprises the following steps: collecting the resume information of candidates; performing data cleaning and data conversion on the resume information; performing feature extraction, analyzing candidate online behavior data based on a natural language processing technology, and extracting behavior patterns and interest points; performing model training and evaluation based on historical resume data to obtain a talent portrait model; the resume information, the behavior modes and the interest points of the candidates are input into a talent portrait model, candidate portraits are obtained, and the candidate portraits comprise skill ability evaluation, occupational development potential and culture adaptation degree; and providing personalized vocational development suggestions and post matching recommendations according to the candidate portraits. According to the method, through feature extraction and machine learning model training, a detailed talent portrait is generated, the scientificity of recruitment decision making is improved, suitable candidates are rapidly identified, and vocational suggestions are provided for the candidates.

The invention provides an analysis method and system fusing a large model and a knowledge graph technology, and relates to the field of deep learning, and the method comprises the steps: firstly extracting the text content of an industrial technology related original file from a patent database, an academic literature library and a policy file, and constructing an industrial technology knowledge graph according to the text content; then, relevant information nodes are positioned through an industrial technology knowledge graph to extract industrial technology query matching content, significant semantic interaction analysis is carried out by utilizing a large language model, and then a question answer text matched with user consultation is generated by utilizing an intelligent answer robot based on the large language model; therefore, semantic interaction analysis and automatic answer generation are carried out on industrial technology related files and user consultation questions, more personalized and comprehensive question answering texts related to industrial technology analysis are provided for different user consultation questions, and the performance of a question answering system is improved.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention discloses an extreme low water change attribution and low water resource potential evaluation method, which belongs to the technical field of water resource calculation, and comprises the following steps of: obtaining raster data of runoff and driving factors of the runoff in a research area, performing data preprocessing, deducing the frequency distribution of runoff depth of each grid unit by adopting a non-parametric kernel density estimation method, and calculating the runoff depth of each grid unit; and counting the proportion of the grids meeting the guarantee rate runoff depth threshold in each year in the total grid, and further determining a low water typical year under each guarantee rate. A nonlinear regression simulation model of coupling optimization deep learning is adopted to carry out extreme low-water runoff depth regression simulation research under different driving factor combinations, and a key driving factor combination influencing low-water runoff depth change is mined based on a regression simulation precision result; and analyzing the constraint effect of each key driving factor on the extreme low water and a possible threshold value by adopting a constraint line deduction technology, and evaluating the low water resource potential of a typical year of the extreme low water.

The invention relates to the technical field of teaching quality evaluation, and particularly discloses a teaching quality comprehensive evaluation system and method based on a teaching sequence perspective, and the system comprises a data management module, a learning sequence analysis module, a teaching sequence analysis module, a teaching quality evaluation module, a feedback improvement module, and a decision support module. The data management module is used for collecting data generated in an education and teaching process, including learning sequence data of students and teaching sequence data of teachers, and performing arrangement and standardization processing on the collected data; the learning sequence analysis module is used for analyzing learning sequence data of the students through a data mining technology; according to the invention, by integrating the student learning sequence data and the teacher teaching sequence data and combining a data mining technology and a deep learning algorithm, key factors influencing the teaching quality are accurately identified, the teaching quality is objectively evaluated in a data-driven manner, the deviation of manual evaluation is avoided, and the teaching quality is accurately evaluated. And the transparency and fairness of teaching quality evaluation are enhanced.

The invention discloses a water conservancy project building full life cycle management method based on BIM technical analysis, and relates to the technical field of building full life cycle management. And respectively constructing three structural indexes, namely a microcrack expansion rate index F1, a damp-heat permeation combined degradation index F2 and a dynamic stiffness phase deviation index F3. The multi-dimensional feature system not only covers key risk sources such as material microcosmic degradation, environmental coupling effect and mechanical response mutation, but also breaks through a traditional extensive method which depends on single sensing data to perform health judgment, and provides a digital expression model with physical interpretation force for structural risks. According to the method, a unified health data standard system is established, and the health data standard system can be used as a core data source of upper-layer applications such as dynamic structure evaluation, an early warning model and visual mapping.

