Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Abstract: Unique key fields in structured data is a critical characteristic of data which plays significant role in data management. Profiling high volume of data for discovering all possible unique keys with high accuracy is a costly and time taking affair. A method and system for finding one or more unique entities in a data have been provided. The one or more unique entities obtained by this approach are complete and the response time is quick. The method is scalable to increasing volume of data and number of fields. The system is configured to perform the analysis process in multiple phases taking the less volume initially and increasing it gradually, thereby reducing the load on later phases as the unique results found with light volume phases. The method also comprises a time check mechanism after different stages if a user wants to do discovery for limited time.

Abstract: Spatio-temporal climate forecasts are analyzed and one or more resiliency policies for a supply chain are dynamically generated. The resiliency policy is embedded in a resiliency reasoning graph and a temporal feedback loop is performed based on user feedback regarding the generated resiliency policy and user interaction with the resiliency reasoning graph. One or more machine learning models are updated based on the user feedback and a joint optimization of the machine learning models is re-solved based on the user feedback. The resiliency policy is updated based on the updated machine learning models based on the user feedback and an operation of a supply chain is adjusted based on the updated resiliency policy.

Abstract: A method and system generate news headlines from user input parameters. The user input parameters include a specified geographic region of interest and an industry of interest. Climate data and carbon emissions data for the specified geographic region of interest is retrieved. Supply chain dependencies are determined. A machine learning model is generated using the specified geographic region of interest, the industry, the climate data, the carbon emissions data, and the supply chain dependencies. The machine learning model performs an impact analysis on a supply chain based on the climate data and the carbon emissions data. The machine learning model predicts a supply chain performance for the industry based on the impact analysis. A news headline is automatically generated describing the predicted supply chain performance. The news headline includes an underlying basis for the predicted supply chain performance.

Abstract: Methods, systems, and computer program products for dynamically enhancing supply chain strategies based on carbon emission targets are provided herein.

Abstract: In an approach to improve order management by performing sustainable order fulfillment optimization through computer analysis, embodiments receive an order from a user through the order management system for performing sustainable order fulfillment. Further, embodiments estimate carbon emissions and economic costs of fulfilling the order from a plurality of nodes, and output an optimal sustainable order fulfillment. Additionally, responsive to receiving confirmation to implement the output optimal sustainable order fulfillment, embodiments place the optimal sustainable order fulfillment for the received order.

Abstract: Methods and apparatuses for peritoneal catheter placement are disclosed herein. An example method includes using a puncture device to create an access hole to a patient's peritoneal cavity. The puncture device includes a lumen that enables a guidewire to be inserted into the peritoneal cavity. The puncture device is then replaced with a catheter, which is inserted along the guidewire. A surgical seal is next applied to connect the catheter to adjacent patient muscle tissue. After the guidewire is removed, a tunneling device creates a pathway through fat tissue to anchor the catheter. An end of the tunneling device is aligned with an external piercing device to pierce the patient's skin from the outside, creating a catheter exit hole. An end of the catheter is connected to the tunneling device and pulled through the exit hole, thereby enabling the catheter to be connected to peritoneal dialysis tubing for a treatment.

Abstract: A heating arrangement for an oven includes a heating assembly including a plurality of elongate heating members extending along respective longitudinal axes oriented in a first direction and mutually spaced apart in a second direction perpendicular to the first direction. The heating arrangement further includes a cover unit having a body extending along a plane oriented parallel with the plurality of elongate heating members and spaced apart therefrom in a third direction perpendicular to the first and second directions. A plurality of openings extend through the body with at least some of the openings being aligned with respective ones of the elongate heating members in the first and second directions.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Abstract: One embodiment provides a method, including: receiving a multi-variate time-series dataset comprising a plurality of time-dependent datasets; for each of the plurality of time-dependent datasets, segmenting each of the plurality of time-dependent datasets at a transition point; clustering segments of the plurality of time-dependent datasets into clusters having similar lengths of segments; for each cluster (i) selecting a representative segment length and (ii) identifying a feature subset in that cluster; identifying, across the feature subsets, subset transition points, wherein each of the subset transition points corresponds to a change in value that meets a predetermined threshold within its corresponding feature subset; and determining, by applying a threshold test to the subset transition points, a segment length to be used in segmenting the entire multi-variate time-series dataset.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: A supply chain optimization method, system, and computer program product include predicting a risk of a supply chain operation across a supply chain network caused by a term impact analysis of a global hazard, estimating a carbon footprint for the supply chain operation across the supply chain network, generating an alternative resilience plan as an alternative to an existing supply chain plan based on the predicted risk and the estimated carbon footprint.

