200 results about "Dynamic reasoning" patented technology

A scalable platform for orchestrating networks of specialized AI multi-agent networks that enables secure collaboration through token-based protocols and real-time result streaming with advanced dynamic chain-of-thought pruning. The central orchestration engine manages domain-specific agents, implementing sophisticated multi-branch reasoning with contribution-estimation layers that evaluate each agent's utility using Shapley value-inspired metrics. The system employs information-theoretic and gradient-based surprise metric to guide memory updates and dynamic reasoning expansion, preventing local minima stagnation while preserving valuable insights through adaptive forgetting mechanisms. The platform unifies Monte Carlo tree search with contribution-aware estimation to detect high-synergy expert combinations while maintaining privacy through partial data approaches. It scales across distributed computing environments, enabling complex collaborative tasks like materials discovery, product engineering and manufacturing process design, biomedical research, and drug development. The system supports multi-party economic rewards through systematic contribution effort, cost and importance tracking, while standardized interfaces manage security, privacy, and policy constraints across heterogeneous agents.

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention discloses a customer service data quality inspection method and device based on dynamic reasoning, equipment and a medium, and the method comprises the steps: analyzing customer service dialogue data, and generating structured dialogue data containing an intention label and a key problem node; according to an intention label in the structured dialogue data, a framework is generated through retrieval enhancement, and matched domain knowledge is recalled in real time from a standard knowledge base so as to construct a dynamic context knowledge graph; taking the dynamic context knowledge graph as input, utilizing a thinking chain prompt template to guide a large model to carry out step-by-step logical reasoning, and outputting a preliminary quality inspection conclusion chain; performing retrieval verification on assertion nodes in the initial quality inspection conclusion chain to generate a traceable final quality inspection conclusion; and based on the final quality inspection conclusion, calculating a quantitative score of the customer service dialogue data and generating a visual thinking chain report. And through dynamic retrieval of the knowledge base and logical reasoning, the accuracy, interpretability and adaptability of customer service data quality inspection are improved.

The invention provides a multi-modal intelligent medical scene dynamic reasoning method and device based on medical knowledge, and the method comprises the steps: obtaining multi-modal data of a user, and extracting the multi-modal data as multi-modal features; inputting the text modal features into a graph neural network preset with a medical knowledge graph for feature enhancement to obtain medical knowledge enhanced text features, and inputting the text modal features into a memory network stored in a user personalized health file for feature enhancement to obtain file enhanced text features, splicing the medical knowledge enhanced text features and the file enhanced text features to obtain context enhanced text features; inputting the context enhanced text features and the multi-modal features into a pre-trained multi-modal fusion model to output fusion features; and inputting the fusion features into a dynamic reasoning tree constructed by a plurality of reasoning nodes in a graph structure to determine an optimal reasoning node, and reasoning the fusion features by using the optimal reasoning node to realize dynamic reasoning adaptive to the medical scene.

The invention relates to the technical field of artificial intelligence, and discloses an AI intelligent auxiliary question answering system for student practice. According to the system, various interactive data such as texts, voices and handwritten formula images of students are collected through a multi-modal data acquisition module, semantic feature vectors of all modals are generated through a multi-dimensional feature analysis module, and then the semantic feature vectors are mapped to a unified semantic space through a cross-modal fusion module. The double-layer graph construction module constructs a domain knowledge graph and a learning behavior graph, and the dynamic reasoning and recommendation module generates a personalized problem solving path recommendation and knowledge point completion strategy based on fusion semantic representation and a graph library. In addition, the system also has the functions of interactive behavior log anomaly detection, path backtracking, knowledge forgetting curve prediction and the like. The system can comprehensively understand questions of students, provides personalized question answering service, and effectively improves the learning efficiency and knowledge mastering degree of the students.

