59 results about "Model system" patented technology

Modular mucosal tissue bioreactors combined with bioartificial mucus provide a high-fidelity, medium-throughput platform for evaluating CF treatments and modeling host-pathogen biology, a stepping stone towards better options for HEMT-nonresponsive patients. Whole lung ex vivo Cystic Fibrosis model system allows for investigation of candidate CF therapies.

The invention discloses a chronic obstructive pulmonary disease high risk dynamic assessment model system based on an artificial intelligence algorithm, relates to the technical field of chronic obstructive pulmonary disease high risk assessment, and aims to solve the technical problem that the timeliness and accuracy of an assessment result of an existing assessment model are limited. The medical data acquisition module is used for acquiring medical data of a subject through a wearable device, an electronic medical record system or a manually input multi-source channel, generating standardized preprocessed medical data after performing format cleaning, missing value filling and abnormal value filtering on the data, and transmitting the standardized preprocessed medical data to the memory of the cloud database; and the artificial intelligence dynamic assessment model construction module is used for constructing an artificial intelligence assessment model fusing time sequence characteristics and a nonlinear relationship based on the preprocessed medical data, and generating an intermediate result and a final parameter of risk assessment through multi-level calculation. The method has the advantage that the high risk of chronic obstructive pulmonary disease can be dynamically evaluated in real time.

The invention relates to the technical field of three-dimensional modeling, and discloses a modeling system fusing a laser point cloud and a two-dimensional vector, and the system comprises a data collection module, a data fusion module, a model generation module, a visualization module, an optimization adjustment module, and a user feedback module. Laser point cloud and two-dimensional vector data are acquired through a data acquisition module and standardized preprocessing is carried out; performing registration, conversion and weighted fusion on the point cloud and vector data based on a data fusion module to generate high-precision fusion data; performing three-dimensional reconstruction, detail enhancement and quality verification through a model generation module, and outputting a final model; utilizing a visualization module to render a scene and support interactive operation; evaluating model efficiency and dynamically optimizing parameters through an optimization adjustment module; and a user feedback module is combined to analyze requirements and adaptively adjust system settings. According to the method, the precision and reliability of three-dimensional modeling are improved, the robustness and consistency of the modeling process are enhanced, and the overall output quality and engineering applicability of the system are improved.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

Systems and methods include techniques associated with one or more machine learning systems to normalize disparate entries within one or more datasets for common data types. The one or more machine learning systems may be used to generated relationships between attribute-value pairs associated with a particular data type and then to determine, from a corpus of free-form data, individual entries for a target data type. The identified individual entries may be used to extract information from the dataset and generate a modified, clean dataset.

The application discloses a geothermal caprock evaluation modeling method and belongs to the technical field of geothermal resource exploration. In view of the problems that existing caprock evaluation standards are uneven, subjective and lack of quantitative coupling of structural factors and calibration of measured data, the application firstly acquires caprock physical properties and structural data; secondly, a single-factor evaluation model based on physical mechanism is constructed for lithology, thickness, permeability and thermal conductivity; on this basis, a structure-mechanical-integrity coupling model is constructed, which includes basic structure scores, fracture risk indexes and plastic self-repairing capacity factors; finally, by using observation data such as historical breakthrough pressure and leakage events, key parameters in the above model are calibrated through regression and Bayesian methods. The method forms a unified and portable caprock evaluation basic model system, reduces subjectivity, and improves the stability and interpretability of the evaluation results.

Systems and methods for learning from mistakes to improve detection rates of Machine Learning (ML) models. The systems and methods including receiving data with labels; running the data through a trained ML model for predictions; identifying errors in the predictions based on the labels received with the data; adjusting weights associated with samples in the data based on the identified errors; and retraining the ML model with the adjusted weights.

The application relates to a calculation method of a high-temperature alloy impurity diffusion coefficient and an atomic mobility parameter based on a molecular dynamics simulation, which comprises the following steps: parameter setting; model establishment; setting a potential function as a multicomponent EAM alloy potential; energy minimization and setting an initial atomic speed; calculating RDF values of a model system and drawing RDF curves; calculating MSD values of all atoms and respectively outputting MSD values of various atomic groups according to various different metal atom types; defining thermodynamic physical quantity output and commanding each step to output and save; giving a random initial speed to the system, and running under each system in three stages: a temperature rising stage, a temperature keeping stage and continuous temperature keeping and relaxation, and outputting MSD values; calculating the impurity diffusion coefficient of the system based on the MDS values; and taking the measured impurity diffusion coefficient as an input value, and obtaining the atomic mobility parameter through DICTRA software. Compared with the prior art, the application has the advantages of simple method, accurate numerical value and the like.

