Abstract: The present disclosure is related to a process comprising steps of fermentation, separation and purification that can be carried out in a centralized facility with large scale fermentation or in a decentralized facility with smaller scales of fermentation to make value added products from substrate not limiting to gaseous substrates effectively. The fermentation involves fermentative production of a marketable product using reactors or fermentors which are optimized for influential parameters such as gaseous substrate, gas hold up, mass transfer coefficient, etc. The separation step involves an effective way of separating desired product from fermentation broth by employing filtration, precipitation or adsorption techniques thereby enabling easy transportation of the product in case of a decentralized facility. The final stage of the process, either in centralized or decentralized facility, includes the elution of retained product and further purification of the same using downstream processing techniques.

Abstract: Electronic system level (ESL) design and verification of the present disclosure is utilized to provide an electronic simulation and modeling of function safety and fault management of an electronic device. A method for simulating a safety circuit includes providing an electronic architectural design to perform one or more functional behaviors of the electronic device in accordance with an electronic design specification. The method further includes modeling the safety circuit of the electronic architectural design and one or more other electronic circuits of the electronic architectural design that communicate with the safety circuit. The method further includes simulating, using the modeling, operation of the safety circuit while the electronic architectural design is performing the one or more functional behaviors. The method also includes determining whether the simulated operation of the safety circuit satisfies the electronic design specification.

Abstract: Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline.

Abstract: Methods and systems for determining a systematic defect in a circuit under test is provided. Elements of the circuit under test converted into scan cells. A first scan chain that includes a first plurality of scan cells is formed. Each scan cell of the first plurality of scan cells of the first scan chain are of a first cell type. The first scan chain contains a first scan input and a first scan output. A first test pattern is applied at the scan input and a first test output is collected for the applied first test pattern at the first scan output. The collected first test output is compared with a first expected test output. The first cell type is marked to be a suspect for a systematic defect when the first test output is different from the first expected test output.

Abstract: An apparatus includes a database and a processor. The database stores a set of services, each of which produces outputs when provided inputs. The processor determines a machine learning policy that can be applied to a first service to determine a descriptor file assigned to the service. The first service can produce a first output when provided a first input. The descriptor file includes the first output type and the first input type. The processor applies the machine learning policy to the set of services to determine a set of descriptor files. The processor receives a request from a user for a second service that produces a second output given a second input. The processor then locates a second descriptor file that includes the second output type and the second input type, determines a third service assigned to the descriptor file, and sends the third service to the user.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.

Abstract: A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.

Abstract: Electronic system level (ESL) design and verification of the present disclosure is utilized to provide an electronic simulation of various loads on one or more batteries of an electronic device resulting from the electronic device performing one or more functional behaviors. Before this electronic simulation occurs, the electronic device is modeled using the high-level software language or the high-level software format. For example, a battery discharge model, a regulator efficiency model, a power delivery network (PDN) model, or a component power model are used to model behaviors of the one or more batteries, regulator circuitry, power delivery network (PDN) circuitry, and other electronic circuits, respectively, of the electronic device.

Abstract: A method includes receiving a source code for executing a plurality of operations associated with a machine learning algorithm, classifying each operation into a fast operation group or a slow operation group, defining a neuron network for executing operations of the slow operation group, and mapping the neuron network to an initial machine learning hardware configuration. The method also includes executing operations of the slow operation group on the initial machine learning hardware configuration, modifying the initial machine learning hardware configuration in response to a determination that the slow group operation fails to produce an expected result in response to at least one set of inputs; and executing a fast group operation using a machine learning software code.

Abstract: A method and system are devised of moving at a NAS (1) a client (12, 13) with a MAC address, from a first VLAN to a second VLAN. A leaf is comprised of at least one intermediate L2 bridge/switch (5, 9) being connected to the NAS (1). The client (12, 13) is being connected to one (9) of the at least one intermediate L2 bridge/switches (5, 9) in the leaf. The method and system involve sending at the NAS (1) a first message downlink (31, 36) to intermediate L2 bridge/switches (5) in the leaf directly connected to the NAS.

Abstract: Generally described, one or more aspects of the present application correspond to techniques for modifying volume encryption status, either by creating an encrypted copy of an unencrypted source volume or by re-encrypting the replica with a different key than the source volume. This can be accomplished using an intermediary transform fleet that stores the encryption key(s) and performs encryption (and decryption, in cases where the source is encrypted). Further, these techniques can implement a state refresh for any client attached to the volume in order to equip the client to handle the different encryption state of the encrypted volume.

