79 results about "Predictive factor" patented technology

Methods and systems provided for creating a scalable building block for a virtual power plant, where individual buildings can incorporate on-site renewable energy assets and energy storage and optimize the acquisition, storage and consumption of energy in accordance with a value hierarchy. Each building block can be aggregated into a virtual power plant, in which centralized control of load shifting in selected buildings, based on predictive factors or price signals, can provide bulk power for ancillary services or peak demand situations. Aggregation can occur at multiple levels, including developments consisting of both individual and common renewable energy and storage assets. The methods used to optimize the system can also be applied to “right size” the amount of renewable energy and storage capacity at each site to maximize return on the capital investment.

A system and a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects. Each web-page can include one or more links to be associated with content objects from the group. For each content object presented over a requested web-page, one or more predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer's visit.

A system and method for creating a predictive model to select an object from a group of objects that can be associated with a requested web page, wherein a configuration of the requested web page defines a subgroup of one or more selected objects from the group of objects. Exemplary embodiments of the present invention seek to provide novel solutions for determining which content object, taken from a group of content objects, will be best suited for presentation in association with a link on a web page that has been requested by a certain surfer. Each web page can include one or more links to be associated with content objects from the group.For each content object presented over a requested web page, a predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content. For example, should a surfer select a relevant content object, the probability of the objects that can be presented is calculated. Subsequently, the objects with the highest predictive expected utility are selected to be associated with the links in the web page requested by the surfer.

A system and a method for creating a predictive model to select an object from a group of objects that can be associated with a requested web-page, wherein a configuration of the requested web-page defines a subgroup of one or more selected objects from the group of objects. Each web-page can include one or more links to be associated with content objects from the group. For each content object presented over a requested web-page, one or more predictive model with relevant predictive factors is processed such that the predicted objective, the probability of success for example, is calculated. A success is defined as a surfer responding to the presented content according to the preferences of the site owner. Each predicted model can be associated with a key-performance indicator (KPI). Further, a predictive model can reflect the number of times the surfer requested the web page during the surfer's visit.

Methods and systems provided for creating a scalable building block for a virtual power plant, where individual buildings can incorporate on-site renewable energy assets and energy storage and optimize the acquisition, storage and consumption of energy in accordance with a value hierarchy. Each building block can be aggregated into a virtual power plant, in which centralized control of load shifting in selected buildings, based on predictive factors or price signals, can provide bulk power for ancillary services or peak demand situations. Aggregation can occur at multiple levels, including developments consisting of both individual and common renewable energy and storage assets. The methods used to optimize the system can also be applied to “right size” the amount of renewable energy and storage capacity at each site to maximize return on the capital investment.

The invention comprises systems and methods for making a prediction utilizing admissions-based or personal information. The invention receives information associated with the prospective student or person via a network. The invention determines one or more predictive factors based upon selected prospective student information or selected personal information. Finally, the invention determines a likelihood of a decision such as an enrollment decision based upon at least one predictive factor. Information utilized by the invention consists of at least one of the following: static data, biographical data, statistical data, historical data, behavioral data, preferential data, circumstantial data, demographic data, or other data that permits an observation to be made about a person such as the prospective student. The invention develops and updates a predictive algorithm that correlates one or more predictive factors based upon selected prospective student information or personal information.

An electric power system load prediction method based on a back propagation (BP) neural network comprises the following steps of (1) confirming input-output vectors according to specified electric power load prediction; (2) establishing a BP neural network model according to the input-output vectors; (3) training the BP neural network; (4) inputting a test sample to test the trained BP neural network, judging whether an error of an output predicted value and an actual value is smaller than a set threshold value or not and if so, carrying out a step (5); and (5) obtaining actually required load prediction according to the predicted value. The implicit presentation of internal relation of predictive factors is achieved through automatic learning of the BP neural network model and weight distribution of the BP neural network, the accuracy of the electric power system load prediction is high, and safe and stable operation and economical efficiency of an electric power system can be guaranteed effectively.

A method for diagnosing or differentially diagnosing a cancer characterized by the presence of cancer cells in the pleural fluid of a mammalian subject, the method comprising contacting a sample of pleural fluid of the subject with colloidal magnetic particles coupled to a ligand which binds to a determinant on a cancer cell, but does not bind above a baseline threshold to other cellular and non-cellular components in pleural fluid; subjecting the pleural fluid-magnetic particle mixture to a magnetic field to produce a cell fraction enriched in ligand coupled-magnetic particle-bound cancer cells, if present in the pleural fluid; and analyzing the enriched fraction for the number of cancer cells in the pleural fluid. In certain aspects, this method involves preparing the pleural fluids for the above-noted method steps by, e.g., dilution of unprocessed pleural fluid. In certain aspect, the pleural fluid is subjected to the diagnostic method within 24 hours of withdrawal from the subject. This method has advantages to present diagnostic procedures for identifying malignant pleural effusions. The tumor cells present in pleural fluid can be characterized with cellular and molecular markers to determine prognostic and predictive factors.