The invention discloses a breeding performance prediction system based on an intelligent sheep breeding platform, and belongs to the technical field of animal husbandry informatization. The intelligent breeding platform is designed, standardized collection, storage and intelligent management of sheep full-life-cycle data are achieved, and a solid data foundation is laid for follow-up research; secondly, innovatively combining ensemble learning (AdaBoost, GBRT and the like), machine learning (SVR, KNN and the like) and a sorting learning algorithm, and constructing a multi-level breeding sheep breeding performance prediction model system; particularly, a subjective and objective combination weighting method based on AHP-PCA is provided, and the prediction precision of the model is remarkably improved through feature reconstruction; in addition, an Achimedes optimization algorithm (AOA) is introduced into the field of breeding prediction for the first time, and adaptive search of hyper-parameters is realized by using a physical simulation mechanism of the AOA, so that the accuracy, efficiency and robustness of the model are remarkably improved.

Systems and methods for generating a large volume of synthetic stream-of-commerce security imaging data is disclosed. Methods for creating synthetic baggage x-ray scans, synthetic passenger millimeter wave scans, synthetic passenger video surveillance data, and introducing prohibited items to real security images are also disclosed.

The invention relates to the technical field of model construction, in particular to a modeling system and modeling method suitable for a rat epileptic status model.The modeling method comprises the steps that electrical stimulation is applied to a rat according to preset stimulation parameters, and the stimulation parameters are output; monitoring physiological signals of the rats in real time, performing feature extraction on the physiological signals, and outputting epilepsy feature parameters; comparing the epilepsy characteristic parameters according to a preset threshold value to confirm whether an epilepsy persistent state is reached, and generating a final modeling result based on a state confirmation result; when the model is successfully established, the medicine intervention module is automatically triggered, the anti-epileptic effect of the candidate medicine is evaluated in real time based on the change of epileptic characteristic parameters obtained in real time, and a medicine effect evaluation report is output; according to the method, the process of automatically constructing the rat epileptic status model is realized, and the establishment of the model can be dynamically adjusted and confirmed according to the change of the epileptic characteristic parameters by accurately controlling the preset stimulation parameters and monitoring the physiological signals of the rat in real time.

In some implementations, a model system may train a deep learning model to determine baseline figures and stretch figures and may train a shallow learning model to determine likelihoods associated with achieving the baseline figures and the stretch figures. The model system may receive indicators associated with a current entity and may provide the indicators to the deep learning model to generate a baseline figure and a stretch figure. The model system may receive progress data associated with the current entity. The model system may provide output based on the deep learning model and the progress data to the shallow learning model to generate a likelihood associated with the current entity achieving at least one of the baseline figure or the stretch figure. The model system may selectively provide an alert to a user based on the likelihood.

Systems and methods for property guided molecular optimization using artificial intelligence diffusion models. An equivariant continuous denoising diffusion implicit model autoencoder framework (DDIM-AE) can be trained (510) on a conformational dataset to predict raw data from data corrupted by a time-dependent noise to obtain a trained DDIM-AE that ensures controlled generation of threedimensional (3D) molecules. Linear optimization of semantic embeddings of 3D molecules can be performed (520) with a linear classifier to achieve a target property value from desired properties and obtain an optimized embedding. An optimized 3D molecule that includes molecular conformation with the desired properties while preserving interactions with biochemical molecules can be generated (530) from the optimized embedding with the trained DDIM-AE.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

The present application relates to a kind of model systems, methods and media based on non-ideal diode analog heart valve, belong to computational fluid dynamics and cardiovascular system modeling technical field;Including the following steps: S1: establishing cross-domain physical parameter analogy relationship, the physical position where heart valve is defined as two-dimensional simulation interface;S2: construct non-ideal diode valve mathematical model, define the constitutive equation of "pressure-flow" relationship described by valve;S3: construct valve dynamics equation;S4: based on clinical data adjustment parameter to adapt normal or pathological state;S5: valve model is integrated to CFD solver and realizes coupling calculation, exports hemodynamics and valve function index.The present application establishes the profound physical analogy between fluid system and circuit system, constructs a valve model using the mathematical expression of non-ideal diode, which is computationally efficient, numerically stable, has clear physical meaning of parameters and can simulate various pathological states.

