Abstract: A commercial laundry appliance may include a cabinet having a front panel defining an access opening configured to receive laundry items, at least one sensor configured to detect the presence of a user, a human-machine-interface (HMI) having a display and configured to present information to the user, and a processor configured to instruct the (HMI) to display updated display content in response to the sensor detecting the presence of the user.

Abstract: Disclosed are methods, systems, components, and compositions for engineering enzymes. Particularly disclosed are methods, systems, components, and compositions for engineering phenylalanine ammonia-lyase (PAL) enzymes and isolating variant PAL enzymes with enhanced enzymatic properties. The variant PAL enzymes disclosed herein or obtained by the methods disclosed herein may be utilized for treating diseases or disorders characterized by elevated blood levels of phenylalanine, such as phenylketonuria (PKU).

Abstract: Water leakage detection is provided. One or more water sensing devices are placed at different locations with respect to a laundry treatment appliance. A computing device is in communication with the one or more water sensing devices. The computing device is programmed to receive sensor data from the one or more water sensing devices. Responsive to the sensor data indicating a leak, the computing device performs one or more corrective actions to address the leak.

Abstract: Systems and methods presented herein generally relate to a method that includes receiving water at a centralized facility. The method also includes receiving sand from one or more sand mines at the centralized facility. The method further includes receiving one or more chemicals at the centralized facility. In addition, the method includes using processing equipment of the centralized facility to process the water, the sand, and the one or more chemicals to produce a fracturing slurry. The method also includes conveying the fracturing slurry from the centralized facility to one or more fracturing sites.

Abstract: An interactive toy system is disclosed. The interactive toy system is or includes a toy figurine with a head, a torso, at one or more appendages. The toy figurine also includes a first tag reader disposed in the torso, a second tag reader disposed in a first appendage of the one or more appendages, and an effects module configured to produce a sound effect, a light effect. Still further, the toy figurine includes control circuitry configured to cause the effects module to produce a first effect when the first tag reader reads a first tag and cause the effects module to produce a second effect when the second tag reader reads the first tag.

Abstract: An anti-fogging agent composition that can impart excellent anti-fogging properties to a hard surface of an eyewear lens or the like, and can be uniformly applied. An anti-fogging agent composition comprising a fluorosurfactant and a glycol ether, wherein the content of the glycol ether is 10 wt % or less.

Abstract: A method for automatically converting note-to-self to action reminders in an electronic device is provided. The method includes receiving an input comprising at least one word from a user of the electronic device, analyzing an anaphora representation or a deictic representation for each of the at least one word, and generating a reminder based on a context from the anaphora representation or the deictic representation.

Abstract: Unstructured data items are stored at an object storage service. A filtering requirement to be used to generate a result set for an access request is determined. Using a transformed representation of the filtering requirement, a target set of tokens of the filtering requirement which are to be obfuscated within a log record is identified. A log record that comprises substitute tokens for the target set of tokens is generated and stored.

Abstract: Methods, systems, apparatuses, and computer program products are described. A system, such as a multi-tenant database system, may store tenant-specific observability data for multiple tenants of the system. The system may detect an inefficiency related to a performance metric for a tenant of the multiple tenants based on a subset of the data associated with the tenant and corresponding to a threshold time window. In some examples, the system may analyze the subset of the data for the threshold time window to determine an insight indicating a cause of the inefficiency. The system may determine a suggested action for the tenant based on the insight indicating the cause of the inefficiency, and the system may send, for display at a user interface of a user device, an indication of the insight and the suggested action, the user device operated by a user associated with the tenant.

Abstract: Methods, systems, apparatuses, and computer program products are described. A multi-tenant database system may store a set of data logs indicating performance data for multiple tenants of the system. The system may calculate one or more aggregate performance metrics based on performance data for a tenant stored in the logs, where a performance metric of the one or more aggregate performance metrics may be based on design time data for the tenant, runtime data for the tenant, or both. The system may compare the one or more aggregate performance metrics to one or more performance thresholds defined for multiple tenants and may generate scalability scores corresponding to the one or more aggregate performance metrics for the tenant. The system may send, for display at a user interface of a user device operated by a user associated with the tenant, an indication of the generated scalability scores.

Abstract: Methods, systems, apparatuses, and computer program products are described. A database system may store data logs tracking performance data for multiple tenants of the database system. The database system may receive, via a user interface of a user device, a user input indicating a request to perform an analysis of performance data associated with one of the multiple tenants. The request may indicate a time period and a performance metric for the analysis. The database system may execute a query based on the analysis. The query may execute on a subset of the data logs associated with the tenant and the performance metric. The system may analyze a result of the query to determine one or more entities that affected the performance metric during the time period. The system may transmit an indication of the one or more entities for display at the user interface.

Abstract: Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive market data from a plurality of lines of business across an enterprise, wherein the market data is received in a raw, uncompressed format. Thereafter, the computing platform may assimilate and preprocess the market data to output vectored market data. The computing platform may perform a synthetic DNA stranding process on the vectored market data to create one or more strands of synthetic DNA market data, and output the one or more strands of synthetic DNA market data to a synthetic DNA client server, wherein the one or more stands of synthetic DNA market data is configured for input in a market volatility prediction model.

Abstract: Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive raw market data from a plurality of lines of business of an enterprise organization. Thereafter, the computing platform may preprocess the raw market data to obtain enterprise level market data, execute synthetic DNA stranding of the enterprise level market data to obtain synthetic DNA stranded market data, run the synthetic DNA stranded market data through one or more market volatility models, and compile results from the market volatility models on the synthetic DNA stranded market data. The computing platform may transmit results from the market volatility models on the synthetic DNA stranded market data. The transmitted results may be configured to display a market application interface that includes market volatility forecasting parameters based on results of the market volatility models.

Abstract: An adhesive sheet that is resistant to peeling when adhered to a movable part of a human is provided. The adhesive sheet is an adhesive sheet configured to be adhered to a movable part of a human, the adhesive sheet including: a sheet body having a first region, the first region having a surface on which a gel is laid out; and at least one slit formed from a peripheral part of the sheet body toward the inside of the sheet body.

Abstract: Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may initiate a set of instructions associated with performing an action on a synthetic DNA market data set associated with a plurality of lines of business across an enterprise organization. Thereafter, the computing platform may convert the set of instructions to a mutant nucleotide sequence, and insert the mutant nucleotide sequence into the synthetic DNA market data set. The computing platform may extract, using the mutant nucleotide sequence, target information from the synthetic DNA market data set, and validate the target information to detect one or more anomalies. The computing platform may remove the one or more data anomalies, and subsequently output a validated synthetic DNA market data set to a synthetic DNA client server.

Abstract: The output of an autoencoder model is used to provide reasons for classifying logged event data as anomalous. Multivariate input feature vectors based on the event data are applied to the autoencoder model to generate corresponding predicted multivariate feature vectors with respective feature elements. Each feature element of each vector corresponds to a respective type of event data (e.g., sign-in failures). A reconstruction loss is determined for each predicted feature vector and used to classify the predicted feature vector as anomalous or not anomalous. Reason(s) for an anomaly classification are determined by selecting one or more feature elements based on a level of percent contribution to the vectors loss and underprediction or overprediction, determined for each feature element of the anomalous predicted feature vector. Each reason is related to the type of event associated with a selected feature element. The reasons may be displayed in a security analytics UI.