Abstract: Methods, systems, and computer program products for anomaly and mode inference from time series data are provided herein. A computer-implemented method includes receiving time-series sensor data for each one of a group of devices; extracting a set of states for each device in the group from the time-series sensor data; constructing a state-transition graph for each of the devices, wherein each of the state-transition graphs comprises nodes corresponding to each state in the set and edges corresponding to a probability of transition between the extracted states over time; identifying, for each set, a given state as one of: a mode, a normal state and an anomalous state based on the state-transition graph; and detecting one or more anomalous devices in the group by computing similarities between different devices in the group, based at least in part on the determined state-transition graphs.

Abstract: Methods, systems, and computer program products for contextual anomaly detection across assets are provided herein. A method includes obtaining time-series data frames corresponding to assets; clustering the assets into one or more cohorts based on the time-series data frames, each cohort comprising assets having statistically similar time-series data frames; for each given asset within each cohort: applying a time-context window to the portion of the time-series data frames corresponding to the given asset to generate at least one transformed data frame, and determining an asset distribution for the given asset based on the at least one transformed data frame; determining one or more of that at least one of the assets within at least one of the cohorts is anomalous and that at least one of the cohorts is anomalous; and causing at least one remediation action to be performed based on the determining.

Abstract: One embodiment provides a method, including: receiving information for each of a plurality of products; for each of the plurality of products: predicting a demand for the product; calculating a sustainability score, wherein the sustainability score is based upon a material composition of the product; and computing a similarity score, wherein the computing comprises comparing pairs of products within the plurality of products; generating a plurality of assortment choices from the plurality of products, wherein each of the plurality of assortment choices has a corresponding assortment demand value, assortment sustainability score, and assortment similarity score, wherein the generating comprises utilizing a multi-objective formulation based upon the predicted demand, the calculated sustainability score, and the computed similarity score; and providing the plurality of assortment choices to a user for selection.

Abstract: One embodiment provides a method, including: receiving a multi-variate time-series dataset comprising a plurality of time-dependent datasets; for each of the plurality of time-dependent datasets, segmenting each of the plurality of time-dependent datasets at a transition point; clustering segments of the plurality of time-dependent datasets into clusters having similar lengths of segments; for each cluster (i) selecting a representative segment length and (ii) identifying a feature subset in that cluster; identifying, across the feature subsets, subset transition points, wherein each of the subset transition points corresponds to a change in value that meets a predetermined threshold within its corresponding feature subset; and determining, by applying a threshold test to the subset transition points, a segment length to be used in segmenting the entire multi-variate time-series dataset.

Abstract: Methods, systems, and computer program products for anomaly comparison across multiple assets and time-scales are provided herein. A computer-implemented method includes obtaining time-series data for each one of a plurality of assets; determining, for each of a plurality of time scales, one or more segments of the time-series data for each one of the plurality of assets; generating a graph for each one of the time-scales, the graph representing similarities between the one or more segments of the plurality of assets for the respective time-scale; computing an isolation metric for each of the generated graphs for at least one of the plurality of assets; aggregating the computed isolation metrics for the at least one asset; and determining whether the at least one asset is anomalous, based at least in part on a comparison of the aggregated isolation metric to a threshold.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for predictive maintenance of refrigeration cases. A computer collects a temperature time series for a refrigeration case and, based on the temperature time series, learns a refrigeration case signature for both non-frost and defrost cycles. The computer generates features based on the refrigeration case signature and compares the refrigeration case signature to real time, or observed, temperatures and features using a rule-based and/or machine learning framework. Based on determining that the real time data varies beyond a threshold from the refrigeration case signature, the computer identifies a failure symptom of the refrigeration case and diagnoses a root cause of the symptom or failure. In addition, the computer may activate an alarm and open a work order corresponding to the root cause of the symptom or failure.