Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

Abstract: Techniques are disclosed for correlating delivery receipt (DLR) messages with short message service (SMS) messages sent in an application-to-person (A2P) manner through a messaging network comprising multiple data centers. SMS and DLR messages are received and stored into a correlator comprising a local and a global storage area. It is then determined whether a received DLR message corresponds to a received SMS message within a local timeout period associated with the received SMS message. When corresponding DLR and SMS messages have been received they are correlated and the DLR message is sent to the sending SMS client. When corresponding DLR and SMS messages have not been received the location of a global storage area of a correlator in a data center where the DLR and SMS messages are intended to be stored is derived.

Abstract: A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.

Abstract: A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.

Abstract: Techniques are disclosed for passing short message service (SMS) messages between sending and receiving SMS service providers over a network comprising a default data center and one or more alternative data centers. The default data center may receive message segments of an SMS message from the sending SMS provider into a local storage area of a concatenator comprising a local and a global storage area. The message segments may include segmentation information indicative of a number of message segments associated with the SMS message, routing information, the sending SMS provider, and the receiving SMS provider. When all the message segments have been received within a local timeout period, the default data center concatenates the message segments into a single SMS message and sends it to the receiving SMS service provider.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

Abstract: Multi-ply fibrous structures that exhibit a Geometric Mean Modulus (GM Modulus) of less than 1700 g/cm at 15 g/cm as measured according to the Modulus Test Method described herein and a Geometric Mean Elongation (GM Elongation or GM Elong) of less than 11.7% measured according to the Elongation Test Method described herein are provided.

Abstract: Fibrous structures that exhibit a Tensile Ratio of less than 1.75 and/or less than 1.49 as measured according to the Tensile Strength Test Method described herein and a Geometric Mean Modulus (GM Modulus) of less than 1402.4 g/cm at 15 g/cm and/or a Machine Direction Modulus (MD Modulus) of less than 1253.4 g/cm at 15 g/cm and/or a Cross Machine Direction Modulus (CD Modulus) of less than 1569.2 g/cm at 15 g/cm, are provided.

Abstract: A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.

Abstract: A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.

Abstract: A medical scan comparison system is operable to receive a medical scan via a network and to generate similar scan data. The similar scan data includes a subset of medical scans from a medical scan database and is generated by performing an abnormality similarity function to determine that a set of abnormalities included in the subset of medical scans compare favorably to an abnormality identified in the medical scan. At least one cross-sectional image is selected from each medical scan of the subset of medical scans for display on a display device associated with a user of the medical scan comparison system in conjunction with the medical scan.

Abstract: A medical scan image analysis system is operable to receive a plurality of medical scans that represent a three-dimensional anatomical region and include a plurality of cross-sectional image slices. A plurality of three-dimensional subregions corresponding to each of the plurality of medical scans are generated by selecting a proper subset of the plurality of cross-sectional image slices from each medical scan, and by further selecting a two-dimensional subregion from each proper subset of cross-sectional image slices. A learning algorithm is performed on the plurality of three-dimensional subregions to generate a fully convolutional neural network. Inference data corresponding to a new medical scan received via the network is generated by performing an inference algorithm on the new medical scan by utilizing the fully convolutional neural network. An inferred abnormality is identified in the new medical scan based on the inference data.