The invention belongs to the field of data retrieval, and provides a multi-modal scientific data retrieval method and system based on abstract-assisted cognitive enhancement. According to the technical scheme, a multi-modal scientific data semantic representation model based on a large model is constructed, professional domain knowledge and a structure guiding mechanism are combined, and a multi-modal scientific data semantic representation model is constructed; generating a structured abstract containing the key knowledge; and then, on the basis of knowledge such as keywords and description information provided by the structured abstract, a multi-modal scientific data retrieval model guided by directional thinking chain multipath is constructed, multi-dimensional correlation analysis of scientific data is realized through combination of cognitive prompt knowledge enhancement and multi-step dynamic reasoning, and the data resource retrieval performance in the field of scientific data is improved.

The invention provides a vehicle active service system based on an end-side multi-modal large model and a dynamic reasoning method thereof, and belongs to the field of Internet of Vehicles, and the system comprises a multi-modal data collection module which collects multi-modal perception data of a vehicle; the multi-modal large model processing unit is used for carrying out feature extraction on the multi-modal sensing data to obtain features of different modals; the cross-modal attention fusion module is used for converting the feature data of different modals into a query, key and value triple and fusing the query, key and value triple to obtain fused features; the MCP dynamic reasoning controller is used for carrying out preliminary judgment on a vehicle scene based on the fusion features, collecting specific parameter data when further confirmation is needed, and generating an active service decision in combination with a user portrait and historical behavior data; and the active service execution module is used for generating a vehicle-mounted equipment control instruction and executing active service operation. According to the invention, real-time response and intelligent decision-making of vehicle-mounted active services are realized through an end-side multi-modal large model and an MCP dynamic reasoning protocol.

The invention discloses a large language model low-delay reasoning method based on dynamic reasoning graph optimization, and provides a low-delay reasoning method based on dynamic reasoning graph optimization. Constructing a template inference graph which can be rewritten and replayed, and establishing a template library according to input shape vectors; during reasoning, a template is matched with a distance threshold value, and only the attention / feedforward sub-graph is locally recaptured when the distance threshold value exceeds the threshold value; executing a forward execution graph, injecting a key value cache page pointer, numerical value precision and an adapter identifier, and performing playback; dividing the pre-filling and decoding sub-graphs to implement graph-level scheduling; switching the key operator when the key operator operates between a standard / fast kernel and different precisions; page-level backspacing of speculative branches is achieved through a shadow page table and reference counting, and batch and template selection is adjusted in a self-adaptive mode based on online indexes; compared with the prior art, the method has the advantages that the recapture and start overhead is reduced, tail delay and jitter are inhibited, and the hardware utilization rate and the service stability are improved.

The invention relates to the technical field of artificial intelligence and edge computing, in particular to an end-side cloud collaboration system based on a hybrid scheduling strategy. The end-side cloud cooperation system based on the hybrid scheduling strategy comprises a model segmentation module, a state sensing module, a reasoning scheduling module, a model compression and deployment module, an edge optimization engine and a communication synchronization module. According to the end-side cloud cooperation system based on the hybrid scheduling strategy, elastic deployment and dynamic reasoning of a complex model in a multilayer heterogeneous environment are supported, the system state can be sensed in real time, a scheduling path can be optimized, and the robustness of the system is improved; and in combination with model compression and edge operator optimization, the reasoning efficiency is remarkably improved, the communication load is reduced, and then low-delay cooperation between the terminal and the cloud is ensured.

The invention discloses a system and method for training a spatial intelligent large model based on a BIM technology, and relates to the technical field of building information processing, a training data set construction module is adopted to fuse BIM data with multi-modal information such as point cloud data, RGB-D images and IoT data, so that the BIM technology is dynamically applied to obtain dynamic three-dimensional data, and the BIM technology is applied to the BIM technology; according to the spatial intelligent large model, an accurate training data set is generated, and meanwhile, spatial cognition, dynamic reasoning and environment interaction training are performed by adopting the training data set through the large model training module, so that the spatial intelligent large model can realize cognition, reasoning and interaction of a three-dimensional space, and the operation and maintenance reliability of the three-dimensional space is improved.

