A system provided herein may be configured to evaluate helmet performance. The system may include an impact assembly that includes a stationary post operably coupled to one or more stationary load cells and a plurality of modular headforms. Each modular headform may include a first side and a second side configured to lock together around the impact assembly and receive a helmet. The modular headform may determine a position of the helmet relative to the one or more stationary load cells. Furthermore, the one or more stationary load cells may be configured to measure impact force at a position where one of the plurality of the modular headforms are operably coupled to the impact assembly. Additionally, each of the plurality of modular headforms correspond to a position in relation to the impact assembly to measure the impact force to the one or more load cells at a predefined number of impact locations on the helmet to evaluate the performance of the helmet.

A method for determining a plasticity parameter of a hydrating, cement paste by mixing the components to obtain a cement slurry, pouring the cement slurry in a scaled oedometric cell, and performing an oedometric measurement operation within a predefined early-age time interval by applying a predefined axial stress path to the cement slurry over a predefined measurement duration and measuring an axial strain. A plasticity parameter of the hydrating cement paste is determined b a calibration processing operation comprising providing an initial value of a plasticity parameter of an elastoplastic model of hydrating cement paste, determining simulated axial strain values by solving the elastoplastic model of hydrating cement and comparing the simulated axial strain values with the axial strain measurements of the cement slurry.

Some embodiments relate to a system and method of estimating the viscoelasticity of a material. The system and method includes receiving a plurality of time-amplitude curves measured at a plurality of space points. The time-amplitude curves reflect time evolutions of a propagating mechanical wave. The system and method also include estimating the viscoelasticity of a material between any set of space points using the time-amplitude curves measured at those space points.

An atomic-force-microscope-based apparatus and method including hardware and software, configured to collect, in a dynamic fashion, and analyze data representing mechanical properties of soft materials on a nanoscale, to map viscoelastic properties of a soft-material sample. The use of the apparatus as an addition to the existing atomic-force microscope device.

An additive manufacturing system comprising at least one electronic nose (e-nose) is provided. The e-nose may comprise a housing and gas sensors. The housing may have an air channel. The active sensor portion of the sensors are positioned in the air channel. The housing may be mounted to an extruder head of an additive manufacturing device. The system may also comprise a processor. The processor may determine whether there is an abnormality in an additive manufacturing process based on one or more combinations of outputs from the gas sensors received during the additive manufacturing process input into a deployed machine learning model; and generate a report for the additive manufacturing process containing the determination.

A system for determining an age and/or quality of food or beverage based on one or more combinations of outputs from gas sensors input into a deployed machine learning model is provided. The system may comprise an electronic nose which may comprise a housing and the gas sensors. The housing may have an air channel. Each sensor has its active sensor portion in the air channel. A system for predicting one or more natural language descriptors associated with aromas of an item based on one or more outputs of the gas sensors and calculated one or more ratios input into a logistic regression model is also provided.

A system for predicting one or more analytes based on outputs from thin film gas sensors is provided. The system may comprise an electronic nose (e-nose). The e-nose may comprise the gas sensors and a first processor. The system may further comprise a second processor. The second processor may be configured to receive the output from each of the gas sensors, evaluate a prediction accuracy using an evaluation parameter of each of a plurality of models which are trained and tested and select a model from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models and use the same. The second processor may also receive, an output of each of the gas sensors caused by unknown one or more analytes; and predict, using the deployed model, the one or more analytes that causes the output.

An integrated multifunctional environmental characterization system (IMECS) is provided. The IMECS may comprises a memory, one or more interfaces and a processor. The processor may be configured to predict an environment condition adjacent to a thin film using one or more machine learned models from one or more measured properties of the thin film received via the one or more interfaces; and/or predict values for one or more properties of the thin film using the one or more machine learned models from an environmental condition received via one of the one or more interfaces; and display the predicted environment condition and/or the predicted one or more properties. The processor may also adjust the acquisition parameters used to acquire values of one or more properties of the thin film from received acquisition parameters via a user interface based on measured values for the same properties.

A loss tangent tan δ is the ratio between a storage modulus and a loss modulus calculated from a stress when vibrations of a predetermined frequency are applied to a rubber member. When evaluating a shock resistance performance of the rubber member using an elongation at break Eb, a tensile strength at break TSb, and a loss tangent tan δ of the rubber member, a speed of an impact applied under use conditions of the rubber member is matched with a maximum speed of vibrations. As a result, the shock resistance performance can be evaluated using a value of the loss tangent tan δ corresponding to an impact actually applied to the rubber member, and it is possible to improve the accuracy of the shock resistance performance evaluation.

There is a viscoelastic strain sensor that includes a sensing layer including a viscoelastic material, the viscoelastic material including a viscoelastic hydrogel and a conductive nanofiller. The viscoelastic material has a fractional resistance change that increases with an increase of an applied tensile strain, and the viscoelastic material has a fractional resistance change that decreases with an applied compressional strain.