In some implementations, a model system may receive an indication of the set of models that are associated with a set of data points. Each model in the set of models may have been selected using a grid search. The model system may receive, from a user device, a query associated with a selected data point in the set of data points. The selected data point may be associated with a corresponding model in the set of models. The model system may provide information included in the query to the corresponding model in order to receive a result associated with the selected data point. The model system may transmit, to the user device, the result in response to the query.

The invention relates to the technical field of public health management and decision-making, and discloses a public health risk comprehensive evaluation and decision-making optimization method based on multi-source information, and a constructed emergent public health event analysis model comprises a medical resource competition sub-model and an infectious disease diffusion sub-model. Model calibration, a nonlinear generalized predictive adaptive control law and comprehensive overflow effect evaluation based on data envelope analysis are adopted to deeply integrate infectious diseases, a resource competition theory, an optimal control theory, operational research and an information theory method; a comprehensive model system capable of reflecting complex system interaction of'epidemic situation-resource-decision-society 'in public health events is constructed, strong robustness and dynamic optimization capability for dealing with uncertainty are achieved, the model precision and reliability are remarkably improved, errors are reduced, the defect that traditional evaluation is single is overcome, and the method is suitable for popularization and application. The overall effect of emergency decision making can be comprehensively evaluated from multiple aspects of medical treatment, society, economy and the like, and efficient and comprehensive decision making support is achieved.

A system may be configured to generate an estimate of value at risk and may include a processor to process instructions that cause the system to generate a rolling time series of value data having a plurality of dimensions, perform a transformation of the time series, perform variance scaling and correlation scaling on transformed time series, reverse transform the results of the scaling, and estimate of a value-at-risk for the value data.

The invention discloses an intelligent control cement batching system, which belongs to the technical field of cement batching, and is characterized by comprising an intelligent formula generation module for generating an initial batching scheme according to raw material data and an optimization target, and a model prediction control module connected with the intelligent formula generation module and used for predicting the initial batching scheme according to the initial batching scheme. The system comprises a model prediction control module which is connected with an initial batching scheme and a dynamic model and outputs a control instruction based on the initial batching scheme and the dynamic model, and a process state feedback module which is connected with the model prediction control module and obtains process state information of materials in real time through on-line sensing signals. And the system can foresee and compensate the fluctuation of the large lagging process in advance, thereby fundamentally solving the periodical stubborn disease of the raw material components and remarkably improving the quality stability.

Systems, computer-implemented methods, and / or computer program products to facilitate updating, such as averaging and / or training, of one or more statistical sets are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a computing component that updates a first statistical set with an additional statistical set from an additional system. The additional statistical set can have been generated from a parent statistical set that is based on underlying data. To update the first statistical set, the additional statistical set can be obtained by the system without obtaining the parent statistical set and without obtaining the underlying data. According to an embodiment, the first statistical set can be a model parameter set generated from a first parent statistical set that is an analytical model.

This application discloses a two-frame wide dynamic range (WDR) short-frame denoising system and method, as well as its model system and training method. The method utilizes noise modeling to simulate long and short frames, which is more conducive to the use of the short-frame denoising model on real data. Furthermore, it does not require the collection of a large amount of real sample data. A machine learning model uses information from the long frame to guide the denoising of the short frame, maintaining consistency in noise intensity between the long and short frames, which helps improve the quality of the output image in subsequent WDR synthesis.

The invention discloses an intestinal cancer mouse model based on Par3L gene knockout and a construction method thereof. After sgRNA is designed to knock out a promoter and a first exon of a Par3L gene of a mouse, the progeny of the obtained gene knockout mouse can spontaneously form intestinal cancer along with the increase of age, and the progeny is accompanied with a tissue infiltration phenotype. The intestinal cancer mouse model constructed by the invention can cause abnormal mitosis of intestinal cells and influence the genome stability, a good model system is provided for further research on the occurrence mechanism of intestinal cancer, and the intestinal cancer mouse model has a wide application prospect.

This invention relates to cell screening models, specifically to a label-free cell screening model for G protein-coupled receptors and its applications. The label-free G protein-coupled receptor cell screening model is a BLT1 receptor cell screening model, specifically using the Flp-In cell line stably expressing BLT1. TM -CHO-BLT1. The BLT1 cell screening model constructed in this invention does not require fluorescent labeling and the detection process does not require the addition of additional indicators. It features target pathway integration response, no cell damage, reliable detection results, high sensitivity, high screening throughput, short cycle, and simple operation. It can be used to search for highly active ligands of the BLT1 receptor from natural product libraries, metabolite libraries, and combinatorial chemistry libraries, providing a model system for drug screening of diseases closely related to the BLT1 receptor. Accordingly, lead compounds for related diseases can be obtained through the BLT1 cell screening model established in this invention.

