241 results about "Uncertainty estimation" patented technology

The invention discloses an aviation converter fault detection method based on an information fusion algorithm, and the method comprises the steps: collecting the operation data of an aviation converter, and constructing a data set; performing time information extraction and space information extraction on the data in the data set, and performing feature fusion on time features and space features based on a variational auto-encoder to obtain spatio-temporal data; constructing a Bayesian deep learning model, and training and optimizing the Bayesian deep learning model; inputting the spatio-temporal data into the trained and optimized Bayesian deep learning model for processing, outputting a numerical value representing a fault occurrence probability, and taking a variance based on the probability numerical value as uncertainty estimation; presetting a probability threshold, and when the output probability is greater than the threshold, determining that the converter has a fault; otherwise, judging that the operation state is normal; a more accurate and reliable diagnosis result is provided for fault detection, and the method has high adaptability and robustness in practical application.

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

The invention discloses a welded pipe conveying abnormity prediction method and system based on large model reasoning, and aims to solve the problems that multi-source data is difficult to align, cross-station false correlation is caused, prediction lacks executable positioning and time sequence, and linkage control reliability is insufficient. Event alignment is carried out by taking a controller edge signal and an encoder zero position as time anchor points, a production line topology semantic graph containing time delay, capacity and interlocking attributes is constructed, and topology reachability and physical time delay constraints are applied in a self-attention long sequence model to carry out multi-step rolling prediction. And outputting a risk probability, refining the risk probability to spatial positioning of a roller way section or a shaft and the minimum executable intervention time, and generating a risk interval in combination with uncertainty estimation and calibration so as to drive an upstream beat self-adaptive speed reduction, shunting or stopping strategy. The technical effects of improving accuracy and interpretability, reducing false alarm and missing alarm, ensuring that linkage can be executed in advance and meeting edge time delay budget are achieved.

The invention provides an automatic driving decision-making system based on information entropy-Bayesian estimation. The automatic driving decision-making system comprises an environment sensing module, a hybrid quantification module and a decision-making optimization module. The environment sensing module is used for collecting and processing sensor data and completing target detection and classification; the hybrid quantization module performs reliability quantization on the recognition result based on information entropy and Bayesian uncertainty estimation, generates uncertainty evaluation results of different targets and scenes, and transmits the uncertainty evaluation results to the decision and control module; and the decision and control module dynamically adjusts the strategy of the automatic driving system according to the uncertainty measurement of each target. The automatic driving decision-making system based on information entropy-Bayesian estimation is used for quantifying the target recognition reliability in an automatic driving scene. The output confidence distribution of the target recognition model is evaluated by calculating the information entropy, and the uncertainty of the recognition model is estimated by using a Bayesian uncertainty estimation (such as Monte Carlo Dropout) method.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention relates to the technical field of CPU card chips, and discloses a CPU card chip test method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of a CPU card chip, and obtaining an initial test data matrix; constructing a test parameter association diagram of the initial test data matrix and predicting missing data to obtain a complete test data matrix; performing principal component analysis on the complete test data matrix to obtain an effective test data set; constructing a multi-level fuzzy set according to the effective test data set to obtain fusion test data; and inputting the fusion test data into a variational auto-encoder for self-supervised pre-training, executing conditional interpolation by using a diffusion state space model to obtain a defect detection result and an uncertainty estimation value, and realizing optimal fusion of electrical characteristics and functional test data by using a multi-stage fuzzy set and an adaptive weight adjustment mechanism. Complementary advantages of dual-channel data are fully utilized, and a more comprehensive and accurate test result is provided.

The invention discloses a credible medical image segmentation method and equipment based on evidence uncertainty progressive guidance. The method comprises the following steps: S1, obtaining a training sample and a corresponding label; s2, training a credible medical image segmentation model according to the training samples and the corresponding labels; s3, acquiring a test sample; s4, inputting a test sample into the trained credible medical image segmentation model for prediction, calculating parameters of Dirichlet distribution, and calculating an expected tensor of the Dirichlet distribution; s5, calculating a segmentation evidence uncertainty matrix; s6, enabling the category with the maximum category probability to be a prediction category, and enabling a prediction segmentation result output by the credible medical image segmentation model based on the test sample to be predictioni = argmax pi; and S7, the prediction segmentation result of the test sample and the segmentation evidence uncertainty matrix are visualized. According to the method, the segmentation precision is improved, the calculation cost is greatly reduced, and more accurate uncertainty estimation is provided.