The invention relates to a data query analysis method based on a natural language, which comprises the following steps of: analyzing a natural language query request input by a user into a dynamic semantic intention tensor which comprises an operation type, a comparison relationship, a time constraint and a data role semantic dimension, and dynamically combining according to task semantics to form a complete data query and analysis operation expression; mapping the natural language intention to a controllable data operation space of a database, and generating a preliminary data operation chain; performing side effect perception processing on the operation tensor, predicting a potential execution consequence of the operation, generating a side effect tensor for simulating a side effect of the database operation, and if any index in the side effect tensor exceeds a preset threshold value, performing local correction on the operation tensor; according to the method, a complex natural language query task is disassembled into a plurality of minimum semantic units by adopting a step-by-step semantic resolution mechanism, each unit is mapped into a specific tensor operation, and a dependency relationship between operation units is dynamically reasoned to generate an operation execution chain.

The invention discloses a multi-modal perception and optimization method and system for a low-power-consumption AR device, and the method comprises the steps: collecting multi-modal data, task demands, resource state data and environment data for the AR device; lightweight processing is carried out on the multi-modal neural network model through model pruning, parameter quantification and distillation technologies; inputting the collected data into a lightweight multi-modal neural network model for dynamic reasoning to obtain a multi-modal recognition result; comprising the steps of executing modal adaptive weight acquisition based on task requirements and environment data; executing energy consumption constraint scheduling according to the equipment resource state data, dynamically selecting a reasoning path strategy, and obtaining corresponding modal feature output; multi-modal feature fusion is carried out, task reasoning is completed, and a multi-modal recognition result is obtained; early-leaving control is executed based on a middle-layer confidence coefficient threshold value in the reasoning process; and interactively outputting a real-time multi-mode identification result. According to the invention, energy efficiency and precision balance and multi-mode fusion low-power-consumption optimization can be realized.

The invention relates to an inference decision-making method, device and equipment for a complex dynamic game scene. The method comprises the following steps: constructing a two-step Markov reasoning framework on the basis of an agent decision-making process in war game confrontation simulation, and dividing a decision into two steps of state reasoning and action decision-making. And designing a structured dynamic decision option library based on the framework, and constructing a dynamic reasoning decision chain. In the cue word generation stage, cue words are generated according to chains and combined with real-time observation information to be input into the large language model; in the state reasoning stage, the model selects options from a state option library to form structured situation description; and an action decision-making stage: selecting a strategy from the strategy option library according to a state reasoning result and converting the strategy into a simulation execution action. According to the method, through layered constraint and structured processing, the accuracy of key information extraction in wargame confrontation is improved.

The invention provides an index-based data attribution analysis system, which relates to the technical field of data analysis, and comprises a data fusion layer, a dynamic graph engine and an attribution calculation layer, the data fusion layer is used for uniformly processing and integrating data from different sources to form a triple knowledge graph, and the sources comprise a service database, an event stream and an expert knowledge base; the dynamic graph engine is used for processing and analyzing real-time data, generating a corresponding risk propagation model and updating the knowledge graph to obtain a dynamic graph; the attribution calculation layer is used for dynamically calculating the risk value and the influence of each node according to the dynamic graph; and carrying out dimension contribution degree fusion attribution calculation to obtain a dimension contribution value. According to the method, the problems of poor adaptability and insufficient interpretability of attribution analysis are solved through a dynamic reasoning mechanism and a risk propagation model of the knowledge graph, and more accurate and real-time risk assessment and contribution degree analysis can be provided.

The invention discloses an intelligent construction and tracing method and device for an attack graph, and relates to the technical field of network security. The method comprises the steps of performing semantic analysis and entity relationship extraction on a multi-source heterogeneous security log according to a predefined structured security data model, and generating a standardized security entity relationship triple set; based on the set, taking an entity in an initial alarm as a starting point, and adopting an iterative closed loop driven by a large language model to dynamically construct an attack graph; and carrying out attack technique and tactics mapping and threat attribution based on the final map, and generating a response strategy of priority ranking. According to the method, automatic and high-precision source tracing and response of the attack chain are realized, and the problems that the prior art depends on static rules and semantic segmentation and lacks dynamic reasoning capability are effectively solved.

