183results about "Biomolecular computers" patented technology

A method of simulating a bio-transport system comprising: (a) characterizing one or more elements to represent a bio-transport system of an organism or a portion thereof; (b) constructing one or more mathematical representations that model one or more bio-transport dynamics for each element based on the characterization of the elements to form a configured simulation model; (c) initializing the configured simulation model; (d) executing the configured simulation model to obtain bio-transport dynamics data for one or more elements; and (e) outputting information to a user based on at least a portion of the bio-transport dynamics data.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

A self-assembly method for circuit patterns includes generating a set of tiles, each of the tiles corresponding to a segment of molecules, the set of tiles comprising a set of rule tiles and a set of boundary tiles, each tile having one or more binding regions; assigning a label from a set of labels to each binding region; self interacting, with attractive forces, one or more of the tiles with one or more other tiles among the set of tiles; associating using selective interaction of at least one boundary tile from the set of boundary tiles with at least one rules tile from the set of rules tiles based upon at least a label from the one boundary tile and at label from the one rules tiles; and bonding at least one binding region of the one boundary tile with at least one binding region of the one rules tile.

Belonging to flash memory chip lifetime prediction technologies, the invention in particular relates to a mathematical model based flash memory chip lifetime prediction method. The method includes: firstly using a flash memory chip test system to collect the physical information and lifetime information of a sample flash memory chip, then conducting operation processing on the data information obtained by testing through intelligent algorithm to obtain a flash memory chip lifetime prediction model, finally carrying out a few tests to obtain the physical information of the to-be-predicted flashmemory chip, and inputting the physical information into the prediction model to obtain a predicted lifetime value of the flash memory chip. The flash memory sample test method provided by the invention employs constrained random data as the test data set to perform testing, can more effectively simulate the data operation of the flash memory chip in the actual use process, and the obtained flashmemory physical information and lifetime information are more valuable.

A method for configuring nanoscale neural network circuits using molecular-junction-nanowire crossbars, and nanoscale neural networks produced by this method. Summing of weighted inputs within a neural-network node is implemented using variable-resistance resistors selectively configured at molecular-junction-nanowire-crossbar junctions. Thresholding functions for neural network nodes are implemented using pFET and nFET components selectively configured at molecular-junction-nanowire-crossbar junctions to provide an inverter. The output of one level of neural network nodes is directed, through selectively configured connections, to the resistor elements of a second level of neural network nodes via circuits created in the molecular-junction-nanowire crossbar. An arbitrary number of inputs, outputs, neural network node levels, nodes, weighting functions, and thresholding functions for any desired neural network are readily obtained by the methods of the present invention.

The invention discloses a short-term electric load prediction method based on improved genetic algorithm for optimizing extreme learning machine. A hill climbing method is used to perform preferentialselection again in the progeny population, an initial individual is selected, another individual in a close area is select, their fitness values are compared, and one individual which has good fitness values is leaved. If the initial individual is replaced or a better individual cannot be found in several iterations, iteration is stopped, the search direction of the genetic algorithm through thehill climbing method is optimized, obtaining an optimal weight value and a threshold value, a network optimization prediction model are obtained, a network optimization prediction model is obtained, the network optimization prediction model and prediction results of BP network and the extreme learning machine are comparative analyzed, including selection of input and output of the prediction network model, algorithm of improved genetic algorithm for optimizing extreme learning machine, and analysis of prediction results. The short-term electric load prediction method based on improved geneticalgorithm for optimizing extreme learning machine has faster training speed and more accurate prediction results, and is suitable for modern short-term electric load prediction with plenty of influence factors and huge data volume.

The invention provides various signal processing devices for integrating two or more biological signals (e.g., the presence, absence or concentration of specific ligands, etc.) to generate a status output, or a response that modulates one or more biological activities based on the status of the biological signals. The various described signal processing / integration mechanisms may be combined with one another to provide the device with more flexibility in integrating high-order cellular information. The signal processing devices of the invention have many uses in various biological systems, including gene expression control or ligand-concentration sensing.

Described herein are novel biological converter switches that utilize modular components, such as genetic toggle switches and single invertase memory modules (SIMMs), for converting analog inputs to digital outputs, and digital inputs to analog outputs, in cells and cellular systems. Flexibility in these biological converter switches is provided by combining individual modular components, i.e., SIMMs and genetic toggle switches, together. These biological converter switches can be combined in a variety of network topologies to create circuits that act, for example, as switchboards, and regulate the production of an output product(s) based on the combination and nature of input signals received.

The invention discloses a visible component classification method based on SVM. The method comprises the following steps of in a SVM training phase, A, acquiring a sample picture containing several kinds of visible components and taking the sample picture as a training sample; B, segmenting the sample picture into several visible component images; C, extracting an image characteristic of the visible component images, classifying the visible component images based on the image characteristic and constructing several cascaded visible component picture databases; D, constructing several cascaded SVM classifiers and using the corresponding visible component picture database to train; in a visible component identification classification phase, E, extracting the image characteristic of the visible component images to be identified and classified; F, based on the image characteristic, assigning the visible component images to the corresponding SVM classifiers to carry out identification and classification. The plurality of cascaded SVM classifiers are used to carry out multilevel classification on a visible component graph so that classification accuracy of the SVM classifiers to the visible components is effectively increased.