The invention relates to the technical field of agent path planning, in particular to a multi-agent collision-free path planning method based on a fusion DQN algorithm. The multi-agent collision-free path planning method comprises the following steps: firstly, constructing a two-dimensional grid map as an environment, and carrying out feature extraction by utilizing a CNN (Convolutional Neural Network); then, behavior clone learning is carried out through the expert model to obtain a BC model; the core innovation lies in that a BC model and a CNNDQN model are fused, an adaptive strategy learning framework is constructed, and intelligent dynamic combination of expert experience and reinforcement learning exploration is realized by adopting uncertainty estimation, antagonistic knowledge distillation and performance perception sampling technologies; and finally, further processing an initial path output by the fusion model by a CBS algorithm, and completing multi-agent collision-free path planning. According to the method, the accuracy and efficiency of path planning are optimized through a mixed learning strategy.

The invention relates to an intelligent measurement and control optimization method and device for dynamic parameter adaptive calibration, equipment and a medium. The method comprises the following steps: executing invariance causal test based on an external environment context to obtain a cross-environment invariant explanatory variable subset, and performing anti-fact simulation on each parameter in the cross-environment invariant explanatory variable subset to generate a causal attribution report; based on an under-excitation parameter subset and a cross-environment invariant explanatory variable subset in the baseline recognizable atlas, performing safe active excitation planning on the parameters to obtain a safe micro-perturbation excitation plan; and based on the residual error, the uncertainty estimation, the delay cross-correlation feature, the hysteresis loop area feature, the causal contribution score and the excitation-response fragment, performing calibration estimation on the target parameter by using a hierarchical estimator, and generating a calibration packet according to a calibration estimation result. By adopting the method, self-adaptive calibration of intelligent measurement and control dynamic parameters can be realized through residual attribution and cross-environment invariance test in combination with safe perturbation excitation.

The invention relates to the technical field of weather numerical mode / climate mode prediction and evaluation, in particular to a comprehensive evaluation method and device for climate mode prediction data and a storage medium. The method comprises the following steps: acquiring prediction data and corresponding observation data of different climate modes; calculating evaluation indexes of different climate modes based on the prediction data and the observation data, wherein the evaluation indexes comprise deviation, a spatial correlation coefficient, a root-mean-square error, a time correlation coefficient and a TS score; calculating the weight of each evaluation index according to the information entropy of each evaluation index; and according to the weighted sum of each evaluation index and the corresponding weight, determining a comprehensive evaluation result of the different climate modes. Therefore, according to the method, mode prediction precision, uncertainty estimation, prediction deviation and extreme event prediction capability are evaluated from time and space, one-sidedness of a single index is avoided, and an evaluation result is more objective and credible.

The invention provides an AI-driven equipment health state assessment method and system, and relates to the technical field of intelligent operation and maintenance. The method comprises the following steps: acquiring equipment operation data, and performing time and dimension unification and quality control to form a multi-source operation data set and an environment context; generating an initial state feature based on the mechanism feature library, and obtaining a general representation through self-supervised pre-training; executing calibration learning by using a preset health label, and establishing a fusion evaluation model containing time sequence consistency and physical boundary constraint; carrying out distribution alignment and uncertainty estimation on the basis of scene differences to obtain alignment characterization and credibility scores so as to optimize a model gating strategy; performing joint mapping on the new data, outputting health index, fault probability and residual life estimation, and generating a root cause clue; lightweight online updating is executed under drifting detection, health indexes and root cause clues are written back to a mechanism feature library, early warning levels and maintenance suggestions are generated, and therefore complete-cycle intelligent sensing and self-adaptive optimization of the equipment state are achieved.

The invention discloses a hotel room type matching method and device, and the method comprises the steps: obtaining the original information of a newly-added hotel and room type, carrying out the preprocessing of the original information, and generating a standardized hotel and room type feature data set; constructing a multi-level feature vector based on the standardized feature data set; screening and generating a candidate matching set in combination with an inverted index and a locality sensitive hashing algorithm; a matching score matrix is calculated through fusion of the rule matching model, the machine learning model and the deep learning model; generating a final matching conclusion through Bayesian uncertainty estimation and threshold dynamic adjustment; and optimizing the matching model based on the matching conclusion and the business feedback data. Through multi-dimensional feature extraction, multi-model fusion matching and confidence evaluation, the problem of accuracy of hotel room type cross-supplier matching is solved, and the matching efficiency and robustness are improved.

The invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on uncertainty estimation, and the method comprises the steps: inputting T1, T1c, T2 and Flair images of a brain tumor into a trained image segmentation model which comprises four encoder modules, one decoder module, one Monte Carlo simulation module and one image processing module; the Monte Carlo module repeatedly calls the encoder module and the decoder module for sampling; and the image processing module processes the output to obtain a mean value image, a random uncertainty image, a cognitive uncertainty image, an entropy uncertainty image and a mutual information uncertainty image, thereby realizing accurate segmentation and analysis of the brain tumor. The method improves the segmentation accuracy and the diagnosis reliability, is high in expansibility, and is suitable for various medical image analysis tasks.

In various examples, systems and methods for uncertainty estimation for object detection in autonomous and semi-autonomous systems and applications are provided. The systems and methods may use data from one or more sensors (e.g., camera(s) and / or LiDAR sensor(s) to generate a representation of features surrounding a machine. A model may be used to generate probabilities of objects being present in the representation of features and uncertainty estimates corresponding to the object presence probabilities. The uncertainty estimates may be used to identify scenes that are significantly different from the training data, detect errors in the bounding shapes for objects, and / or highlight areas where object detections may have been missed. The systems and methods may also be used to auto-label scenes associated with the representation of features, and the auto-labeled scenes may be used for training purposes.

The invention provides an end-to-end automatic driving behavior decision-making method and system considering uncertainty estimation, and the method comprises the steps: inputting image information into a convolutional neural network, carrying out the feature extraction, obtaining the state information of surrounding traffic participants, carrying out the feature fusion of the state information of a vehicle, and inputting the fusion features into a full connection layer; constructing an integrated quantile network to obtain complete uncertainty estimation of an agent decision result, wherein the complete uncertainty estimation comprises data uncertainty estimation based on an implicit quantile network and model uncertainty estimation based on a multi-model integrated network considering prior Bayesian estimation; evaluating the confidence coefficient of the agent decision result, if the confidence coefficient is high, outputting the optimal driving action of the vehicle predicted by the integrated quantile network, and if the confidence coefficient is low, switching to the braking strategy output; and a control module of the end-to-end automatic driving decision model agent is further input, and a control signal is output to enable the vehicle to execute different control actions. According to the invention, the reliability and credibility of end-to-end automatic driving behavior decision are improved.

The invention relates to the technical field of ship energy consumption prediction, in particular to a ship main engine energy consumption prediction method and device based on uncertainty estimation, and the method comprises the steps: receiving historical navigation data of a target ship, dividing the historical navigation data into a training set and a test set, and carrying out the standardization processing; training the base model by using the training set and generating a residual sample; taking a prediction result of the base model as a new input feature, and training a linear regression meta-model to construct a Stacking integrated model; calculating quantiles according to the residual samples and constructing a prediction interval; calling an MAPIE library to automatically realize a CV + conformal prediction process, and automatically optimizing hyper-parameters of the base model by using Optuna to obtain an optimal model parameter combination; and outputting the energy consumption predicted value of each target ship and the corresponding confidence interval. According to the method, the stability and reliability of the ship energy consumption prediction result can be improved, quantitative expression of the confidence coefficient of the prediction result in an energy efficiency management system is realized, and the practicability and decision support capability of the method in scenes such as intelligent shipping scheduling are enhanced.

Disclosed is a method and device for determining uncertainty measure of a classification model. The method includes: obtaining a sample data set for evaluating the uncertainty of a classification model, each sample data including an original evaluation sample and a corresponding classification label; inputting the original evaluation sample into the classification model to obtain a first predicted classification result, which includes a first predicted category and a first predicted probability; calculating the first prediction loss of the classification model according to the difference between the first predicted probability and the classification label to obtain a first uncertainty estimate; calculating the first calibration error of the classification model according to the difference between the first predicted category and the classification label and the first predicted probability to obtain a second uncertainty estimate; determining the uncertainty measure of the classification model based on at least the first uncertainty estimate and the second uncertainty estimate. The classification model uncertainty measure determination method according to the present application can achieve applicability in a wide range of application scenarios.

A short-term scheduling rule extraction method for a water-optical complementary system comprises the following steps: correcting an existing business prediction result by adopting a probability prediction model based on historical observation data and business prediction data of runoff and optical power; generating multiple groups of future possible runoff and optical power scenes by adopting a data sampling method based on a commercial prediction result corrected by the probability prediction model in the step 1; constructing a water-light complementary peak regulation optimization scheduling model; 3, constructing a Bayesian gated loop network which can consider the commercial prediction result and the model parameter uncertainty, introducing a probability recalibration mechanism which can effectively relieve the uncertainty estimation deviation of the Bayesian gated loop network, taking the optimal scheduling solution set obtained in the step 3 as the basis, extracting a complementary system scheduling rule which considers the multiple uncertainties, and finally obtaining the optimal scheduling solution set. And the peak regulation scheduling of the water-light complementary system is guided by using a complementary system scheduling rule. The comprehensive benefits of the multi-energy complementary system can be improved.