The invention relates to a man-machine interaction dialogue method, system and device based on a natural language and a medium. The method comprises the steps that firstly, multi-modal interaction data is acquired and preprocessed, and segmented words and syntax are analyzed through a natural language processing technology to construct intention feature vectors; combining the intention feature vector with a historical dialogue record, and using a pre-trained language model to generate context semantic elements containing a semantic relationship; if the context semantic elements are matched with the preset scene feature library information, predicting a user intention change trend by adopting a reinforcement learning model to obtain an intention prediction result; and finally, evaluating user intention change based on an intention prediction result, extracting associated domain knowledge by utilizing a knowledge graph if significant change occurs, and inputting the associated domain knowledge into a dialogue generation model to obtain a natural language reply sequence. According to the method, the understanding precision of the user intention is improved, the dynamic prediction of the intention change is realized, the relativity and coherence of reply are guaranteed, and a more efficient processing path is provided for natural language man-machine interaction.

The embodiment of the invention provides a multi-source integrated energy system energy-saving optimization method based on data mining, and the method comprises the steps: obtaining the real-time operation data of a multi-source integrated energy system, and the real-time operation data comprises the energy demand, supply, environment parameters and equipment state information; utilizing historical operation data and real-time data to construct an energy demand and supply prediction model; analyzing errors of the prediction model by using a data mining technology, and identifying key factors influencing prediction accuracy; adjusting prediction model parameters according to the key factors to improve prediction precision; formulating a scheduling strategy of the multi-source integrated energy system based on the optimized prediction model; and implementing and evaluating the energy-saving effect of the scheduling strategy, and optimizing system operation. Through the scheme of the embodiment of the invention, the problem of prediction accuracy caused by energy demand fluctuation and supply instability in a dynamic operation environment can be solved.

The invention relates to the technical field of recommendation systems, in particular to an AI-based personalized tourism recommendation system, which comprises a video data analysis module for analyzing and monitoring video data on the basis of an image recognition technology, detecting a tourist dense area in a scenic area, converting video image data into a scenic spot people flow density index, and sending the scenic spot people flow density index to a recommendation module; and then matching the scenic spot people flow density index with an actual map position through a geographic information system, and generating people flow data in a positioning area. According to the invention, through integration of real-time image identification and a geographic information system, rapid measurement of the scenic spot people flow density is realized, the data analysis speed and precision are improved, such optimization improves the people flow monitoring efficiency, effectively prevents congestion, allows real-time adjustment of management strategies, and the system accurately analyzes the user behavior and predicts the behavior mode, thereby improving the user experience. And meanwhile, the time sequence analysis technology is utilized to predict the people flow change, scientifically adjust the travel route and time, optimize the resource allocation and enhance the operation rationality.

The invention discloses an enterprise digital operation real-time data analysis method based on multi-source data fusion, relates to the technical field of data processing, and solves the problems that firstly, an enterprise comprehensive benefit index is difficult to analyze and predict; secondly, it is difficult to embed the knowledge graph into a vector space to construct a dynamic graph neural network; then, the equipment abnormal offset, the supply chain abnormal offset and the customer activeness are difficult to analyze, a risk matrix is constructed, and a comprehensive benefit index decline propagation path is identified; service link resources are difficult to dynamically allocate according to an analysis result of the risk matrix; and finally, a data analysis result is difficult to display visually. According to the method, enterprise multi-source data is collected and preprocessed, a three-dimensional vector space and a dynamic graph neural network are constructed, enterprise benefits and risks are analyzed and predicted in combination with multiple technologies, resources of business links are dynamically allocated, and a data analysis result is visually displayed.

The invention relates to the technical field of food detection, and discloses a food pesticide residue safety detection analysis method based on machine learning, which comprises the steps of food sample collection, extract pretreatment, two-dimensional liquid chromatography-high resolution mass spectrometry combined technology analysis, mass spectrum acquisition, mass spectrum numerical data conversion, data correction and machine learning model establishment. The method comprises the following steps: identifying and classifying pesticide residue components, extracting a pretreatment extract, analyzing a to-be-detected sample, acquiring a mass spectrum, converting the mass spectrum into to-be-detected mass spectrum numerical data, correcting the data, inputting a model, identifying and classifying pesticide residues, quantitatively analyzing, comparing food safety standards, judging conformity and generating a detection report. The invention aims to improve the accuracy, sensitivity and efficiency of pesticide residue detection. Meanwhile, through technical correction and optimization, the problems of environmental interference, data deviation and the like faced by an existing method are effectively solved.