The invention provides a multi-agent cooperative task inference and robot scheduling system, which comprises an interaction unit used for acquiring a natural language instruction issued by a user, sending the natural language instruction to an intelligent processing unit and interacting with the intelligent processing unit, receiving an execution result of a task and body state information of a robot, and sending the received execution result to the intelligent processing unit; sending a task missing information supplementing request when the task information is missing; the intelligent processing unit is used for dynamically sensing an environment and a task state according to a user instruction, a body state and environment information fed back by the robot and stored memory, and performing dynamic planning and decision making based on a sensing result and an inverse result of a task execution result to realize task reasoning and robot scheduling; and the robot cluster executes tasks according to the action sequence and feeds back own body state information and environment information. According to the invention, through a multi-agent cooperation framework based on a large model, the robot is endowed with dynamic reasoning and active interaction capabilities, and the requirements of efficient task reasoning and cooperative execution in a dynamic environment are met.

The invention belongs to the technical field of neural network architecture, and particularly relates to a dynamic reasoning path optimization method based on neural architecture search, which comprises the following specific steps: S1, designing a neural architecture search algorithm: firstly defining a search space, then selecting a search strategy, and then setting constraint conditions; s2, constructing a dynamic reasoning path: firstly performing input data feature analysis, then performing reasoning path guidance based on a knowledge graph, and then predicting the reasoning path by using a model according to the input data features and the knowledge graph; and S3, model training and optimization: firstly carrying out joint training, and then carrying out model compression and acceleration. According to the method, through neural architecture search algorithm design and dynamic reasoning path construction, the problem of computing resource waste is effectively solved.

The invention relates to the technical field of intelligent teaching, and discloses an intelligent teaching system and method based on a large language model. The system comprises a teaching intention analysis module, a knowledge graph adaptation module, a dynamic reasoning engine module, a teaching strategy generation module and a feedback optimization module. The teaching intention analysis module is used for disassembling the teaching interaction instruction into a knowledge domain label and a teaching behavior sequence through a semantic segmentation engine; and the knowledge graph adaptation module is used for matching the subject knowledge graph and extracting an associated knowledge node set. The dynamic reasoning engine module inputs the node set into a large language model to complete multi-hop reasoning, and an intermediate state vector containing a reasoning path is generated; and the teaching strategy generation module generates a hierarchical teaching strategy in combination with the vector path weight and the behavior sequence timestamp. The feedback optimization module collects user behavior data, updates knowledge graph matching rules through incremental learning, and adapts to diversified teaching requirements.

The invention belongs to the technical field of data analysis, and provides a questionnaire-based sentiment analysis method, system and equipment in order to solve the problem of inaccurate user sentiment analysis in the existing questionnaire. Using a pre-trained language model to extract a semantic vector of the topic text, and combining with the co-occurrence frequency of the multiple topic options to generate node-level local features; carrying out dynamic reasoning by adopting an improved graph neural network model, calculating a dynamic attention coefficient based on semantic similarity calculated by a topic text semantic vector and a jump probability between topics, and weighting and aggregating neighbor features, so as to obtain context-associated topic node features; the global emotion is calculated by using the PageRank thought, a final emotion analysis result is obtained, dynamic context association generated due to questionnaire jump logic is effectively captured, and the reliability of grasping the overall emotion venation of the user is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a dynamic reasoning method and system based on a language model and a knowledge graph. The method comprises the following steps: step 1, encoding an input QA context text by using a language model encoder; reasoning the knowledge graph by using a graph encoder; 2, iteration is carried out in a hierarchical aggregation module, a joint reasoning module and a dynamic pruning module, so that multi-modal fusion is carried out on the features of the QA context text and the knowledge graph; and step 3, calculating a final output answer. The dynamic reasoning method and the dynamic reasoning system have better answer accuracy, have the advantages of convenience and wide application range, can provide new thoughts and choices for the multi-modal fusion technology in the field of natural languages, and have good application prospects.