The invention belongs to the field of tobacco manufacturing and relates to a method for drying cut tobacco. The method comprises the following steps: acquiring predictive factors, wherein the predictive factors comprise preset moisture content of finished cut tobacco, real-time determined data of temperature and humidity in production places and moisture content of blended cut tobacco; inputting the predictive factors into a prediction model so as to obtain a predicted value of the moisture content of the outlet cut tobacco in a cut tobacco drying process; and adjusting process parameters of the cut tobacco drying process according to the predicted value of the moisture content of the outlet cut tobacco in the cut tobacco drying process, wherein the prediction model is established according to historical production data of the finished cut tobacco of multiple batches, and the historical production data comprises the moisture content of the finished cut tobacco, historical determined data of the temperature and humidity in production places, the moisture content of the blended cut tobacco and moisture content of the outlet cut tobacco in the cut tobacco drying process. The inventionfurther relates to a device for predicting moisture content of outlet cut tobacco in the cut tobacco drying process. According to the method disclosed by the invention, the moisture content of the outlet cut tobacco in the cut tobacco drying process is controlled, so that the moisture content of the finished cut tobacco reaches a design value and is kept stable, and the stable cigarette quality is further ensured.

The invention relates to a sea wave significant wave height long-term trend prediction method based on reanalysis data. The sea wave significant wave height long-term trend prediction method is characterized by comprising the steps that (1) weather forecast data of an ERA-Interim reanalysis data set at each time frequency are collected, (2) coordinates of all lattice points are obtained, (3) SLP anomaly and standard deviation are calculated, (4) principal component analysis of the SLP anomaly is conducted, (5) Box-Cox transformation is conducted on sea area data, (6) a predictive factor of sea wave significant wave height is calculated, (7) the standard deviation of the significant wave height and the predictive factor is calculated, (8) the predictive factor is applied into a prediction model, (9) a significant wave height lagged value is applied into the model, (10) SLP field prediction on the basis of EOF is carried out, (11) predictive factor optimization selection is conducted, (12) the sea wave significant wave height is predicted through the model, (13) the prediction level is evaluated, (14) the sea wave significant wave height long-term trend is calculated, and (15) a significant wave height long-term trend chart is drawn. According to the sea wave significant wave height long-term trend prediction method based on the reanalysis data, the significant wave height long-term trend of multiple time frequencies can be predicted, and accuracy is high.

A healthcare management system and method identify individuals at risk of high near-term healthcare use. On step of the method includes collecting information from an individual for a predetermined set of predictive factors. Another step of the method includes assigning, based upon the information from the individual, a separate value to each predictive factor of the predetermined set of predictive factors. The method further includes the step of generating, based upon a predetermined predictive model and the separate values assigned to the predetermined set of predictive factors, a risk level of the individual utilizing healthcare services at a predetermined level within a prospective time span.

A predictive personal video recorder (PVR) can include a tuner, a buffer, a predictive tuner, and a predictive buffer. The tuner can be configured to allow viewing of a currently-selected digital video channel chosen from a plurality of digital video channels received by a predictive PVR. The buffer can be configured to store video data of the currently-selected digital video channel, allowing time-shifted viewing of the currently-selected digital video channel. The predictive tuner can be configured to capture video data for a predicted digital video channel. The predicted digital video channel can be selected based upon a predictive factor. The predictive buffer can be configured to store the captured video data for the predicted digital video channel received by the predictive tuner to allow time-shifted viewing of said predicted digital video channel.

The invention relates to a method, system and a device for assessing the risk for periodontitis progression or for developing periodontitis, and a method, system and a device for prognosticating the outcome of a treatment procedure for treating periodontitis, on the basis of a risk score calculated on the basis of weight factors, which may be associated with numerical values, assigned to a plurality of measures corresponding to a plurality of predictors promoting periodontitis comprising host predictors, local predictors, and systemic predictors for periodontitis progression or for developing periodontitis for a patient. The invention provides among other things an objective tool that allows for preventive measures to be taken in time before severe and often irreversible damage caused by periodontitis has occurred, by taking into account the most important risk predictors promoting periodontitis, and in particular takes into account the synergy between these predictors. The invention also relates to a computer readable storage medium, on which there is stored a computer program comprising computer code adapted to perform one or more of the above-mentioned methods, and furthermore such a computer program.