A method and an apparatus for controlling a distributed automation system, wherein the method comprises determining global constraint rules according to the cooperative relationships between function blocks in the distributed automation system, and establishing a mathematical model based on Membrane Calculus, performing the instantiation of the established mathematical model based on Membrane Calculus according to an executor corresponding to each of the function blocks in the distributed automation system, so as to produce control logics for output to the distributed automation system. If disturbance information is received from the distributed automation system, altering the parameters of the instantiation of the mathematical model based on Membrane Calculus are altered using the disturbance information, so as to produce control logics for outputting to said distributed automation system. The present invention is capable of effectively dealing with all types of unpredictable disturbances, thus improving the adaptability thereof.

The invention provides, inter alia, recombinase-based systems that provide for integrated logic and memory in living cells such as mammalian cells. The nucleic acid cassettes, switches, and systems described herein allow for control of gene expression or gene regulation. The invention also provides nucleic acid-based switches for adopted T-cell therapy.

The present invention is of a molecular entity comprising a polypeptide core and nucleic acid sequences attached thereto which is capable of forming a universal logic gate such as the NAND gate. Specifically, the present invention can be used to device a molecular computing unit which can be used to detect biological markers and administer therapeutic moieties.

New and improved methods and systems for modeling the performance of selected company metrics. Multiple, non-traditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and / or the performance of a selected industry or industry sector may be analyzed.

The invention belongs to the crossing field of computer science and traffic engineering, and relates to an indoor pedestrian microscopic simulation method based on a cellular automaton. The method comprises following steps of firstly, carrying out gridding on an indoor pedestrian region, thus obtaining a cellular space corresponding to a cellular automaton model; dividing the cellular space into some convex polygon regions; secondly, judging whether there are pedestrians in each region; stating the temporary destination of each pedestrian; calculating the transfer probability of each pedestrian; simulating movements; observing whether conflicts appear or not; finally, solving the conflicts; and updating the practical position of each pedestrian until the pedestrians arrive at the corresponding destinations. According to the method, the simulation model can correspondingly respond to complex walk environments and not merely distinguish the pedestrians and barriers; moreover, the self-organization phenomena displayed in the regional pedestrian flow indoor evacuation process are simulated; the model simulation effect is effectively improved; and the method is more suitable for simulating and analyzing movement evolution of the pedestrian flow in the indoor complex environments.

The invention provides, inter alia, recombinase-based systems that provide for integrated logic and memory in living cells.

A device, system and method for molecular computing which not only includes a suitable, renewable power source, but actually is able to receive power through the performance of the computations themselves. The molecular computing machine of the present invention actually employs the free-energy difference between its input and output to accomplish a computation, preferably by using its input DNA molecule as a partial source of energy, or alternatively by using the input DNA molecule as the sole source of energy. This molecular finite automaton preferably transforms an input DNA molecule into an output DNA molecule by digesting the input as it computes.

A system and method for selecting an optimal path in a multi-media multi-path network. The system for selecting an optimal path in a multi-media multi-path network includes a memory storing a program for selecting an optimal path in the multi-media multi-path network and a processor for executing the program, wherein the processor uses a network performance parameter, which serves as state information, as an input value of a reinforcement learning algorithm and selects an optimal path using a Q-table obtained by applying the reinforcement learning algorithm.

Embodiments of the invention provide a land use state prediction method, apparatus and system. The method comprises the steps of obtaining a predetermined type of image data of a target region, and generating a corresponding raster layer, wherein the raster layer at least comprises a plurality of geometric factor layers and a water body layer; according to the geometric factor layers, determining a third raster layer; training the third raster layer by utilizing a classification and regression tree to determine a development applicability probability; according to a constraint condition determined by the water body layer and the development applicability probability, building a cellular automaton model; and according to the cellular automaton model, predicting a land use state of the target region to obtain a prediction result. According to the scheme, the training is performed through the classification and regression tree to determine the development applicability probability, and the cellular automaton model is built according to the development applicability probability to predict the land use state, so that the technical problem of inaccurate prediction of the land use state in an existing method is solved.

Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. A technique includes using machine learning to create or extract a laser input file for wear pattern from an existing garment. Machine learning can be by a generative adversarial network, having generative and discriminative neural nets. The generative adversarial network is trained and then used to create a model. This model is used generate the laser input file from an image of the existing garment with the finishing pattern. With this laser input file, a laser can re-create the wear pattern from the existing garment onto a new garment.