The invention discloses an autoregressive language model training process-oriented data leakage risk quantification method, which comprises the following steps of: executing enhanced member data division processing on an original training text set, and generating a ternary partition text set containing member, non-member and key boundary texts through an optimization function; for each text, using the target model to extract member attributes from three channels of forward reasoning, back propagation and state evolution, and fusing the member attributes into member attribute vectors; inputting the member attribute vector into a hierarchical comparison embedded network, and mapping the member attribute vector into an optimized embedded representation vector through comparison learning including similar clustering, heterogeneous separation and boundary positioning; and inputting the embedded representation vector into a dynamic reasoning classifier capable of perceiving a training stage, judging the risk membership degree of the dynamic reasoning classifier, and generating a data leakage risk quantification result. According to the invention, real-time and fine-grained dynamic quantification can be carried out on the leakage risk, and timely early warning is provided for model training.

The invention relates to the technical field of software architecture design, and discloses a software architecture design method based on intelligent bionic implementation of a large model group. The method comprises the steps of obtaining software design requirement input; performing multi-level semantic analysis on software design requirement input to generate an initial component set and context environment description; based on the context environment description, candidate component knowledge related to the initial component set is retrieved from a distributed knowledge base; performing consistency verification on the candidate component knowledge and the initial component set by utilizing a dynamic inference engine to generate a verified component map; according to the verified component atlas, candidate software architectures are generated through an architecture template matching algorithm; and performing iterative adjustment on the candidate software architecture by adopting an optimization strategy, and outputting a target software architecture design. Through collaborative analysis and intelligent reasoning of the large model group, automation and intelligence of software architecture design are achieved, and the efficiency and quality of software design are improved.

The invention discloses a flood control and emergency rescue plan generation method, which comprises the steps of S1, collecting flood control and emergency rescue original data, performing preprocessing and feature extraction, and labeling and confirming extracted feature data to serve as a training set; s2, performing LoRA fine tuning of a low-rank decomposition matrix on the basis of a pre-trained large language model, and performing large language model training operation through the training set; s3, constructing a flood control and emergency rescue field ontology model, learning distributed representation of the knowledge graph by adopting a graph neural network technology, and dynamically updating the knowledge graph and executing reasoning query according to real-time dangerous case information by establishing a dynamic reasoning mechanism; and S4, constructing a multi-modal information fusion framework which is used for carrying out information fusion processing on the knowledge graph reasoning result and then guiding the big language model to generate an emergency plan as a control signal. According to the method, the technical bottlenecks of low plan generation quality, difficulty in knowledge updating, insufficient multi-source information fusion and the like of a traditional method in a complex dangerous case scene are effectively solved.

The invention discloses an energy internet intelligent key node identification and elasticity enhancement method and system, and the method comprises the steps: 1, constructing and dynamically maintaining a semantic enhanced energy internet digital twin super network and a multi-modal knowledge graph, fusing multi-dimensional information, and achieving the self-evolution and dynamic reasoning capability; 2, based on the model, fusing multi-time scale prediction data, and adaptively evaluating the dynamic comprehensive criticality of the node through an intention-function-resource-vulnerability four-layer penetrating traceability model; 3, aiming at the key nodes, generating an intelligent attack strategy by utilizing a generative adversarial network, deducing an information physical cascade failure process by combining multi-agent deep reinforcement learning, and quantitatively evaluating the system elasticity; and step 4, based on an evaluation result, generating a self-adaptive security reinforcement and dynamic reconstruction decision oriented to active immunity and elastic optimization. According to the method, the accuracy and the dynamism of key node identification can be remarkably improved.