Abstract: Techniques of thermoelectric power generation are described. In an example, a power generation system (100) may include a thermoelectric unit (102), a DC booster (104) and a supercapacitor unit (106). The thermoelectric unit (102) may generate electivity using heat, such as heat obtained from human body. The DC booster (104) may step up the voltage generated by the thermoelectric unit (102). The supercapacitor unit (106) may store electrical energy generated by the thermoelectric unit (102) and start discharging after a threshold level. The power generation system may be implemented to power a wearable device (304), such as fitness tracker and smartwatch.

Abstract: Disclosed is a vehicle acceleration compensation system, including an accelerator pedal, throttle, and a transmission configured to shift between two or more fixed gears, wherein each gear relates the motor power to a vehicle torque. The system also includes a control unit configured to receive data from one or more sensors. The control unit includes a real-time throttle map relating the accelerator pedal position to the throttle position, such that a given accelerator pedal position directs a corresponding target throttle position, and a real-time shift map relating a desired transmission gear to a current transmission gear, current vehicle speed, and current throttle position, such that a given vehicle speed, given throttle position, and given transmission gear directs a corresponding target transmission gear. In response to sensor data, the control unit updates the throttle map and shift map such that the vehicle torque is altered to produce a desired acceleration value.

Abstract: The present invention is directed to PVRIG polypeptides and their uses.

Abstract: The current disclosure relates to methods and compositions for improving plant varieties through plant breeding and plant genetics. For instance, the disclosure concerns increasing the recombination frequency of a heterozygous trait genetically linked to a second trait within plants. Further, the disclosure concerns breaking the genetic linkage between a first allele and a second allele.

Abstract: The present invention provides a process for upgrading LCO to (i) high octane gasoline and high aromatic feedstock for aromatic complex, (ii) Utilization of FCC for cracking unconverted diesel range stream from hydrocracker to high octane gasoline. This invention discloses the process to improve the quality (e.g., RON, Sulfur) of gasoline by utilizing FCC and hydrocracking processes.

Abstract: A device includes a memory, a transceiver, and a processor. The processor receives a request (including a first identifier assigned to a first mobile device) from the first mobile device. In response, the processor determines that a server connection is unavailable, adds a second identifier to the request, and broadcasts the request. The processor then receives a first path (including the first and second identifiers along with a third identifier assigned to a second mobile device) from the second mobile device and sends it to the first mobile device. The processor then receives a first piece of data and a second path (including the first and second identifiers along with a fourth identifier assigned to a third mobile device) from the first mobile device. In response, the processor identifies the third mobile device and sends the first piece of data and the second path to the third mobile device.

Abstract: A testing probe structure for wafer level testing semiconductor IC packaged devices under test (DUT). The structure includes a substrate, through substrate vias, a bump array formed on a first surface of the substrate for engaging a probe card, and at least one probing unit on a second surface of the substrate. The probing unit includes a conductive probe pad formed on one surface of the substrate and at least one microbump interconnected to the pad. The pads are electrically coupled to the bump array through the vias. Some embodiments include a plurality of microbumps associated with the pad which are configured to engage a mating array of microbumps on the DUT. In some embodiments, the DUT may be probed by applying test signals from a probe card through the bump and microbump arrays without direct probing of the DUT microbumps.

Abstract: A method and system are devised of moving at a NAS (1) a client (12, 13) with a MAC address, from a first VLAN to a second VLAN. A leaf is comprised of at least one intermediate L2 bridge/switch (5, 9) being connected to the NAS (1). The client (12, 13) is being connected to one (9) of the at least one intermediate L2 bridge/switches (5, 9) in the leaf. The method and system involve sending at the NAS (1) a first message downlink (31, 36) to intermediate L2 bridge/switches (5) in the leaf directly connected to the NAS.

Abstract: A method includes receiving a source code for executing a plurality of operations associated with a machine learning algorithm, classifying each operation into a fast operation group or a slow operation group, defining a neuron network for executing operations of the slow operation group, and mapping the neuron network to an initial machine learning hardware configuration. The method also includes executing operations of the slow operation group on the initial machine learning hardware configuration, modifying the initial machine learning hardware configuration in response to a determination that the slow group operation fails to produce an expected result in response to at least one set of inputs; and executing a fast group operation using a machine learning software code.