The invention discloses a semi-supervised bladder tumor medical image segmentation method based on supervised branches and uncertainty estimation, and relates to the technical field of bladder tumor medical image segmentation. Comprising the following steps: S1, acquiring data; s2, data preprocessing; s3, data division; s4, constructing a model; s5, performing model training; and S6, carrying out segmentation identification. The invention provides a new semi-supervised task method, which introduces a supervision branch, combines a pseudo tag and uncertainty estimation based on information entropy, uses an average teacher model as a bottom layer architecture, and provides a supervision signal through a prediction result of a supervision branch network. Therefore, the bladder tumor segmentation model is supervised to generate a more accurate segmentation result.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

A control system and a method for estimating a risk exposure of an automated driving system (ADS) of a vehicle. Obtaining an actuation capability of the vehicle, wherein the obtained actuation capability includes an uncertainty estimation for the actuation capability. Obtaining a location of free-space areas in the surrounding environment of the vehicle, wherein the obtained location of free-space areas comprises an uncertainty estimation for the estimated location of free-space areas. Forming a risk map of the surrounding environment of the vehicle based on the obtained actuation capability and the obtained location of free-space areas, wherein the risk map includes a risk parameter for each of a plurality of area segments included in the surrounding environment of the vehicle. Determining a total risk value of the ADS based on the risk parameters of a set of area segments intersected by at least one planned path of the ADS.

The invention provides a medical image cell segmentation method based on progressive pseudo-label optimization in order to solve the problem of learning deviation caused by unreliable pseudo-label learning and the limitation that a fixed-form pseudo-label cannot provide reliable cell morphological characteristics in an existing weak supervision method. The method comprises the following specific steps: 1) utilizing weak supervision point labeling information, generating two initial pseudo labels with complementarity through a clustering algorithm and a superpixel segmentation algorithm, and respectively guiding a training process of a double-branch network; 2) utilizing a dynamic threshold and watershed algorithm to realize growth and boundary division of a cell region in the pseudo tag, so that the pseudo tag is gradually close to the real form of a cell; 3) designing a bidirectional cross supervision mechanism, and realizing knowledge migration and collaborative optimization through high-confidence prediction results of the two branch networks; and 4) uncertainty estimation and a difficult sample attention loss function are designed, the feature learning ability of the network to difficult samples with low contrast, fuzzy boundary and the like is enhanced, and the precision of weak supervision cell segmentation is effectively improved.

A method for training an artificial intelligence (AI) model includes receiving a training dataset and utilizing processing circuitry to train a Bayesian Neural Network (BNN) based on the dataset and a selected training algorithm including backpropagation. The method estimates aleatoric and epistemic uncertainty for each sample in the dataset. Based on these uncertainty estimates, weights are assigned to the samples, prioritizing those with lower aleatoric uncertainty. A conditioned training dataset is generated by increasing the weights of these prioritized samples. The AI model is then trained using this conditioned dataset. Finally, the pre-trained AI model outputs a prediction. This approach improves model performance by focusing on data with more predictable characteristics, reducing bias and enhancing prediction reliability.

The invention discloses a dynamic target tracking method based on LBP (Local Binary Pattern) features and semantic features, and the method can accurately predict and reduce a first search area through the calculation of the offset of a target in an inter-frame image, the introduction of a motion amplification coefficient, uncertainty estimation and other technologies, and reduces the interference of background information on the target while reducing the calculation amount. According to the method, the target features are extracted through ResNet-50, the target features are subjected to dimensionality reduction processing while the discrimination capability and robustness of the target features are guaranteed, and the problems that the calculation amount is too large and the complexity is high are effectively avoided. Through accurate positioning and reduction of a first search area where a target is located and robust feature extraction and cooperative interaction, the ability of continuously and stably tracking the target under the complex conditions that target movement is difficult to fit, the illumination condition is complex, the target is shielded and the like is enhanced.