The invention discloses a technology development analysis method and system based on a patent big language model, and relates to the field of big data analysis. The problems that an existing artificial intelligence patent analysis method is difficult to accurately analyze a technical structure in a patent text, cannot describe a technical hierarchical relationship, is difficult to eliminate technical semantic diversity, cannot systematically analyze a technical evolution process and the like are solved. And the network structure characteristics and the technology node importance are analyzed, and the evolution characteristics of the technology system in different stages, the key technology nodes and the change trend of the technology community structure can be identified, so that technical support is provided for artificial intelligence technology development situation research and judgment and technology layout decision. The method is suitable for a technical analysis scene of million-level patent data.

The invention discloses an intelligent operation and maintenance method and system for an energy management platform, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: analyzing data through employing an NLP technology, generating a multi-objective optimization function, constructing a user behavior portrait, adjusting the weight parameters of the multi-objective function through employing a reinforcement learning algorithm in combination with an Actor strategy network, and carrying out the operation and maintenance of a user. An energy scheduling scheme is obtained by using an improved bald eagle search algorithm and a prey escape energy mechanism, a digital twinborn model of mapping of a physical space and a virtual space is constructed, and the energy scheduling scheme is optimized and implemented by combining the digital twinborn model with a soft constraint mechanism. Through fusion of NLP technology semantic analysis and a reinforcement learning algorithm, a multi-target scheduling strategy is dynamically optimized, and an improved bald eagle search algorithm and a digital twin model are combined to realize simulation verification and closed-loop feedback, so that the intelligence and optimization level of energy system operation is improved, and the stability, economy and low-carbon operation capability of the system are also improved.

The invention provides an online examination cheating behavior detection method and system, and relates to the technical field of examination behavior detection, and the specific steps are as follows: collecting answer texts of examinees in real time, analyzing the answer texts of the examinees by using an NLP technology, and calculating the plagiarism probability of the answer texts by comparing the texts with data in a database; monitoring the network activity of the examinee, analyzing the network behavior mode of the examinee, and calculating the abnormal behavior mode index of the examinee; physiological data of examinees are collected, and psychological stress indexes of the examinees are calculated; calculating a comprehensive evaluation index by using weighted average, comparing the comprehensive evaluation index with an examination cheating threshold value, and judging that the behavior is cheating if the comprehensive evaluation index exceeds the threshold value; according to the invention, the behaviors of examinees are monitored in all directions, and a three-dimensional cheating detection system is provided; the artificial intelligence technology is applied, answer texts, network activities and psychological stress indexes are automatically analyzed, dependence on manual invigilation is reduced, and the monitoring efficiency and accuracy are improved; and cheating behaviors can be found in time.

The invention discloses a knowledge graph construction method based on data assets, and relates to the technical field of artificial intelligence and knowledge management, and the method comprises the steps: collecting data assets, employing an adaptive data preprocessing technology, automatically recognizing and cleaning different types of data assets, and carrying out the fusion of multi-modal data through deep learning. Constructing an inference engine based on semantic understanding, analyzing data context by using a natural language processing technology, deducing an implicit relationship between data, and enhancing a relationship between entities in the atlas; the method comprises the following steps: deducing missing information in a knowledge graph through knowledge reasoning and a completion algorithm of a graph neural network, generating an interpretable knowledge graph, increasing the credibility of a user to the knowledge graph by recording a derivation process of a node and a relationship between nodes, and recording an updating process of the knowledge graph by utilizing a block chain technology. According to the method, the overall richness and accuracy of the knowledge graph are improved, the credibility and use convenience of the user to the knowledge graph are enhanced, and the information integrity and accuracy of knowledge are improved.

The invention relates to the technical field of photovoltaic energy storage, and provides a photovoltaic energy storage equipment remote AI monitoring method and system based on a management platform, and the method comprises the steps: obtaining an operation signal of energy storage equipment, carrying out the analysis and processing of the operation signal, and obtaining signal feature data and a signal abnormality index; performing trend analysis on the signal characteristic data to obtain a trend analysis result, and verifying the signal abnormal index by using the trend analysis result to obtain an abnormal verification result; inputting the abnormal verification result into a preset energy storage management system for analysis to obtain preliminary diagnosis information, and analyzing the preliminary diagnosis information to obtain a potential fault; and generating a corresponding fault alarm signal according to the potential fault, and generating a corresponding fault processing suggestion based on the fault alarm signal. By combining artificial intelligence and a data mining technology to analyze feature data and abnormal indexes of operation signals, the problems that data analysis is not intelligent enough and fault diagnosis precision is insufficient in practical application are solved.