The invention relates to a microblog spread prediction method based on user influences and contents. The method includes the following steps that firstly, by means of the scrapy technology, personal information of two users, the forward relationship between the two users and forwarded microblogs are crawled; secondly, the user influences are extracted through the RankPage influence analysis technology to form a user authority predictive factor; thirdly, the percentage of fans forwarded microblogs in all published microblogs in unit time is used, and a fan forward activeness predictive factor is extracted; fourthly, the microblog contents are subjected to importance analysis through the TF-IDF word weight technology to extract a microblog importance predictive factor; fifthly, the extracted forward relationship is divided into 10 microblog forward training sets and microblog ignoring training sets through the snowball sampling method; sixthly, the training sets are trained by means of a monitored Bayesian network till classifier parameters are converged. By means of the method, the prediction accuracy of Sina microblogs forwarded by special fans can be improved.

Intended meanings of user input character strings having multiple interpretations and having a single intended meaning are predicted in real time as they are entered by applying a profile thereto. The profile is generated by application of unsupervised and supervised learning techniques which identify predictive factors. Substantially simultaneously with application of the profile to the character strings, a plurality of remotely located profiles are accessed. The remotely located profiles are determined by application of supervised and unsupervised techniques to a plurality of third parties and are identified and ranked to determine those profiles that most nearly match one or more predictive factors in the user profile, where the predictive factors are weighted based on the ranking, the weighted predictive factors are combined with the user profile to determine the intended meaning of the character string from among the multiple interpretations, and the intended meaning is displayed to the user.

The invention belongs to the technical field of a surgery part infection risk predicating scoring and a system based on independent danger factors. Particularly the invention relates to a risk predicting evaluation scoring method for neck anastomosis esophagus cancer resection surgery part infection and a system thereof. According to the independent danger factors which are firstly screened, an infection predicting model is established. According to an assignment score which is generated by a formula, the influence of the danger factors to a final result-surgery part infection can be more visually presented, thereby facilitating clinical early prediction and evaluation to possibility of surgery part infection after the surgery. Individualized infection risk evaluation supplies a basis forkey disposition of an infection preventing measure. The content of the invention is not reported in China and other countries. Through data statistics analysis, the independent predicting factors forneck anastomosis esophagus cancer resection surgery part infection are screened. A mathematical model is used for establishing the predicting scoring system for the surgery infection risks, thereby laying a certain basis for individually predicting the surgery part infection, and supplying guidance for clinical early intervention measure application.

The invention discloses a coal mining equipment predictive maintenance method through regression analysis combined with two-dimensional projection, and relates to a coal main fan and an elevator. The diagnosis method includes the steps of extracting a vibration signal from an equipment monitoring system to obtain 24 characteristic indexes used for describing equipment operating states through data analysis and calculation, respectively extracting a time sequence for each of the 24 characteristic indexes, carrying out regression analysis to obtain predictive factors corresponding to the 24 characteristic indexes respectively, projecting the predictive factors on a two-dimensional space by means of two-dimensional projection, building a fitting function of predictive factor projection values and corresponding characteristic index values, calculating future values of the 24 characteristic indexes, projecting the future values of the 24 characteristic indexes in a best projection image direction matrix, judging the trend of the equipment future operating state according to the distribution situation of the projection values, and accordingly achieving predictive maintenance of coal mine equipment.

The invention discloses application of a product for detecting ceramide in preparation of a kit. The application of the kit is at least one of the following (a1)-(a3): (a1) screening or assisting in diagnosing heart failure patients; (a2) evaluating or assisting in evaluating the prognosis risk of heart failure patients; and (a3) evaluating or assisting in evaluating the adverse event risk of heart failure patients. Research results show that the ratio of plasma ceramide cer (d18: 1 / 16: 0) to cer (d18: 1 / 24: 0) is an important predictive factor for the occurrence of adverse events of heart failure in patients with heart failure at the final stage, and is independent of currently used lipid markers, which facilitates the identification of high risk patients requiring more positive therapeutic interventions.