The invention relates to the technical field of intelligent law education and law reasoning, and discloses a law immersive education system based on artificial intelligence, and the system comprises an input processing module which is configured to carry out the multistage semantic analysis of a law text inputted by a user, and generates a structured law fact representation; the legal knowledge graph construction module is connected with the input processing module and is used for receiving the structured legal facts and constructing and updating a legal knowledge graph; and the automatic theorem proving module is connected with the legal knowledge graph construction module and is used for executing multi-hop reasoning on the legal knowledge graph based on probabilistic reasoning. According to the technical scheme, the self-adaptive optimization module and the dynamic reasoning mechanism are combined, the technical effects of adjusting the priority of the legal rule in real time and automatically optimizing the reasoning path are achieved, the defects that the reasoning process is stiff, the adaptability is poor, and reasoning errors are likely to be caused are overcome, and the intelligent decision-making capacity of the system is remarkably improved.

The invention discloses a task disassembling method based on a large model, and aims to automatically disassemble requirements input by a user into a series of independent and accurate minimum sub-tasks so as to support high efficiency and intelligence of task execution. Different from a traditional natural language processing method, the method adopts a large model technology to intelligently analyze and optimize input demand content, so that task description is clearer, and operability is achieved. The method comprises the following steps: firstly, deeply analyzing and adjusting a demand by utilizing the understanding ability of a large model to make the demand accord with a logic structure of task execution; then required core steps and logic levels are extracted through a multi-level task decomposition algorithm, and clear sub-task chains are generated through step-by-step decomposition; by means of the adaptability and dynamic reasoning ability of the large model technology, the decomposition efficiency and accuracy of complex tasks are remarkably improved, the burden of a user in the aspects of task planning and execution is relieved, and efficient and automatic development of intelligent task management is promoted.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The invention discloses a knowledge graph-based intelligent question and answer method for text travel. The method comprises the following steps of: obtaining and preprocessing related data in a text travel service system; constructing a structured knowledge graph in the text travel field; constructing a candidate entity pair and executing relation extraction and weight updating guided by a behavior sequence; dividing and generating a behavior preference sub-graph according to a behavior mode, and extracting a text travel knowledge slice set according to a theme; natural language questions of tourists are received, and path search and semantic aggregation with behavior weights and path structures as constraints are executed; and generating a text travel intelligent question-answer result through a question-answer generation engine, and recording a text travel question-answer interaction log for iterative updating. According to the method, knowledge graph modeling and behavior guiding relation extraction technologies are fused, dynamic reasoning and personalized question and answer generation of the travel knowledge are achieved, and the method has the advantages of being accurate in semantic understanding, high in answer association degree and continuous in self-adaptive evolution.

The invention provides a video question-answering system and method based on iterative multi-mode, and the method comprises the steps: carrying out the preprocessing of an input original video file and natural language query, extracting a key frame sequence, and generating an initial subtitle sequence; performing multi-granularity retrieval based on the preprocessed natural language query and the current subtitle sequence, and determining a candidate region; carrying out fine-grained frame selection in the candidate region by using a large language model, and identifying a key frame; based on the candidate area and natural language query, determining the type of the visual information to be supplemented and generating a multi-modal cue word corresponding to the type, and extracting the visual information by the visual language model according to the multi-modal cue word to update the subtitle sequence of the candidate area; generating a prediction answer by adopting a large language model and a visual language model; and judging the confidence of the generated predicted answer, and outputting a final answer. According to the method, the processing mode of video understanding can be optimized, and an accurate cross-modal coordination solution is provided through dynamic reasoning-sensing coordination.

The invention provides a college laboratory safety management and risk identification intelligent agent platform, which relates to the technical field of safety management, and comprises the following steps: obtaining environmental parameters through edge computing nodes to carry out real-time risk analysis, and setting multi-level safety threshold grading early warning; a mixed decision framework combining a graph neural network and reinforcement learning is adopted, a dynamic reasoning mechanism is constructed based on a security management knowledge graph, and intelligent research and judgment and co-processing of multi-dimensional security risks are realized; and finally, feeding back a disposal result to a safety management personnel terminal. According to the invention, the laboratory safety risk identification precision and the emergency disposal efficiency are improved, and intelligent safety management is realized.