The invention discloses a human mobility prediction and uncertainty estimation method and system based on a sparse spatio-temporal trajectory, and the method comprises the steps: obtaining a spatio-temporal trajectory, and carrying out the double-graph convolution embedding and global timestamp embedding of the spatio-temporal trajectory, and obtaining a spatio-temporal vector; for the discrete time trajectory, predicting a place of the discrete time trajectory at the next moment based on a sequence encoder of an autocorrelation mechanism, evaluating a place prediction effect based on an evaluation index, and optimizing a discrete time trajectory prediction model; for the continuous time track, the location and time of the next time-space point of the continuous time track are predicted based on a relative time self-attention sequence encoder, the location prediction effect is evaluated based on an evaluation index, the accuracy of time is measured based on a mean square error, and a continuous time track prediction model is optimized; and for an uncertain trajectory, defining the data uncertainty as trajectory entropy based on the spatio-temporal trajectory, determining a trajectory entropy coordination loss function based on the trajectory entropy, and estimating the uncertainty of the model.

The invention provides a dynamic sparse observation-oriented deep neural process ocean data assimilation method, and relates to the field of ocean data processing, and the method specifically comprises the following steps: constructing a training data set; simulating actually observed non-uniform and uncertain characteristics through Gaussian nuclear diffusion; building an ocean assimilation network oriented to sparse dynamic observation, outputting an analysis field and estimating uncertainty; and performing end-to-end training on the ocean assimilation network model by taking the reanalysis true value field as a supervision signal, and optimizing network parameters by combining a minimum error term and a structure constraint term. And after training is completed, inputting the background field in the test stage and sparse observation into the ocean assimilation network model for reasoning to obtain an ocean state reconstruction field conforming to the actual physical quantity scale. According to the technical scheme, the problem that in the prior art, calculation feasibility, cross-scale correlation modeling and credible uncertainty output cannot be considered under the real conditions of sparse observation and dynamic change of spatial-temporal distribution is solved.

Systems and methods are provided for subsurface characterization from seismic data. The system can receive a plurality of candidate velocity and anisotropic parameter models and seismic gather data. A subset of the plurality of candidate velocity and anisotropic parameter models can be selected to form a training data set. The system can generate a joint probability functions of depth differences and seismic semblances based on the training data set and generate a likelihood function based on the joint probability function. A Bayesian model can be defined using the likelihood function and the prior probability functions. The system can draw a plurality of samples from a posterior distribution of the Bayesian model using Markov Chain Monte Carlo sampling methods and calculate depth uncertainty values using the plurality of samples.

The invention provides a data-driven sand shale rock lithology prediction and uncertainty estimation method, and the method comprises the steps: 1, carrying out the rock physical analysis, and selecting a proper rock physical model; 2, a fusion method of data generated by forward modeling of the rock physical model and actual data under the physical property parameter disturbance condition is researched, and an elastic parameter data set is generated to serve as an expanded simulation training sample; 3, establishing a statistical rock physical mapping relation between reservoir sand shale and elastic parameters; and step 4, the improved deep neural network no longer outputs the lithology result but outputs the lithology probability. According to the data-driven sand shale mass lithology prediction and uncertainty estimation method, a more accurate sand shale prediction model is established by using a deep learning technology, the probability of a sand shale prediction result is given, and finally a fine statistical physical model for sand shale stratum lithology body prediction and uncertainty estimation is obtained.

The present invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on uncertainty estimation. The method is as follows: input the T1, T1c, T2, and Flair images of the brain tumor into a trained image segmentation model, which includes 4 encoder modules, 1 decoder module, 1 Monte Carlo simulation module, and 1 image processing module; the Monte Carlo module repeatedly calls the encoder module and the decoder module for sampling; the image processing module processes the output to obtain a mean image, a random uncertainty image, an epistemic uncertainty image, an entropy uncertainty image, and a mutual information uncertainty image, so as to achieve accurate segmentation and analysis of the brain tumor. The present invention improves the accuracy of segmentation and the reliability of diagnosis, has strong scalability, and is applicable to a variety of medical image analysis tasks.

The invention discloses a sample uncertainty estimation-based self-adaptive target recognition method during online testing, which belongs to the technical field of computer vision and comprises the following steps of: firstly, downloading a data set and augmenting the data set; then sequentially sending each batch of data into the source domain model, evaluating the current batch of data twice through the source domain model, and calculating an uncertainty value of each sample; carrying out model learning on the batch of samples and carrying out model training updating; and finally, predicting the samples by using the model which learns the current batch of target data to obtain a classification result of the batch of samples. According to the invention, the problem that the target domain data cannot be obtained and real-time identification needs to be carried out in the adaptive framework during the online test at present is well solved, and the accuracy of identifying the vehicle target object by the intelligent traffic during the online test is improved.