The invention relates to the technical field of workflow construction, in particular to an AI workflow construction method and system based on image recognition and electronic equipment. The method comprises the following steps: recording a working process by using a video recording tool, collecting operation data, analyzing video content and extracting key frames and operation steps by adopting image recognition and natural language processing technologies, and performing time sequence division and behavior analysis on operation behaviors in combination with user operation event data so as to determine actual operation steps. The method comprises the following steps: generating a detailed step list with alliance tag information, then combining operation steps with video segments according to an analysis result to form a structured work flow, adding detailed description and annotation information for each step, and finally establishing an index structure, so that a user can quickly retrieve a required business flow video or document through keywords. According to the method, the visualization degree and the understandability of the business process can be improved, the learning cost is reduced, and the working efficiency is improved.

The invention discloses a hotspot technology analysis system and analysis method, and belongs to the technical field of information processing. The system comprises an intelligence resource database which stores a plurality of documents to be subjected to hotspot technology analysis; the hotspot technology keyword extraction module is used for extracting a plurality of keywords related to the hotspot technology from the literature; the hotspot technology association degree analysis module is used for determining association values between the hotspot technology and other hotspot technologies; the hotspot technology importance degree analysis module determines an academic influence value of the hotspot technology; the hotspot technology difficulty degree analysis module determines the technology complexity of the hotspot technology; and the hotspot technology merging and sorting module is used for merging the hotspot technologies meeting a first preset condition into the same technology group and sorting the hotspot technologies in the same technology group. The system can more scientifically and reasonably reflect the internal logic connection on the technical layout.

The invention discloses a nursing decision support system and a nursing decision support method, and relates to the field of health care, in the system, a data acquisition module is used for acquiring multi-source data in real time; the data processing module is used for integrating multi-source data by adopting a natural language processing technology and a data mining technology to obtain a unified data set; the graph construction module is used for taking each type of data in the unified data set as a knowledge node, analyzing association relationships among the knowledge nodes by adopting a data mining technology, endowing each association relationship with a weight value, and constructing a knowledge graph according to the knowledge nodes, the association relationships and the weight values; the dynamic updating module is used for regularly updating the knowledge graph according to the currently acquired multi-source data; the decision support module is used for calling the current knowledge graph to generate nursing measures of the patient, comprehensive, accurate and real-time nursing decision support can be provided, and the nursing experience and nursing quality of the patient are improved.

The invention discloses an airport aircraft variable anti-collision protection area construction early warning method and system and electronic equipment. The method comprises the steps that S1, a multi-level anti-collision protection area basic standard model of an aircraft is constructed in an airport map model; s2, collecting motion sequence data of the aircraft in a time period t1 before the current moment, and inputting the motion sequence data into the LSTM data prediction model to predict motion sequence data of the aircraft in a time period t2 after the current moment; s3, extracting motion sequence data of the time period t1 to adjust the sizes of the emergency collision avoidance area, the early warning monitoring area and the adjacent risk area; collecting the vehicle speed in the vehicle motion sequence data and the speed and course data in the motion sequence data in the time period t2 to calculate the relative distance and the relative speed of the vehicle and the aircraft; and S4, the anti-collision early warning system judges and predicts whether falling into an adjacent risk area, an early warning monitoring area and an emergency collision avoidance area. According to the method, prospective anti-collision technical analysis is realized, and the airport safety operation margin is improved.

The invention belongs to the field of medical technology analysis, and discloses an analysis method and system for revealing a hidden binding pocket of a drug target, and the method comprises the steps: firstly obtaining a representative conformation metastable state of a target protein through conventional molecular dynamics simulation and clustering analysis; secondly, constructing a Markov state model to analyze a dynamic transformation rule between conformations; carrying out enhanced sampling by adopting meta-dynamics, and deeply exploring a rare conformation space; and finally, constructing a free energy landscape to quantitatively evaluate the relative stability of the conformation, and identifying a hidden binding pocket in the stable rare conformation. According to the method, the limitation of a single calculation means is overcome, a full-chain calculation system of dynamic conformation analysis-hidden cavity feature mining-novel ligand rational design is constructed, and the formation mechanism and potential druggability of the hidden pocket can be comprehensively revealed from the two dimensions of dynamics and thermodynamics; and an efficient and accurate calculation framework is provided for research and development of innovative drugs targeting difficult drug targets.