The invention provides a method for eliminating an influence on infrared spectrum detection of soil total nitrogen content from moisture. The method comprises: scanning a soil sample set with spectrum to obtain near-infrared absorbance value of each soil sample in the soil sample set in a scheduled wavelength range, designing a normalized moisture absorption index MAI, classifying soil according to a gradient of the MAI and putting forward a corresponding correction factor, taking advantaging the correction factor to correct original absorption photometry and obtaining corrected absorption photometry data, selecting a plurality of wavelength corrected absorption photometry data as total nitrogen predictive factors, and taking advantages of a BP neural network to build a soil total nitrogen detection module to detect the total nitrogen content. By means of the method for eliminating the influence to infrared spectrum detection of the soil total nitrogen content from the moisture, experiment pretreatment process is reduced, the influence on the accuracy of the infrared spectrum detection of the soil total nitrogen content from the soil moisture is eliminated, and detection efficiency is improved.

The invention discloses an electric transmission line icing grade long-term prediction method based on support vector classification. The electric transmission line icing grade long-term prediction method comprises the steps of grading power grid icing, extracting icing grade predictive factors, conducting data normalization on the power grid icing grade predictive factors, establishing a classification model based on multi-classification support vectors and conducting long-term prediction on the power grid icing grade. By the adoption of the prediction method, the power grid icing grade long-term prediction precision can be effectively improved, reliable data support is provided for power grid ice resistance, the prediction method has important significance in taking power grid ice resistance measures in advance and guaranteeing the reliability of operation of a power grid, the concept is clear, operation is convenient, the practicability is high, the nonlinear mapping relationship between the power grid icing predictive factors and the power grid icing grade can be effectively processed, and a good training and learning performance is achieved for small sample data.

The invention discloses a medium-and-long-term runoff predicting method based on an improved Elman neural network, comprising the following steps: S1, preprocessing data; S2, selecting a predictive factor and extracting a principal component; S3: constructing an Elman neural network model; S4, subjecting the network model to 10-fold cross validation; S5, if forecast accuracy meets a requirement, saving a network and forecast result, otherwise, going to the step S2; and S6: if the number of forecast times reaches a requirement, calculating the average number of all forecast results, otherwise,going to the step S4. The method has fast learning convergence speed and can avoid falling into local optimum values.

An accurate predictive model that identifies the patient / members within the healthcare system at high risk of hospital admission for a wide range of morbidities, or co-morbidities, and that allows subsequent intervention to manage those patients identified as high risk to an acceptable level. There is a further need for such a predictive model that focuses on specific groupings of conditions, e.g., behavioral health predictive modeling. There is also a need for a method that provides for intervention to manage the risk to the identified patients / members. One embodiment of the present invention discloses and claims a method of high-risk patient identification and management. In one aspect, the inventive method may comprise compiling a listing including all individuals with any primary behavioral health diagnosis over a specified time period; merging the listing with at least one data source to extract at least one behavioral health-related predictive factor; generating, based on at least one predictive model, a predictive output comprising the probability that the individuals listed will require a future behavioral health-related hospital admission; identifying the high-risk individuals from the predictive model output; and intervening with the high risk members to identify and modify, to the extent possible, the risk factors that place the member at high risk.

The invention provides a data-driven spacecraft state prediction method, first a prediction sample is input, predictive factors are determined, a projection value is calculated, then a projection pursuit auto-regressive model is established, a projection indicator function is built, an acceleration genetic algorithm is used to optimize a projection direction, the projection pursuit regressive model is finally determined, and parameter prediction is performed. The data-driven spacecraft state prediction method provided by the invention can find the structure or characteristic from telemetry data themselves which do not conform to normal distribution or have little prior information, and can solves the problem of a nonlinear structure in linear projection. The data-driven spacecraft state prediction method has a good prediction effect, and has positive significance to spacecraft fault prediction and diagnosis.

The invention discloses a multi-year short-term climate prediction method based on a multi-factor regression set, a cross-check set, and a month-by-month rolling set, and belongs to the field of short-term climate prediction theory and methods in meteorology. The method of the present invention includes, firstly, according to the large-scale barotropic eddy equation, using the potential function field and the flow function field reflecting the effects of the advection term and the exogenous forcing term in the theoretical equation as the predictor field; then, considering the combination of different factor time effects and Its independence, comprehensive application of multi-factor regression set, cross-check set, month-by-month rolling set, to predict the target. The invention has improvements in the selection of the basic factor field, the determination of the final factor and the application of various sets. Its advantages lie in the characteristics of long prediction timeliness, simple calculation relative model, good prediction effect, strong regional adaptability, etc., and can be applied to prediction problems of other scales and different element variables.