The invention provides an information spreading model based on a cellular automaton, comprising the evolution rule, parameter and algorithm of the model and main technological indexes for measuring states. On such a basis, user states are divided into sever types, a rumor spreading process is analyzed and researched, and a rumor spreading mechanism is revealed according to differences of different parameter comparison results. A rumor spreading control strategy is further provided, and a great suppression effect on a final information spreading extent is achieved. Simulation of the model built based on the cellular automaton starts with microcosm to reflect macroscopic behaviors of groups, a simulation result is more intuitional, meanwhile, the model is flexible in structure, and can randomly change the control strategy during an evolutionary process, which is incomparable with a traditional differential equation model. Real data and a simulation experiment result have a relative high fitting degree, so that the built model has the actual application value, and can provide a support for making the rumor control strategy.

The present invention relates to a new method for classification of spectral data comprising:a. analyzing at least two samples belonging to at least one cluster through recording of a spectrum;b. for each spectrum determining the peaks and the spectral value at which they occur;c. calculating the probability (p) of occurrence for each peak for every cluster and from this the odds ratio p / (1−p) for each peak;d. preparing a spectrum from a sample to be classified with the same technique as in step a);e. determine the peaks in the spectrum obtained in step d);f. calculate the likelihood of identity for each cluster by multiplying per cluster the odds ratio in said cluster for each peak found in the spectrum of step d); andg. assign the sample tested in step d) to the cluster that provides the largest number as a result of step f).The invention also comprises a system for performing such a method and the use of such a system for classification of spectral data.

The invention discloses a secure evaluation method of an online public opinion situation, a terminal and a computer storage medium. On one hand, the self regulation capability of an online public opinion ecological system is diagnosed by a mathematical modeling method, and the secure and insecure evaluation scale in the online public opinion governance is set according to the maximum bearing threshold value of the regulation capability, so that the problem of inaccurate prediction on the online public opinion in the prior art is effectively solved. On the other hand, by a scientific method, a plurality of online public opinion general data of one event is gathered and collected to form an integral situation and is visualized; and whether the current situation of the event needs the institutionalized management and control or needs more space to develop and to generate the public opinion value is judged on the basis of the evaluation scale obtained on the basis of the self regulation capability of the online public opinion ecological system, so that great convenience is brought to relevant technicians to monitor the online public opinion.

The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

The invention discloses a cellular automaton-based arid region natural oasis space dynamic simulation method. The cellular automaton-based ecological water delivery driven arid region natural oasis space dynamic simulation method includes:, based on normalized vegetation indexes, underground water burial depth observation data and other multi-source information obtained through remote sensing images, simulating the total oasis area by adopting an underground water bearing capacity function and an oasis area dynamic function; constructing a cellular NDVI, a neighborhood vegetation rate, a neighborhood flooding rate and a cellular state change probability function according to the NDVI sequence; calculating the coating conversion probability at the t + 1 moment by adopting the cellular NDVI,the neighborhood vegetation rate and the neighborhood flooding rate at the t moment; and calculating a coating conversion score of each cell by adopting a weighted average method, sorting the coatingconversion scores from large to small, setting a coating conversion dynamic threshold in combination with the oasis area simulation result, finally judging how to change the state of the cell in combination with the coating conversion static threshold, obtaining an oasis space dynamic simulation result, and reflecting a model simulation effect through precision evaluation.

The invention discloses a pheromone prediction-based ant colony multi-cell tracking system. The system comprises four modules: a pheromone field prediction module, an ant decision module, a pheromonefield Gaussian fitting module and a state estimation module. Through analysis of the modules, multi-cell tracking is realized. After an original picture is input, based on a cell pheromone field Gaussian fitting result of a previous frame, pheromone field prediction is performed by utilizing a Gaussian mixture model; a pheromone field is constructed by utilizing a bell curve-based pheromone diffusion model in a pheromone gradient-based ant working mode; and through a K-mean clustering method, cell position estimation is performed after removal of a false target caused by clutters, and finallyassociation is performed by utilizing cell distance features to obtain cell motion tracks, thereby realizing the multi-cell tracking.

The invention relates to the field of intelligent optimization algorithms and discloses a method for improving population diversity in a gravitational search algorithm. The particle population diversity is calculated in each iteration process for performing optimization search through the gravitational search algorithm. When the population diversity is larger than the maximum threshold, each particle gets close to the current best position and the previous best position thereof, and the particles perform the suction operation of a bacterial chemotaxis process to improve the local optimization ability; when the population diversity is smaller than the minimum threshold, each particle gets away from the current worst position and the previous worst position, the particles perform the exclusive operation of the bacterial chemotaxis process to increase the population diversity; when the population diversity is located between the maximum diversity threshold and the minimum diversity threshold, the original velocity updating formula in the gravitational search algorithm is used. According to the method for improving the population diversity in the gravitational search algorithm, the exclusive operation of the bacterial chemotaxis process is led to the gravitational search algorithm to improve the particle population diversity and avoid premature convergence, and accordingly the optimization ability of the algorithm is improved.

New and improved methods and systems for modeling the performance of selected company metrics. Multiple, non-traditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and / or the performance of a selected industry or industry sector may be analyzed.