This invention relates to a simulation and testing system for narrow foundation pit excavation in a hypergravity field, comprising a foundation pit model system, a foundation pit support system, and a measurement system. The foundation pit model system includes a model box, a partition, and an adjusting rod. The partition is vertically positioned inside the model box. One end of the adjusting rod is connected to the partition, and the other end rests against the inner wall of the model box. The adjusting rod is used to adjust the position of the partition. The space between the partition and the model box is used to place test soil, which is formed by consolidating mud in a hypergravity field. The foundation pit support system includes two retaining walls arranged parallel to the excavation trench and a support rod connecting the two retaining walls. The support rod is used to adjust the distance between the two retaining walls. The measurement system is used to measure the strain of the retaining walls, ground settlement, and displacement field changes. This invention can comprehensively measure all aspects of the impact of foundation pit excavation in a hypergravity field and simulate the full excavation and addition of supports to the foundation pit.

The invention discloses a meta universe digital human MEES modeling system and method based on a large model. The system comprises a perception and understanding layer, an MEES core processing layer and an expression and action layer. According to the method, the intelligent level, the emotion interaction depth and the lasting attraction of user experience of the element universe digital human are remarkably improved, so that the element universe digital human becomes an intelligent partner or an interaction object which is more real, has higher personality charm and can better meet the emotion requirement of a user in the element universe.

The specification provides a large model-based task processing method, device, medium and equipment, the method is executed by a target large model system; the target large model system includes a plurality of agents; the target large model system executes the following first step for a first task: obtaining the prompt word corresponding to the first task; based on the prompt word, multiple result samplings are performed through multiple sampling strategies to obtain multiple reference results; wherein different sampling strategies are based on different agent combinations and / or different reasoning enhancement strategies; the consensus score reflecting the consensus degree corresponding to each reference result is calculated; based on the consensus score corresponding to each reference result, the first result corresponding to the first task is determined.

A sequential feedback prediction system predicts a risk of an event associated with a set of potential outcomes. The system receives training data including data records that have information associated with risk factors. Each data record may be labeled with outcomes that have inherent sequential dependencies. The system trains sub-models, each sub-model predicting risks for a respective outcome using a set of risk factors. For each sub-model, the system may determine a subset of sub-models whose results are used as input for the sub-model. The system determines an order to run the set of sub-models such that prediction results for preceding sub-models may be used as inputs for subsequent sub-models. The system may determine a number of rounds to run the ordered sequence of sub-models until the performance of the ensemble model meets a predetermined threshold. The system may generate a risk score for each of the set of outcomes.

This invention discloses an ecological seawall simulation model system, relating to the ecological construction of seawalls. The system includes: a data acquisition module for acquiring multi-source heterogeneous data of the target seawall, including geographic information data, hydrological monitoring data, ecological survey data, and remote sensing imagery data; a data fusion processing module connected to the data acquisition module for performing spatiotemporal registration, normalization, and missing data imputation on the multi-source heterogeneous data to generate a standardized fused dataset; and a dynamic simulation model construction module for constructing a three-dimensional digital model based on the fused dataset, including the main structure of the seawall, the shoreline in front of the seawall, and the ecological space behind the seawall, and setting hydrodynamic boundary conditions and ecological evolution rules. This invention, by quantitatively evaluating ecological seawall assessment indicators, helps improve the marine ecological environment, enhances the ecological service functions of ecological seawalls, and provides strong support for the sustainable development of coastal areas.

The application discloses a TCT smear automatic detection system based on multi-level hard polarization feature fusion, which comprises a YOLO-TCT model, the YOLO-TCT model is obtained by improving YOLOv8, and the improvement lies in that a C2f module in a YOLOv8 backbone network is replaced by an MLHP-C2f module. Through an ablation experiment, it is proved that feature fusion when ML, PSA or HPSA is separately added on the basis of YOLOv8-L has a negative effect on the performance of the model, and only when ML and HPSA are combined, the model realizes the best mAP 0.5:0.95 and mAP 0.5 , which shows the rationality and superiority of the MLHP structure. The comparison experiment results of different models show that in the detection of high-resolution cervical cell TCT smear images, the detection accuracy of the YOLO-TCT is significantly better than those of other model systems.