Abstract: Systems and methods of the various embodiments may provide metal electrodes for electrochemical cells. In various embodiments, the electrodes may comprise iron. Various methods may enable achieving high surface area with low cost for production of metal electrodes, such as iron electrodes.

Abstract: A computer system for validating an account to be used in an electronic payment includes a processor coupled to a memory device. The computer system is programmed to receive an account validation web request from a bill payment originator. The account validation web request includes account details. The computer system is further programmed to process the account validation web request to parse the account details, validate the account details associated with the account validation web request using at least one validation rule, and transmit an account validation web response to the bill payment originator.

Abstract: According to an aspect, an electrochemical cell may include an electrolyte and an anode in the electrolyte, the anode including an iron-containing active material, at least one of the anode and the electrolyte including an additive reactive to inhibit hydrogen evolution in a charge state and in a resting state of the electrochemical cell, and the additive in a concentration greater than about 10 and less than about 10,000 atoms of additive per million atoms iron of the iron-containing active material.

Abstract: According to one aspect, an electrochemical cell may include a first electrode including a metal-containing active material, a second electrode, and an electrolyte in ionic communication between the first electrode and the second electrode, the electrolyte including a gel and an additive, the gel including a polymer network and a liquid medium, the polymer network carried in the liquid medium, the additive suspended in the gel and accumulable at the metal-containing active material of the first electrode.

Abstract: Systems and methods for the selective ablation of a target chromosome or fragment thereof in a cell have been established. Methods for the production of isogenic diploid-like cells, derived from aneuploid precursor cells have been developed. The isogenic diploid-like cells are genetically equivalent to the precursor cells, except for the loss of a target chromosome or fragment thereof. The systems provide isogenic “aneuploidy-loss” cancer cells. The cells can be used to, e.g., screen aneuploidy-selective therapies. In some forms, the methods target aneuploid cancers harboring extra copies of Chr1q, which encodes the UCK2 gene, by contacting the cells with agents activated by UCK2. Exemplary agents include RX-3117 and 3-deazauridine. Methods targeting aneuploid cancers harboring extra copies of Chr7p, which encodes the AHR gene, by contacting the cells with agents that bind to AHR, such as CGS-15943.

Abstract: Systems and methods of the various embodiments may provide metal electrodes for electrochemical cells. In various embodiments, the electrodes may comprise iron. Various methods may enable achieving high surface area with low cost for production of metal electrodes, such as iron electrodes.

Abstract: There is described a bioresorbable metal alloy which is particularly suitable for the formation of bioresorbable medical devices, for example stents. The metal alloy essentially comprises 3.2 to 4.8% by weight lithium, 0.5 to 2.0% by weight yttrium; and the balance being magnesium, in addition to any trace elements. The metal alloy can be drawn into a wire which can be shaped into a stent scaffold. The stent can be produced using one or more stent scaffolds together with one or more bioresorbable polymer connectors, for example formed from PLGA.

Abstract: According to one aspect, a feedstock for fabricating an iron electrode of an electrochemical cell may include iron-containing particles of a first material, sulfide-containing particles of a second material different from the first material, and a barrier material different from each of the first material and the second material, the barrier material at least partially physically separating the sulfide-containing particles from the iron particles, the at least partial physical separation of the iron-containing particles from the sulfide-containing particles maintainable by the barrier material at temperatures at which iron in the iron-containing particles bonds in the solid state.

Abstract: There is described a bioresorbable metal alloy which is particularly suitable for the formation of bioresorbable medical devices, for example stents. The metal alloy essentially comprises 3.2 to 4.8% by weight lithium, 0.5 to 2.0% by weight yttrium; and the balance being magnesium, in addition to any trace elements. The metal alloy can be drawn into a wire which can be shaped into a stent scaffold. The stent can be produced using one or more stent scaffolds together with one or more bioresorbable polymer connectors, for example formed from PLGA.

Abstract: According to one aspect, an additive for an iron negative electrode of an alkaline electrochemical cell may include a powder of discrete granules including agglomerated particles, the agglomerated particles including at least one metal sulfide.

Abstract: According to one aspect, an additive for an iron negative electrode of an alkaline electrochemical cell may include a powder of discrete granules including agglomerated particles, the agglomerated particles including at least one metal sulfide.

Abstract: Systems, methods, and devices of various aspects include using tin, antimony, and/or indium as an additive to an electrolyte and/or electrode in an electrochemical system, such as a battery, having an iron-based anode. In some aspects, the addition of tin, antimony, and/or indium may improve cycling of the iron-based anode. Systems, methods, and devices of various aspects include using high hydroxide concentration electrolyte in an electrochemical system, such as a battery. In some aspects, a high hydroxide concentration electrolyte may increase the stored amount of charge stored in the cell (i.e., the capacity of the battery material) and/or decrease the overpotential (i.e., increase the voltage) of the battery.

Abstract: Systems, methods, and devices of various aspects include using tin and/or antimony as an additive to an electrolyte and/or electrode in an electrochemical system, such as a battery, having an iron-based anode. In some aspects, the addition of tin and/or antimony may improve cycling of the iron-based anode. Systems, methods, and devices of various aspects include using high hydroxide concentration electrolyte in an electrochemical system, such as a battery. In some aspects, a high hydroxide concentration electrolyte may increase the stored amount of charge stored in the cell (i.e., the capacity of the battery material) and/or decrease the overpotential (i.e., increase the voltage) of the battery.

Abstract: Various embodiments may include a battery electrode, comprising: an iron electrode body comprising iron active material and a zinc sulfide additive, wherein the zinc sulfide additive comprises crystalline cubic zinc sulfide. Various embodiments may include a battery electrode, comprising: an iron electrode body comprising iron active material and a manganese sulfide additive, wherein the manganese sulfide additive comprises crystalline cubic manganese sulfide. Various embodiments may include an iron electrode battery, comprising: an iron electrode; and a sulfide reservoir separate from the iron electrode, the sulfide reservoir comprising crystalline cubic zinc sulfide. Various embodiments may include an iron electrode battery, comprising: an iron electrode and a sulfide reservoir separate from the iron electrode, the sulfide reservoir comprising crystalline cubic manganese sulfide.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using multi-variate mixture datastructures. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using neural networks and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using multi-variate mixture datastructures is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using multi-variate mixture datastructures and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. An asset return metrics calculation request datastructure is obtained. The number of sessions to utilize for calculating asset return metrics data is determined.