The invention relates to the technical field of intelligent testing, and discloses a software test task management method and device based on an intrusive instruction knowledge base, equipment and a medium, and the method comprises the steps: analyzing an obtained demand card based on a large model technology, analyzing a semantic role of a vocabulary in the demand card through a semantic role labeling technology, and obtaining a semantic role of the vocabulary in the demand card; extracting test requirements and test points of semantic roles of vocabularies in the requirement cards; querying test information matched with the test requirements and the test points in a knowledge base of the software project, and determining the test information with the highest matching degree as target test information; in combination with the demand card and the target test information, automatically generating a test scheme of the current version based on a large model technology; and according to the generated test scheme. The method can be applied to the development of business systems such as financial science and technology, medical health, old-age care and the like, and the efficiency of the test work is remarkably improved by automatically analyzing the demand card, generating the test scheme and arranging the test task.

The invention relates to the technical field of low-altitude aircraft attitude control, in particular to a low-altitude aircraft attitude control system based on intelligent information data analysis. The method comprises the following steps: a data acquisition and storage unit acquires aircraft data and environmental parameter data; the intelligent information analysis unit analyzes the real-time attitude of the aircraft by using a nonlinear dynamics technology and a singular perturbation theory technology, and judges whether the aircraft is in a safe state or not by using a singular perturbation analysis technology; the single aircraft control unit generates an optimal control strategy by using a linear quadratic regulator in combination with an advanced attitude control algorithm; the multi-aircraft control unit adopts a distributed control strategy to control coordinated flight among the aircrafts; and the control display terminal unit is used for providing an integrated human-computer interaction interface. According to the low-altitude aircraft attitude control system based on intelligent information data analysis, nonlinear dynamics and singular perturbation theories are combined, and cooperative flight control of a single aircraft or multiple aircrafts is realized by adopting a distributed control architecture.

The invention provides a fishery resource dynamic monitoring method and system based on underwater detection equipment, relates to the field of dynamic monitoring technology analysis, and realizes fine management of water area monitoring by collecting fish school density data in real time, constructing a fish school density matrix and combining grid unit division and geographical hash coding. The gradient value of the adjacent grid units is calculated based on the density matrix, the gradient matrix is generated, the fish school density abrupt change area can be accurately recognized, the abnormal area is marked, then the sonar sampling frequency of the underwater detection equipment is dynamically adjusted, and the monitoring precision of the abnormal area is improved. Compared with the prior art, the method has the advantages that the monitoring precision, the anomaly detection response capability and the resource utilization efficiency are remarkably optimized, and the adaptability and the prediction capability of a monitoring system are improved particularly in the aspects of fish swarm migration trend prediction and grid division dynamic adjustment.

The invention relates to the technical field of business and financial fusion data security interaction scheme design based on credential creation, in particular to a business and financial fusion data security interaction method and system based on credential creation. The method comprises the steps of collecting, standardizing, storing and encrypting business and financial data, constructing a dynamic access control rule base by utilizing multi-dimensional information and a machine learning algorithm to carry out accurate access control, adding an identifier and a timestamp for interactive data, realizing data tracing through a block chain, and realizing data tracing through a block chain. And analyzing the operation log and the data traceability information by adopting a natural language processing and machine learning technology to perform exception auditing. The system comprises a data acquisition module, a standardization module, a storage module, an encryption module, a dynamic access control module and a data traceability auditing module. According to the invention, the data security and management efficiency of the business and financial fusion system are improved to a great extent, and the problems of inaccurate dynamic access control, difficult data tracing and emerging technology fusion are solved.

To provide a system.SOLUTION: The system includes: means for automatically generating a business document from an instruction; means for checking a grammatical error in the generated business document; means for checking compliance of the generated business document; means for determining whether the generated business document conforms to the standard of a business communication; analyzing the generated business document by using a natural language processing technology; and means for repairing the business document on the basis of the result of analysis.SELECTED DRAWING: Figure 1

The invention relates to the technical field of low-code development platforms, in particular to a simulation process testing method based on a low-code platform, which comprises the following steps: based on the low-code platform, adopting a K-means clustering algorithm and a decision tree classification method to analyze historical process data, performing mode and exception recognition, and generating a process mode and an exception condition. According to the method and the device, through a low-code platform and a machine learning algorithm, the flow mode and the abnormal condition can be identified more accurately, and the test efficiency and accuracy are improved. And test parameters are optimized by using reinforcement learning, and the strategy is adjusted in real time. And the subtasks are executed in parallel, and the parallel processing capability is improved by message transmission coordination. Visual tools and interfaces are provided, and the design and execution process is simplified. And a real-time editing and debugging function is realized, and the flow chart is dynamically modified. AI and NLP technologies are utilized to analyze a process model and requirements, an intelligent test strategy and result analysis are generated, and deep process insight and understanding are provided.