A method for treating patients afflicted with cancer, wherein the patients are treated with a compound including tyrosine kinase inhibitor, mast cell inhibitor or c-Kit inhibitor, in particular masitinib, optionally in combination with at least one antineoplastic agent. The tyrosine kinase inhibitor, mast cell inhibitor or c-Kit inhibitor, and the optional at least one antineoplastic agent, are administered in a dosage regimen that includes a therapeutically effective amount. Also described are methods for predicting therapeutic response to the treatment in a given patient and therefore identification of applicable patient subpopulations based upon these predictor factors; sometimes referred to as biomarkers. One method is based upon the clinical marker of pain intensity. Another method is based upon gene expression predictive biomarkers assessed via RNA expression in peripheral blood cell samples collected prior to treatment with the compound, which is also used for treating patients afflicted with pancreatic cancer.

The invention discloses a long-term rainfall prediction model construction method, a long-term rainfall prediction method and a long-term rainfall prediction device. The construction method comprises the following steps: acquiring a sample set; screening explanatory variables in the sample set based on an error discovery rate control method of multi-hypothesis testing and a random forest model to obtain predictive factors influencing precipitation in corresponding months of the next year; and carrying out random forest modeling according to the prediction factors influencing the precipitation in the corresponding month of the next year and the precipitation in the corresponding month of the next year, and training to obtain a long-term precipitation prediction model of the precipitation in the corresponding month. According to the long-term rainfall prediction model construction method, the long-term rainfall prediction method and the long-term rainfall prediction device provided by the invention, variable screening can be optimized from an experience-dependent method to a data-dependent method, the problem of false positive error rate of a random forest method during empirical variable screening is improved, and the accuracy and reliability of model prediction can be effectively improved.

The invention belongs to the fields of functions and applications of gene and protein, and relates to Flotilli-2 as a target in screening nasopharyngeal carcinoma metastasis inhibiting drugs and an application of the Flotillin-2. The invention provides a target for screening nasopharyngeal carcinoma metastasis inhibiting drugs, wherein the target is Flotillin-2. The invention also provides an application of the Flotillin-2 as a drug target in screening nasopharyngeal carcinoma metastasis inhibiting drugs. The invention also provides a drug for inhibiting nasopharyngeal carcinoma metastasis, which takes the Flotillin-2 as a target. The drug is siRNA of Flotillin-2. The invention discloses the relation between the Flotillin-2 (Flot2) and a TGF-beta signal path in nasopharyngeal carcinoma as well as effects of the Flot2 and the TGF-beta signal path in nasopharyngeal carcinoma metastasis for the first time. In the nasopharyngeal carcinoma, the Flot2 is an independent predictive factor of patient poor prognosis, an important component of the TGF-beta signal path and an indispensable important node in a process of stimulating emergency medical treatment (EMT). Silent Flot2 may become one of important means of resisting EMT caused by TGF-beta. Therefore, the invention provides a theoretical foundation and a clinical base for researching a new target and a new strategy in inhibiting nasopharyngeal carcinoma metastasis.

The invention discloses a binary classification oriented factor screening method based on boosted regression trees. The method comprises the following steps that: (1) searching data, and establishinga target variable-predictive factor dataset; (2) on the basis of the target variable and all factors, utilizing the boosted regression trees to carry out modeling, and calculating and sorting factor importance; (3) carrying out correlation analysis on all factors, analyzing a Pearson correlation matrix, and carrying out screening; (4) on the basis of the target variable and the retained factor, utilizing the boosted regression trees to establish a new model, calculating a predictive deviation, calculating and sorting the factor importance, and removing the factor with the lowest importance until the amount of the retained factors is less than or equal to 2; and (5) comparing the predictive deviation of each boosted regression tree model in the (4), and taking all factors adopted by the boosted regression tree model with the smallest predictive deviation as an optimal factor combination. By use of the method, a quantitative factor selection system is established, results are reliable, and an application field is wide.

The invention provides a data missing repairing method and a data missing repairing device. The data missing repairing method comprises the following steps: determining sequences corresponding to the type of a sequence to be repaired from a database as candidate sequences; selecting sequences from the candidate sequences as adjacent sequences of the sequence to be repaired, wherein degrees of mutual correlation between the sequences and the sequence to be repaired meet preset requirements; with a data value of a data integrity interval in the sequence to be repaired as a target and data values corresponding to the data integrity interval in the adjacent sequences as predictive factors, establishing a regression model; with data values corresponding to a data missing interval of the sequence to be repaired in the adjacent sequences as predictive factors, regressing the data value of the data missing interval of the sequence to be repaired by using the regression model. Through the data missing repairing method and the data missing repairing device, the data repairing accuracy can be improved.