The present invention is a system for measuring at least one property of concrete that comprises: a housing having an inner cavity, said housing being embeddable into concrete upon deployment of said system in the field; a controller having a form that fits inside said cavity, said controller being retrievable from said cavity after said system is deployed; a sensor detachably connected to the controller such that the sensor becomes embedded into concrete upon said deployment of said system; wherein said system is capable of measuring a property of concrete upon deployment. The present invention also provides a novel method of measuring the strength of concrete comprising attaching the system as described above to a supporting element of a construction structure; immersing said housing and said sensor into concrete whereby the sensor is completely embedded inside the concrete; and measuring data related to the strength of concrete using the sensor and then transmitting the data wirelessly to a remote server.

The present disclosure relates to bendable and/or foldable articles and uses thereof and to a method of providing bendable and/or foldable articles. The articles are of translucent and brittle material, such as glass, glass ceramic, ceramic or crystals. The articles may be used as a display cover such as a protecting cover in displays in, for example, smartphones, tablet computers, or TV devices. The articles may also be used as a substrate for electronic components, such as OLEDs or LEDs.

A physical property estimation device for a resin composition, which estimates a physical property of a resin composition that is manufactured by using a base polymer, a flame retardant, a flame retardant auxiliary, and other materials. The physical property estimation device is provided with a regression model building processing unit that learns by machine learning a relationship between explanatory variable data and physical property data including physical property data as an estimation target, and builds a regression model that shows a correlation between the explanatory variable data and the physical property data, and a physical property estimation processing unit that estimates the physical property of an estimation target by using the regression model. The explanatory variable data includes compound amount data of the base polymer, the flame retardant, and the flame retardant auxiliary respectively, when a physical property to be estimated by the physical property estimation processing unit is either initial elongation or initial tensile strength of the estimation target.

The present disclosure relates to bendable and/or foldable articles and uses thereof and to a method of providing bendable and/or foldable articles. The articles are of translucent and brittle material, such as glass, glass ceramic, ceramic or crystals. The articles may be used as a display cover such as a protecting cover in displays in, for example, smartphones, tablet computers, or TV devices. The articles may also be used as a substrate for electronic components, such as OLEDs or LEDs.

Described herein are examples of material testing systems having new and improved processes that allow users to quickly and easily set several parameters defining a test method in response to a single user input, rather than having to manually set each and every parameter through a workflow. The parameters may further be optimized to comply with certain standards, save time, and/or reduce the potential for error. As the inputs required from the user during the new and improved processes are far fewer (and/or simpler) than if the user were to manually set each parameter through a workflow, there is a substantial time saving, and a substantial simplification of the endeavor.

A physical property estimation device for a resin composition, which estimates a physical property of a resin composition that is manufactured by using a base polymer, a flame retardant, a flame retardant auxiliary, and other materials. The physical property estimation device is provided with a regression model building processing unit that learns by machine learning a relationship between explanatory variable data and physical property data including physical property data as an estimation target, and builds a regression model that shows a correlation between the explanatory variable data and the physical property data, and a physical property estimation processing unit that estimates the physical property of an estimation target by using the regression model. The explanatory variable data includes compound amount data of the base polymer, the flame retardant, and the flame retardant auxiliary respectively, when a physical property to be estimated by the physical property estimation processing unit is either initial elongation or initial tensile strength of the estimation target.